BR112019009341A2 - automated initiation and library loading device - Google Patents

Abstract

Here, methods and apparatus are provided for the identification of pathogenic and non-pathogenic microorganisms in food and environment samples. The report addresses the existing challenges encountered in identifying foodborne pathogens, including pathogens of the genera Salmonella, Campylobacter, Listeria, and Escherichia in a timely and efficient manner. The report also provides methods for differentiating a transient pathogen versus a resident pathogen, correlating the presence of non-pathogenic microorganisms with pathogenic microorganisms, distinguishing live versus dead microorganisms by sequencing.

Description

AUTOMATED INITIATION AND LOADING DEVICE OF LIBRARY CROSS REFERENCE

[0001] This application claims the priority of provisional patent application serial number 62 / 611,846, filed on December 29, 2017, of provisional patent application serial number 62 / 646,135, filed on March 21, 2018; and provisional patent application serial number 62 / 730,288, filed on September 12, 2018; all of which are incorporated here as a reference in their entirety.

BACKGROUND

[0002] Food producers withdraw their products from the market when the products are mislabeled or when the food may present a health hazard to food consumers, because the food is contaminated or has caused an episode of food-borne illness. Although these producers rely on a number of existing monitoring programs for pathogens, natural toxins, pesticides and other contaminants, about 48 million cases of foodborne illness are still identified annually in the United States alone - the equivalent of 1 in 6 Americans ill. every year. And each year these diseases result in 128,000 hospitalizations and 3000 deaths. The threats are numerous and varied, with symptoms ranging from relatively mild discomfort to very serious and life-threatening illnesses. Although the very young, the elderly and those with weakened immune systems are at a higher risk of serious consequences from most foodborne illnesses, some of the microorganisms detected in food pose serious threats to all people.

SUMMARY

[0003] In some respects, the report provides a Method characterized by the fact that it comprises: (a) development of a test for one or more food processing facilities; (b) carrying out a sequencing reaction on a food sample or a sample from the environment of one or more food processing facilities; (c) transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample from said one or more food processing facilities to a server; and (d) scanning, by a computer, of at least a fraction of said data set transmitted to one or more polymorphic regions associated with a microorganism.

[0004] In some respects the report provides a method characterized by the fact that it comprises: (a) obtaining a plurality of nucleic acid sequences from a sample; (b) scanning, by a computer, of at least a fraction of said plurality of said nucleic acid sequences to a plurality of nucleic acid regions from one or more microorganisms selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria and a microorganism of the genus Escherichia, where said scanning characterizes said one or more microorganisms with a sensitivity greater than 99.5%.

[0005] In some respects the report provides a Method characterized by the fact that it comprises: (a) the sequencing of a plurality of nucleic acid sequences from a food sample or from a sample of the environment associated with said food sample by a period of time; and (b) performing a test on said food sample or in said environment associated with said food sample, if sequencing for said period of time identifies a limit level of nucleic acid sequences of a microorganism in said food sample .

[0006] In some respects the report provides a Method characterized by the fact that it comprises: (a) obtaining a first plurality of nucleic acid sequences from a first sample of a food processing facility; (b) creating a data file on a computer that associates one or more of said first plurality of nucleic acid sequences with said food processing facility; (c) obtaining a second plurality of nucleic acid sequences from a second sample of said food processing facility; and (d) scanning a plurality of sequences from the second plurality of nucleic acid sequences to one or more sequences associated with said food processing facility in (b).

[0007] In some respects, the report provides a method characterized by the fact that it comprises: (a) obtaining a first sample from a food processing facility; (b) sequencing said first sample from said food processing facility, thereby generating a first set of sequencing data from said food processing facility; (c) obtaining a second sample from said food processing facility; (d) sequencing said second sample from said food processing facility, thereby generating a second set of sequencing data from said food processing facility; and (e) comparing said second set of sequencing data with said first set of sequencing data; and (d) decontaminating said food processing facility if said comparison identifies a pathogenic microorganism in said food processing facility.

[0008] In some respects, the report provides a method characterized by the fact that it comprises: (a) obtaining a first plurality of nucleic acid sequences from a first sample from a food processing facility; (b) obtaining a second plurality of nucleic acid sequences from a second food sample from said food processing facility; and (c) performing sequence alignments on a computer between said first plurality of nucleic acid sequences and said second plurality of nucleic acid sequences, thereby determining a similarity between said first sample and said second sample from the food processing facility.

[0009] In some respects the report provides a Method characterized by the fact that it comprises: (a) the addition of a reagent to a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said sample of food, thus forming a plurality of modified nucleic acid molecules, whereby said reagent: (i) modifies a structure or interacts with a plurality of nucleic acid molecules derived from one or more dead microorganisms; and (ii) does not modify a structure of a nucleic acid molecule derived from one or more living microorganisms; thereby providing a plurality of modified nucleic acid molecules; and (b) sequencing by a sequencing reaction of said plurality of modified nucleic acid molecules, thereby distinguishing one or more living organisms from said food sample or said environment sample associated with said food sample.

[0010] In some respects the report provides a method comprising carrying out a pore sequencing reaction on a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said food sample, so said pore sequencing reaction distinguishes one or more nucleic acid molecules derived from a dead microorganism from one or more nucleic acid molecules derived from a living microorganism based on a methylation pattern or another epigenetic pattern from said or more molecules nucleic acid derived from said dead microorganism.

[0011] In some respects the report provides a method characterized by the fact that it comprises: (a) obtaining the plurality of nucleic acid sequences from a food sample or a sample from the environment of a food processing facility; (b) carrying out a first test on said plurality of nucleic acid sequences of said food sample, whereby said test provides for the presence or foresees the absence of a microorganism in said food sample; and (c) determining, based on said predicted presence or said predicted absence of said microorganism, of (b) a second test is performed, so that a sensitivity of said second test is selected in order to determine a genus, a species , a serotype, a sub-serotype, or a strain of said microorganism.

[0012] In some respects, the report provides a Method characterized by the fact that it comprises: (a) the detection of the presence or absence of a non-pathogenic microorganism in a sample; (b) prediction, by a computer system, of the presence or absence of a pathogenic microorganism in said sample based on said presence or said absence of said non-pathogenic microorganism.

[0013] In some respects, the report provides a Method characterized by the fact that it comprises: (a) the detection of the presence or absence of a microorganism in a sample or in an installation associated with said sample; and (b) prediction, by a computer system, of a risk that presents the said installation based on said presence or said absence of said microorganism.

[0014] In some respects, the report provides a Method characterized by the fact that it comprises: (a) the addition of a first bar code to a first plurality of nucleic acid sequences in a sample, thus providing a first plurality bar code nucleic acid sequences; and (b) carrying out a first sequencing reaction on said first plurality of barcode nucleic acid sequences, wherein said sequencing reaction is performed on a sequencing apparatus comprising a flow cell; (c) adding a second bar code to a second plurality of nucleic acid sequences from a second sample, thereby providing a second plurality of nucleic acid sequences with bar codes; and (d) carrying out a second sequencing reaction on said second plurality of barcode nucleic acid sequences, where said second sequencing reaction is performed on said sequencing apparatus comprising said flow cell, thereby reusing said flow cell.

[0015] In some respects, the report provides a nucleic acid sequencing apparatus comprising: (a) a nucleic acid library preparation compartment comprising two or more cameras configured to prepare a plurality of nucleic acids from a sample for a sequencing reaction, where said compartment is operationally connected to a nucleic acid sequencing camera; (b) a nucleic acid sequencing chamber, wherein said nucleic acid sequencing chamber comprises: (i) one or more flow cells comprising a plurality of pores or sequencing cartridges configured for the passage of a nucleic acid strand , where two or more of the one or more flow cells are juxtaposed to each other; and (c) an automated platform, where said automated platform is programmed to robotically move a sample from said nucleic acid library preparation compartment to said nucleic acid sequencing chamber.

[0016] In some respects, the report provides a Method characterized by the fact that it comprises: (a) adding a first molecular index to a first plurality of nucleic acid sequences in a sample, thus providing a first plurality of sequences indexed nucleic acids; and (b) adding a second molecular index to said first plurality of nucleic acid sequences of said first sample, thereby providing a second plurality of indexed nucleic acid sequences; and (c) adding a third molecular index to said first plurality of nucleic acid sequences of said first sample, thereby providing a third plurality of indexed nucleic acid sequences; (d) carrying out a sequencing reaction on said third plurality of nucleic acid sequences; and (e) demultiplexing, by a computer system, said third plurality of nucleic acid sequences comprising said first molecular index, said second molecular index, and said third molecular index.

[0017] In some respects, this report writes a device capable of detecting and distinguishing microorganisms, including foodborne pathogens. Foodborne pathogens can include any of numerous organisms that spread through food consumption, including enterotoxic E. Coli and Salmonella bacteria. These microorganisms can often survive a wide variety of environments, including food preparation surfaces and food processing equipment, as well as in the food itself. Tracing the origins and movements of foodborne pathogen outbreaks often requires the detection of one or more microorganisms from a variety of sample types, including mops, food samples, and stool samples. Bearing in mind that outbreaks may be linked to a particular strain of a microorganism, for example, E. coli O157: H7, and since its detection is critical to stop its spread, detection must be fast and accurate.

[0018] The detection system of a foodborne pathogen can be designed for numerous purposes, including implantable systems that can be moved to any environment, for example, a farm field or fixed devices for laboratory configurations where the collected samples are taken to the device. In most cases, it is highly desirable to have a device that is highly automated in order to reduce the steps in which a user must be involved in order to increase ease of use and reduce the risk of contamination or other sources of process failure .

INCORPORATION BY REFERENCE

[0019] All publications, patents and patent applications mentioned in this report are hereby incorporated by reference to the same extent that each publication, patent or patent application was specifically and individually indicated as being incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The new features of the invention are presented with particularity in the attached claims. A better understanding of the characteristics and advantages of the present invention will be obtained by reference to the detailed description below which presents illustrative realizations, in which the principles of the invention are used, and in the accompanying drawings in which:

[0021] Figure 1 (Fig. 1): illustrates the implementation of a sequencing assay 101 for one or more food processing facilities, food testing laboratories, or any other diagnostic laboratory 102 to perform a reaction sequencing a food sample or a sample of the environment of said food processing facilities such as, for example, soil, water, air, animal product (s), food, manure, crop product or any sample associated with a manufacturing plant.

[0022] Figure 2 (Fig. 2): illustrates an electronic communication transmission comprising a data set associated with a sequencing reaction from one or more food processing facilities to a server.

[0023] Figure 3 (Fig. 3): is a graph illustrating that a redundancy in genetic markers reduces a false negative rate of a report method.

[0024] Figure 4 (Fig. 4): illustrates a process for assessing the predicted risk based on a detection of a non-pathogenic microorganism.

[0025] Figure 5 (Fig. 5): is a thermal map illustrating the detection of pathogen predictive through machine learning.

[0026] Figure 6 (Fig. 6): illustrates a process for predicting the shelf life of a food based on the detection of a microorganism.

[0027] Figure 7 (Fig. 7): it is a diagram illustrating the tunable resolution of several tests.

[0028] Figure 8 (Fig. 8): is a schematic illustration of various serotypes of various microorganisms that can be detected by analyzing a plurality of nucleic acid sequences as described here and further validated with a serotyping assay.

[0029] Figure 9 (Fig. 9): is a schematic illustration of a process for distinguishing a living microorganism from a food or a sample from the environment.

[0030] Figure 10 (Fig. 10): illustrates a process for the reuse of flow cells with different indexes.

[0031] Figure 11 (Fig. 11): illustrates an automated report sequencing device.

[0032] Figure 12 (Fig. 12): illustrates a sequencing process without points of human interference after enrichment.

[0033] Figure 13 (Fig. 13): illustrates the PMAxx-induced removal of floating DNA.

[0034] Figure 14 (Fig. 14): illustrates an initiation port in a flow cell.

[0035] Figure 15 (Fig. 15): illustrates the production of a load library in a flow cell.

[0036] Figure 16 (Fig. 16): illustrates the simultaneous targeting for multiple pathogens.

[0037] Figure 17 (Fig. 17): illustrates the in silico prediction of the sensitivity / specificity of the initiator.

[0038] Figure 18 (Fig. 18): illustrates the reuse of MinION / GridION flow cells.

[0039] Figure 19 (Fig. 19): illustrates the number of readings per sample when reusing MinION / GridION flow cells.

[0040] Figure 20 (Fig. 20): illustrates the performance of the automated handling system described in samples containing 10 different Salmonella serotypes (Enteritidis, Thyphimurium, I 4_ [5] _12: i :, Newport, Javiana, Infantis, Montevideo, Heidelberg, Munich).

[0041] Figure 21 (Fig. 21): illustrates a principal component analysis for chicken wing data sets.

[0042] Figure 22 (Fig. 22): illustrates a principal component analysis for ground chicken data sets.

[0043] Figure 23 (Fig. 23): illustrates periodic and non-periodic barcode drawings.

[0044] Figure 24 (Fig. 24): illustrates a major component analysis of Listeria sequences identifying clusters of closely related bacteria possibly originating from the same source.

[0045] Figure 25 (Fig. 25): illustrates a typical automatic nanopore flow cell suitable for use with the methods according to this report.

[0046] Figure 26 (Fig. 26): illustrates a typical automatic nanopore flow cell with an alternative sample inlet port as described here.

[0047] Figure 27 (Fig. 27): illustrates the method of microbial concentration of phase separation described in Example 23.

DETAILED DESCRIPTION

[0048] Food security is a complex problem that has an impact on multiple segments of society. Usually, a food is considered to be adulterated if it contains: (1) a poisonous or otherwise dangerous substance that is not an inherent natural constituent of the food itself, in an amount that signifies a reasonable possibility of damage to health, or (2) a substance that is an inherent natural constituent of the food itself; it is not the result of environmental, agricultural, industrial, or other contamination; and is present in an amount that normally makes the food harmful to health. The first includes, for example, a bacterium, fungus, parasite or pathogenic virus, if the amount present in the food can be harmful to health. An example of the second is the tetrodotoxin which occurs naturally in some organs of some types of puffer fish and which will normally make the fish harmful to health. In both cases, foods adulterated with these agents are generally considered unsuitable for consumption.

[0049] Many different disease-causing microorganisms can contaminate food, and there are many different food-borne infections. Although our scientific understanding of pathogenic microorganisms and their toxins is continually advancing, some of the most common microorganisms associated with foodborne diseases include microorganisms from the genera Salmonella, Campylobacter, Listeria, and Escherichia.

[0050] Salmonella, for example, is widely dispersed in nature. It can colonize the intestinal tracts of vertebrates, including wild livestock, domestic and human pets, and can also live in environments such as pond sediments. It is disseminated through the fecal-oral route and through contact with contaminated water. (Certain protozoa can act as a reservoir for the organism). It can, for example, contaminate poultry, red meat, irrigation water (thus contaminating products in the field), soil and insects, factory equipment, kitchen surfaces and utensils.

[0051] Campylobacter jejuni is estimated to be the third leading bacterial cause of foodborne diseases in the United States. The symptoms that this bacteria causes usually lasts for 2 to 10 days and, although diarrhea (sometimes bloody), vomiting and cramps are unpleasant, they usually pass by themselves in people who are otherwise healthy. Raw poultry, unpasteurized (“raw”) milk and cheeses made from it, contaminated water (for example, non-chlorinated water, such as in rivers and lakes) are the main sources, however C. jejuni also occurs in other types meat and was found in seafood and vegetables.

[0052] Although the number of people infected with food-borne Listeria is comparatively small, this bacterium is a major cause of death from food-borne diseases. It can cause two forms of illness. One can range from mild to severe symptoms of nausea, vomiting, pain, fever and sometimes diarrhea and usually go away on their own. The other, more deadly form, occurs when the infection spreads through the bloodstream to the nervous system (including the brain), resulting in meningitis and other potentially fatal problems.

[0053] The microorganisms of the genus Escherichia are also diverse in nature. For example, at least four pathogenic Escherichia coli groups have been identified: a) Enterotoxigenic Escherichia coli (ETEC), b) Enteropathogenic Escherichia coli (EPEC), c) Enterohemorrhagic Escherichia coli (EHEC), and Enteroinvasive Escherichia coli (EIEC). Although ETEC is generally associated with traveller's diarrhea, some members of the EHEC group, such as E. coli 0157: H7, can cause bloody diarrhea, blood clotting problems, kidney failure and death. Thus, it is important to be able to not only identify individual microorganisms, but also to distinguish them.

[0054] Here are provided methods and devices for the identification of pathogenic and non-pathogenic microorganisms in food and samples from the environment. The report addresses the existing challenges encountered in identifying foodborne pathogens, including pathogens of the genera Salmonella, Campylobacter, Listeria and Escherichia in a timely and efficient manner. The report also provides methods for differentiating a transient versus a resident pathogen, correlating the presence of non-pathogenic microorganisms with pathogens, and distinguishing live versus dead microorganisms by sequencing, among others.

[0055] As used here, the term "food processing facility" includes facilities that manufacture, process, pack or manipulate food anywhere in the globe. A food processing facility can, for example, determine the location and source of a foodborne disease onset or a potential biological terrorism incident.

[0056] As used here, the term "food" includes any nutritive substance that people or animals eat or drink, or that plants absorb, in order to maintain life and growth. Non-limiting examples of food include red meat, poultry, fruits, vegetables, fish, pork, seafood, dairy products, eggs, roe, raw agricultural products for use as food or food components, canned foods , frozen foods, bakery products, appetizers, sweets (including chewing gum), dietary supplements and dietary ingredients, formulas for children, beverages (including alcoholic drinks and bottled water), animal feed and pet food and live food. The term “environmental sample” as used here, includes all substances in contact with food or items from a food processing facility. The term environmental sample includes a surface mop of a food contact substance, a surface rinse of a food contact substance, a food storage container, food handling equipment, a piece of clothing from a individual in contact with a food processing facility or another suitable sample from a food processing facility. The term “sample”, as used here, refers generically to any sample that may be informative of an environment or a food, such as a sample that comprises soil, water, water quality, air, animal production, feed, manure, crop production, manufacturing plants, environmental samples or food samples directly. The term "sample" can also refer to another non-food sample, such as samples derived from an individual, such as blood, plasma, urine, tissue, faces, marrow, saliva or cerebrospinal fluid. Such samples can be derived from a hospital or clinic.

[0057] As used here, the term "individual" can refer to a human or another animal. An animal can be a mouse, a rat, a guinea pig, a dog, a cat, a horse, a rabbit and several other animals. An individual can be any age, for example, an individual can be a baby, a child, a preteen, a teenager, an adult, or an elderly person.

[0058] As used here, the term "disease" generically refers to conditions associated with the presence of a microorganism in a food, for example, outbreaks or incidents of foodborne illness.

[0059] The term "nucleic acid" or "polynucleotide" as used here, refers to a polymeric form of nucleotides of any length, ribonucleotides or deoxyribonucleotides. Polynucleotides include sequences of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or copies of ribonucleic acid (cDNA) DNA.

[0060] The term "polyribonucleotide", as used herein, refers generically to polynucleotide polymers that comprise ribonucleic acids. The term also refers to polynucleotide polymers that comprise chemically modified ribonucleotides. A polyribonucleotide can be formed from D-ribose sugars, which can be found in nature, and L-ribose sugars, which are not found in nature.

[0061] The term "polypeptides" as used here, generically refers to polymeric chains of monomers of amino acid residues that are joined by means of amide bonds (peptide bonds). The amino acids can be the L optical isomer or the D optical isomer.

[0062] The term "barcode" as used here, generically refers to a label, or identifier, that contains or is capable of containing information about one or more nucleic acid sequences in a food or food sample. a sample of the environment associated with said food sample. A bar code can be part of a sequence of nucleic acids. A bar code can be independent of a nucleic acid sequence. A barcode can be a label attached to a nucleic acid molecule. A barcode can have a variety of different formats. For example, bar codes can include: polynucleotide bar codes; random nucleic acid and / or amino acid sequences; and nucleic acid and / or synthetic amino acid sequences. A barcode can be added, for example, to a sample of a fragment of a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) before, during and / or after sequencing the sample. Bar codes can allow the identification and / or quantification of individual sequencing readings. Examples of such bar codes and their uses, for use with the methods, apparatus and systems of this report, are provided in US patent publication No. 2016/0239732, which is incorporated herein entirely by reference. In some cases, as described here, a “molecular index” can be a bar code itself or it can be a building block, that is, a component or part of a larger bar code.

[0063] The term "sequencing", as used here, refers generically to methods and technologies for determining the sequence of nucleotide bases in one or more polymers of nucleic acid, that is, polynucleotides. Sequencing can be performed by several systems currently available, such as, without limitation, an Illumina®, Pacific sequencing system

Biosciences (PacBio®), Oxford Nanopore®, Genia (Roche) or Life Technologies (Ion Torrent®). Alternatively or in addition, sequencing can be performed using nucleic acid amplification, polymerase chain reaction (PCR) (for example, digital PCR, quantitative PCR or real-time PCR), or isothermal amplification. Such systems can provide a plurality of raw data corresponding to the genetic information associated with a food sample or a sample from the environment. In some instances, such systems provide nucleic acid sequences (also "readings" or "sequencing readings" here). The term also refers to epigenetics which is the study of inheritable changes in genetic function that do not involve changes in the DNA sequence. A reading may include a strand of nucleic acid bases corresponding to a sequence of a nucleic acid molecule that has been sequenced. Sequence Analysis Required by a Customer

[0064] Many outbreaks of food poisoning have been associated with pathogenic microorganisms including pathogens of the genera Salmonella, Campylobacter, Listeria, and Escherichia. Examples of foods that have been associated with such outbreaks include milk, cheese, vegetables, meat (mainly meat from livestock and poultry), fish, seafood, and many others. Potential sources of contamination for various pathogens include raw materials, food workers, intake air, water and food processing environments. In addition, contamination after processing on food contact surfaces in a food processing facility poses a greater threat to product contamination.

[0065] There are many challenges in ensuring the security of our food supply. Some of these challenges include changes in a food processing environment that lead to food contamination, such as the introduction of a new batch of contaminated raw products. Other challenges include changes in food production and supply, which include the import and export of food from different jurisdictions, which may have different standards for assessing the risk associated with a food. In addition, new and emerging bacterial strains, toxins and antibiotic resistance may not be detected by traditional serotyping or PCR detection methods.

[0066] In some respects, the report provides a method for identifying a microorganism associated with a food or a food processing facility. In some respects, the method comprises the implementation of a test for one or more food processing facilities; carrying out a sequencing reaction of a food sample or a sample of the environment of said one or more food processing facilities; transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample of said one or more food processing facilities to a server; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism. In some embodiments, the method comprises the implementation of a test for one or more food processing facilities; receiving by means of an electronic communication server comprising a set of data associated with a sequencing reaction, where the sequencing reaction characterizes a food sample or a sample of the environment from said one or more food processing facilities; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism.

[0067] In some respects, the report provides a method for identifying a microorganism associated with a food or a food processing facility. In some respects, the method comprises receiving a test at one or more food processing facilities; carrying out a sequencing reaction of a food sample or a sample of the environment of said one or more food processing facilities;

transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample of said one or more food processing facilities to a server; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism. In some embodiments, the method comprises receiving a test at one or more food processing facilities; reception by means of an electronic communication comprising a set of data associated with a sequencing reaction, where the sequencing reaction characterizes a food sample or a sample of the environment of said one or more food processing facilities; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism.

[0068] In some respects, the report provides a method for identifying a microorganism associated with a food or a food processing facility. In some aspects, the method comprises the implementation of a test for one or more food processing facilities; receiving an electronic communication comprising a set of data associated with a sequencing reaction of a food sample or a sample of the environment from said one or more food processing facilities for a server; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism. In some embodiments, the method comprises the implementation of a test for one or more food processing facilities; reception by means of an electronic communication server comprising a set of data associated with a sequencing reaction, where the sequencing reaction characterizes a food sample or a sample of the environment of said one or more food processing facilities; and scanning, by a computer, at least a fraction of said data set transmitted to one or more genes associated with a microorganism.

[0069] In some cases, scanning scans less than 1%, less than 0.1%, less than 0.001% of said data set transmitted to one or more genes associated with said microorganism. Said scanning can be performed to identify a variety of polymorphic genetic regions (comprising SNP's, RFLP's, STRs, VNTR's, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, indices, and elements insertion) associated with a wide variety of microorganisms. The variety of polymorphic regions to be searched can be determined by creating a large database of dozens, hundreds and thousands of samples of food and environments. For example, a database of such polymorphic regions can be constructed by performing sequencing reactions on at least 5,000, at least 10,000, at least 15,000, at least 20,000, at least 25,000, at least 30,000, at least 35,000, at least least 40,000, at least 45,000, at least 50,000 different ambient food samples. The obtained sequences can be used to compile information in a database that includes: a) the composition of each sample; and b) the presence or absence of a variety of pathogenic and non-pathogenic organisms associated with each sample. In addition to the fact that it contains information about several types of genera and species, such databases comprise data from polymorphic genetic regions of a variety of strains that are variants of a single species. For example, a plurality of sequences in the database can correspond to one or more serovars, morphovars, biovars, or other strain specific information.

[0070] A variety of sequencing techniques, such as a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing, can be used to create a collection of polymorphic regions. In some cases, said sequencing reaction is a pore sequencing reaction and said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0071] In some cases, said microorganism may be previously selected by a customer. A customer can be an individual or an entity, such as one or more food processing facilities. For example, a customer may be a food packaging facility; a food distribution center; a food storage center; a facility for handling meat, poultry, eggs, or another edible product; a farm; a retail food outlet; a fish tank; or another type of facility that also manufactures, processes, packs or handles food for any period of time.

[0072] A customer can previously select a microorganism of interest to be identified with any of the methods described here. For example, raw or undercooked beef and beef products are vehicles often implicated in E. coli O157: H7 outbreaks. Products, including packaged lettuce, spinach, and alfalfa sprouts, are also increasingly being implicated in outbreaks of E. coli O157: H7. A food processing facility that produces raw meats or other products associated with E. coli O157: H7 may be a customer that pre-selects E. coli as a microorganism for analysis. A customer can pre-select one or more types of microorganisms for analysis. A microorganism can be one or more of the types of bacteria, fungi, parasites, protozoa and viruses.

[0073] Non-limiting examples of bacteria that can be previously selected by a customer and detected using the reporting methods include: bacteria in the genus Escherichia, including enterotoxigenic Escherichia coli (ETEC), enteropathogenic Escherichia coli (EPEC), enterohemorrhagic Escherichia coli (EHEC ) and Escherichia coli enteroinvasiva (EIEC); bacteria of the Salmonella genus; bacteria of the genus Campylobacter; bacteria of the Listeria genus; bacteria of the genus Yersinia; bacteria of the genus Shigella; bacteria of the genus Vibrio; Coxiella bacteria; bacteria of the genus Mycobacterium;

bacteria of the genus Brucella; bacteria of the genus Vibrio; bacteria of the genus Cronobacter; bacteria of the genus Aeromonas; bacteria of the genus Plesiomonas; bacteria of the genus Clostridium; bacteria of the Staphylococcus genus; bacteria of the genus Bacillus; bacteria of the genus Streptococcus; bacteria of the genus Clostridium; and bacteria of the genus Enterococcus.

[0074] A microorganism can be a virus. Non-limiting examples of viruses that can be previously selected by a customer and detected using the reporting methods include: norovirus, Hepatitis A virus, Hepatitis E virus, rotavirus.

[0075] Conducting a sequencing reaction on a food sample or a sample from the environment of said one or more food processing facilities often generates a plurality of nucleic acid sequences that contain redundant information or information associated with genes that they are not of a microorganism. In some respects, the methods described allow efficient data analysis by facilitating targeted analysis of a smaller set of data. The data generated can be in the range of Kb, Mb, Gb, Tb or more per sample analyzed. In some respects, said scanning scans less than 1/10, less than 1/20, less than 1/30, less than 1/40, less than 1/50, less than 1/60, less than 1/70, less than 1/80, less than 1/90, less than 1/100, less than 1/200, less than 1/300, less than 1/400, less than 1/500, less than 1/600, less than 1/700, less than 1/800, less than 1/900, less than 1/1000, less than 1/10000, or less than 1/100000 of a data set, such as a data set transmitted to one or more genes associated with a microorganism. In some respects, said scanning scans at least a fraction of said data set transmitted for one or more genes associated with two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more microorganisms or another suitable number. In some cases, said scanning comprises scanning said data set transmitted to one or more polymorphic genetic regions. In some cases, said one or more polymorphic regions comprises one or more single nucleotide polymorphisms (SNP's), one or more restriction fragment length polymorphisms (RFLP's), one or more short tandem repeats (STRs), one or more more variable numbers of tandem repetitions (VNTR's), one or more hypervariable regions, one or more minisatellites, one or more dinucleotide repetitions, one or more trinucleotide repetitions, one or more tetranucleotide repetitions, one or more simple sequence repetitions , one or more indices, or one or more insertion elements. In some cases, said one or more polymorphic regions comprise one or more single nucleotide polymorphisms (SNPs). A set of data associated with a sequencing reaction from a food sample or a sample from the environment can be transmitted to a server and scanned by a computer.

[0076] In some cases, a method can detect a microorganism selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria, and a microorganism of the genus Escherichia. The detected microorganisms can be of any serotype and a computer scan of one or more genes associated with a microorganism can detect a microorganism regardless of its serotype.

[0077] In some cases, a sequencing reaction of a food sample, a sample from the environment, or another sample is a pore sequencing reaction, such as an Oxford Nanopore® sequencing reaction. In some cases, at least one bar code is added to one or more nucleic acid polymers derived from a food sample, a sample from the environment, or another sample before carrying out said sequencing reaction. In some cases, a plurality of mutually exclusive bar codes is added to a plurality of food processing facilities, thereby creating an identifiable bar code that can be associated with each food processing facility. For example, a barcode sequencing reading comprising sequences from a pathogenic microorganism can be associated with a food or processing facility. In some respects, a method described here further comprises the creation, on a computer, of a data file that associates said at least one bar code with a source of said food sample, said environment sample, or a another sample.

[0078] In some respects, the methods described comprise computer systems or devices that use computer systems that are programmed to implement the reporting methods. Fig. 1 illustrates the implementation of a sequencing assay 101 for one or more food processing facilities 102, a food testing laboratory, or any other laboratory for diagnosing and performing a sequencing reaction on a food or food sample. a sample of said environment one or more food processing facilities 102. The food processing facility, food testing laboratory, or any other diagnostic laboratory may contain one or more computer systems that can be used to transmit data results of sequencing readings to a server, in the local cloud environment or deployed remotely. Fig. 2 illustrates an electronic communication transmission comprising a data set associated with a sequencing reaction from one or more food processing facilities, food testing laboratories, or any other diagnostic laboratories to a server.

[0079] The raw sequence data collected from the sequencing reaction includes a large data set that includes all the individual sequences, as well as the quality in each base. From this large data set, the Clear Labs bioinformatics pipeline extracts a final report in a minor order of magnitude. The final report (for example, electronic communication) is essentially limited to the presence or absence of an organism of interest, for example, pathogens, and an additional classification of the organism in terms of serotypes, strains or other subclassifications. The collected data not used in the report comprises the following:

(a) Quality of reading: The raw sequences include information regarding the quality of the sequences by base. Quality scores can be used in a Bayesian model where classifications are statistically sensitive to these quality scores. In addition, quality scores can reveal more about possible relationships that sample content has with the accuracy of the sequencing platform. (b) Sequence time: Raw sequences also include information about the time when the sequence was read by the sequencer. The number of sequences from the same source as a function of time can reveal much more information than we currently have. In addition, this time data can be useful in generating reports for all or some of the fastest samples that are currently done. (c) Trimmed parts of the sequences: During demultiplexing the initial and terminal parts of the sequences are trimmed. These parts include adapters, indexing barcodes and initiators. The main data extracted from the trimmed parts, identify which sample the sequence belonged to. This decision, however, is influenced by sequencing errors and special properties of the sequences involved. Information about the accuracy of this decision, and other factors, is lost with trimming. In addition, the quality of these parts can be used as an indicator for the quality of the entire sequence. (d) Agglomeration: An important step in the pipeline involves agglomerating sequences that are close enough together and representing all sequences within a cluster by a consensus sequence. This significantly reduces the data and facilitates the classification of these sequences. However, these differences, even if minimal, carry information that is lost with agglomeration. Agglomeration with stricter criteria, or absence of agglomeration, can lead to higher resolution and perhaps finer classification.

[0080] A computer system 201 can be programmed or otherwise configured to process and transmit a data set from a food processing facility, food testing labs, or any other diagnostic labs. Computer system 201 includes a central processing unit (CPU, also "processor" and "computer processor" here) 204, which can be a single-core or multi-core processor, or a plurality of processors for parallel processing . Computer system 201 also includes memory or memory location 205 (for example, random access memory, read-only memory, flash memory), electronic storage unit 206 (for example, hard disk), communication interface 202 (for example, network adapter) for communication with one or more other systems, such as, for example, transmitting a set of data associated with said sequencing readings, and peripheral devices 204, such as cache, other memory, data storage and / or electronic presentation adapters. Memory 205, storage unit 206, interface 202 and peripheral devices 203 are in communication with CPU 204 by means of a communication bus (solid lines), such as a motherboard. Storage unit 206 may be a data storage unit (or data repository) for data storage. For example, in some cases, the data storage unit 206 may store a plurality of sequencing readings and provide a library of sequences associated with one or more strains of one or more microorganisms associated with a food processing facility, food laboratories. food testing, or any other diagnostic labs.

[0081] Computer system 201 can be operationally coupled to a computer network ("network") 207 with the aid of communication interface 202. Network 207 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is communicating with the Internet. The 207 network, in some cases, is a telecommunication network and / or data network. Network 207 can include one or more computer servers, which can enable distributed computing, such as cloud computing. Network 207, in some cases, with the aid of computer system 201, can implement a “peer-to-peer” network, which can enable devices coupled to computer system 201 in order to behave like a client or a server . High Sensitivity Detection of Microorganisms

[0082] Some families of microorganisms comprise both harmless and pathogenic individuals. The Escherichia family of pathogens, for example, comprises lethal strains and harmless strains of E. coli. Thus, it is not only relevant to be able to identify a pathogen in a sample, but it is also relevant to be able to characterize it with high sensitivity. In some respects, the report provides a method comprising obtaining a plurality of nucleic acid sequences from a food sample, from an environment associated with said food sample or from another sample, such as non-derived food samples. clinical sources, including blood, plasma, urine, tissue, feces, marrow, saliva, or cerebrospinal fluid samples; scanning, by a computer, of at least a fraction of said plurality of said nucleic acid sequences to a plurality of nucleic acid regions of one or more microorganisms selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter , a microorganism of the genus Listeria and a microorganism of the genus Escherichia, where said scanning characterizes said one or more microorganisms with sensitivity above 98%, sensitivity above 98.5%, sensitivity above 99%, sensitivity above 99, 5% or sensitivity above 99.9%. In some aspects, said scanning characterizes said one or more microorganisms with specificity above 98%, specificity above 98.5%, specificity above 99%, specificity above 99.5% or specificity above 99.9% . Sensitivity can be a measure of a microorganism that is correctly identified (for example, the percentage of a microorganism that can be correctly identified based on the analysis of the sequencing reading). Specificity (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (for example, the percentage of the food sample or samples from the environment that is correctly identified as not showing the microorganism). In some cases, said method can distinguish a genetic variant or subtype from a microorganism (for example, one or more bacterial strains).

[0083] In some cases said plurality of nucleic acid sequences comprise complementary DNA (cDNA) sequences, ribonucleic acid (RNA) sequences, genomic deoxyribonucleic acid (gDNA) sequences or a mixture of cDNA, RNA and gDNA sequences . In some cases, the high sensitivity of the described method, the high specificity of the described method, or both, can be obtained by scanning said plurality of said nucleic acid sequences for one or more polymorphic genetic regions associated with said microorganisms. In some cases, said one or more polymorphic regions are selected from the group consisting of one or more single nucleotide polymorphisms (SNP's), one or more restriction fragment length polymorphisms (RFLP's), one or more repetitions of short tandem ( STRs), one or more variable numbers of tandem repetitions (VNTR's), one or more hypervariable regions, one or more minisatellites, one or more dinucleotide repetitions, one or more trinucleotide repetitions, one or more tetranucleotide repetitions, one or more more repetitions of simple sequence, one or more indelible, or one or more insertion elements. In some cases, said scanning compares a scanned polymorphism with a sequence library comprising sequences of tens, hundreds or thousands of unique strains of a microorganism. The greatest sensitivity is achieved by comparing the sequence information of the target region that can discriminate different microorganisms through SNPs, indel lenses or other specific non-universal target markers that are present only in the genome of the target microorganisms.

[0084] In some respects, an analysis of redundancy in genetic markers increases the specificity and sensitivity of a method described here.

Fig. 3 is a graph illustrating that redundancy in genetic markers reduces a false negative rate for a reporting method and increases its sensitivity compared to PCR-based methods. As shown in Fig. 3, three commercially available q / PCR-based pathogen detection kits have been shown not to be able to detect known Salmonella or Listeria genomes. In 301, the percentages of Salmonella detection by existing commercial kits are illustrated. In 302 the percentages of detection of Listeria by the existing commercial kits are illustrated.

[0085] Scanning a plurality of nucleic acid regions within said plurality of nucleic acid sequences can characterize said one or more microorganisms with a specificity, sensitivity, or both, desired. In some respects, a scan of no more than 0.001%, 0.01%, 0.1%, 1%, 5%, 10%, 25%, 50%, 90%, 99%, 100% or any number between these from nucleic acid regions within said plurality of nucleic acid sequences characterizes said one or more microorganisms with sensitivity above 90%, 95%, 98%, 99%, 99.9%, 99.99% and 99.999% . In some respects, the method has less than 2%, less than 1.5%, less than 1.0%, less than 0.5%, or less than 0.1% of a false positive identification rate. In some aspects, a scan of no more than 1% of a complete genome can characterize the said microorganism.

[0086] In some cases, the high sensitivity and high specificity of the described methods are independent of a serotype of the microorganism. For example, a scan of a plurality of nucleic acid regions can identify a microorganism of the genus Salmonella that has a serotype selected from the group consisting of: Enteritidis, Typhimurium, Newport, Javiana, Kids, Montevideo, Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup, Paratyphi B var. L (+) Tartrate +, Agona, Thompson, and Kentucky; a microorganism of the genus Escherichia that has a serotype selected from the group consisting of: O103, O111, O121, O145, O26, O45, and O157; a microorganism of the genus Listeria that has a serotype selected from the group consisting of: 2a, 1 / 2b, 1 / 2c, 3a, 3b, 3c, 4a, 4b, 4ab, 4c, 4d, and 4e; a microorganism of the genus Campylobacter with the serotype of C. jejuni, C. lari, or C. coli and others.

[0087] A non-pathogenic strain of Citrobacter, namely Citrobacter sedlakii, expresses the Escherichia coli O157: H7 antigen. This is usually associated with the detection of an E. coli false positive in a sample. Typically, when Citrobacter is wrongly classified as E. coli, a batch of food can be unnecessarily discarded and a food processing facility can be wrongly classified as a contaminated facility. In some aspects, the high sensitivity of the described methods can be used to distinguish a microorganism from the genus Escherichia from a microorganism from the genus Citrobacter. In some cases, the report provides a method characterized by the fact that it comprises: scanning, by a computer, of a plurality of readings for sequencing a food sample or an environment associated with said food sample, so the said scanning distinguishes a microorganism from a Citrobacter genus from a microorganism from an Escherichia genus by identifying one or more single nucleotide polymorphisms that are associated with either said Citrobacter genus or with said Escherichia genus. Other examples include E. coli O157: H7 cross-reaction assay with E. coli O55 (which is not a STEC). Likewise, some assays provide false positives against E. coli O104 (which is not a STEC). Citrobacter is also a long-standing challenge for some E. coli O157: H7 systems.

[0088] In many cases, the onset of disease requires a rapid response, often including multi-jurisdictional coordinates. In some respects, the report provides methods for the rapid identification of a microorganism from a food sample. In some cases, the report provides a method for sequencing a plurality of nucleic acid sequences from a food sample, a sample from the environment associated with said food sample or another sample (such as a sample derived) for a period of time; and performing a test on said food sample or said environment associated with said food sample if said sequencing for said period of time identifies a threshold level of nucleic acid sequences of a microorganism in said food sample. In some cases, said time period is less than 12 hours, less than 6 hours, less than 4 hours, less than 2 hours, less than 1 hour, less than 30 minutes, less than 20 minutes, less than 15 minutes or another suitable time. Fig. 4 is a schematic illustrating the sequencing of a plurality of nucleic acid sequences in a food sample over a period of time and the advantages of performing a test on said food sample if said sequencing for said time period identifying a limit level of nucleic acid sequences of a microorganism in said food sample. Pathogenic Microorganisms

[0089] In general, a microorganism that can cause disease in its host, for example, by competing with it for metabolic resources, destroying its cells or tissues, or secreting toxins, can be considered as a pathogenic microorganism. Examples of classes of pathogenic microorganisms include viruses, bacteria, mycobacteria, fungi, protozoa, and some helminths. In some respects, the report provides methods for detecting one or more microorganisms from a food sample or from an environment associated with said food sample - such as a table, a floor, a boot cover , equipment from a food processing facility - or a food-related sample comprising soil, water, water quality, air, animal production, feed, manure, crop production, manufacturing plants, samples of the environment, or non-food derived samples, such as samples from clinical sources comprising blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid by analyzing a plurality of nucleic acid sequencing readings from such samples.

[0090] Many pathogenic microorganisms are further subdivided into serotypes, which can differentiate strains by their surface and antigenic properties. For example, Salmonella species are commonly referred to by the names of their serotypes. For example, the subspecies Salmonella enterica is further subdivided into numerous serotypes, including S. enteritidis and S. typhimurium. In some respects, the report's methods can distinguish between such subspecies of a variety of Salmonella by analyzing their nucleic acid sequences.

[0091] Escherichia coli (E. coli) bacteria normally live in the intestines of people and animals. Many E. coli are harmless and in some ways are an important part of a healthy human's intestinal tract. However, many E. coli can cause disease, including diarrhea or disease outside the intestinal tract and must be distinguished from less pathogenic strains. In some respects, the report's methods can distinguish between several subspecies of a variety of Escherichia bacteria by analyzing their nucleic acid sequences.

[0092] Listeria is a dangerous bacterium that can be found in refrigerated products, ready-to-eat foods (meat, poultry, seafood and dairy products - milk and unpasteurized milk products or foods made with unpasteurized milk), and products collected from soil contaminated with, for example, L. monocytogenes. Many animals can carry this bacterium without appearing to be sick, which adds to the challenges in identifying the pathogen derived from a food source. In addition, some species of Listeria can grow in refrigerator temperatures where most other foodborne bacteria do not grow, another factor that increases the challenges in identifying Listeria. When consumed, Listeria can cause listeriosis, a disease to which pregnant women and their fetuses are very susceptible. In some respects, the report's methods can distinguish between several subspecies of a variety of Listeria bacteria by analyzing their nucleic acid sequences.

[0093] Campylobacter jejuni is estimated to be the third leading bacterial cause of foodborne diseases in the United States. Raw poultry, unpasteurized (“raw”) milk and cheeses made from it, and contaminated water (for example,

non-chlorinated water, such as streams and ponds) are important sources of Campylobacter, but this also occurs in other types of meat and has been found in seafood and vegetables. In some ways, the report's methods can distinguish between several subspecies of a variety of Campylobacter bacteria by analyzing their nucleic acid sequences.

[0094] Non-limiting examples of pathogenic microorganisms that can be detected with the methods in the report include: the Escherichia coli pathogenic group, including Enterotoxigenic Escherichia coli (ETEC), Enteropathogenic Escherichia coli (EPEC), Escherichia coli Enterohemorrágica (EHEC), Escherichia coli Enterohemorrágica (EHEC), Escherichia coli colo Enteroinvasiva (EIEC), Salmonella spp., Campylobacter jejuni, Listeria, Yersinia enterocolitica, Shigella spp., Vibrio parahaemolyticus, Coxiella burnetii, Bovine mycobacteria, Brucella spp., Vibrio cholera, Vibrio vulnificus, Sponobila, Aeron and bacterium Plesiomonas shigelloides, Clostridium perfringens, Clostridium botulinum, Staphylococcus aureus, Bacillus cereus and other Bacillus spp., Listeria monocytogenes, Streptococcus spp., Enterococcus, and others. Identification of a New Microorganism in an Environment

[0095] Here are described methods and devices that allow the distinction of a microorganism that was recently introduced in a food processing facility or any other environmental setting in which hygienic screening, such as a hospital or clinic, is critical. In some cases, resident microorganisms reflect persistent contamination at a site, for example, a food processing facility or a hospital, which is very different from the transient pathogens that are being repeatedly introduced at the sites. Discrimination against resident and transient pathogens provides more clarity for differentiating sources of contamination and intervention strategies. This strategy can be used, for example, in order to manage contamination with the management of contamination with Listeria monocytogensis. For example, Campylobacter is part of the natural intestinal microflora of most animals that produce food, such as chickens, turkeys,

pigs, cattle and sheep. Typically, each contaminated bird carcass may contain about 100 to about 100,000 cells of Campylobacter. On the one hand, given the fact that fewer than 500 Campylobacter cells can cause infection, poultry products pose a significant risk to consumers who mishandle fresh or processed poultry during preparation or who undercoat them poorly. On the other hand, it is necessary to be able to distinguish a normal level of a Campylobacter in a food carcass from an excessive population of Campylobacter in a sample or the presence of a new strain of Campylobacter in a food processing environment, or food sample. It is also necessary to be able to identify a new source of contamination in an installation of existing sources. Fig. 4 illustrates a process for assessing the predicted risk based on a detection of a non-pathogenic microorganism. In summary, a food sample, such as a sample of steak illustrated as 401 is processed and an assay, such as a nucleic acid sequencing reaction, is performed. An analysis of a plurality of 401 nucleic acid sequencing readings may, in some cases, fail to detect a particular pathogen, such as the pathogenic E. coli illustrated in this example. Nevertheless, a 403 analysis of microbiome 402 of food sample 401 may indicate a high risk for that of a pathogen, such as E. coli. In such cases, the food sample can be sampled and reprocessed to confirm the presence of a pathogenic microorganism.

[0096] In some cases, the methods described here further comprise the performance of an additional assay in order to confirm the presence of the pathogenic microorganism in the sample, such as a serotyping assay, a polymerase chain reaction (PCR) assay, an enzyme-linked immunosorbent assay (ELISA), or an enzyme-linked fluorescent assay (ELFA), restriction fragment length polymorphisms assay (RFLP), pulsed field gel electrophoresis assay (PFGE), multi sequence typing assay - locus (MLST), DNA directed sequencing assay, complete genome sequencing assay (WGS), or shotgun sequencing assay.

[0097] In some respects, the report provides a method comprising obtaining a first plurality of nucleic acid sequences from a first sample from a food processing facility; creating a data file on a computer that associates one or more of said first plurality of nucleic acid sequences with said food processing facility; obtaining a second plurality of nucleic acid sequences from a second food sample from said food processing facility; and scanning a plurality of sequences from said second plurality of nucleic acid sequences to one or more sequences associated with said food processing facility in the created data file.

[0098] One or more data files can be created which associate a microorganism with a food processing facility. In some cases, a data file may provide a collection of sequencing readings that can be associated with one or more strains of a microorganism present in the processing facility. In some cases, more than 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, or 1000 bacterial strains can be associated with one or more food processing facilities . Correlating a Presence of a Microorganism with the Risk Associated with a Food Sample

[0099] This report recognizes that the presence of some non-pathogenic microorganisms, that is, indicator microorganisms, may be correlated with the presence of pathogenic bacteria in food, in a sample of the environment, or in another sample. In some respects, the report provides a method comprising detecting the presence or absence of a non-pathogenic microorganism in a food sample, an environment associated with said food sample, or another sample described here, by a computer system , and the presence or absence of a pathogenic microorganism in said food sample, associated environment, or another sample based on said presence or said absence of said non-pathogenic microorganism. Fig. 5 is a thermal map illustrating the predictive detection of pathogen through machine learning using associated non-pathogenic microorganisms. Data were collected from more than 20,000 food samples ranging from food categories identified by CODEX, with proportional presentation of their market share. Among these, about 950 samples were identified as having pathogens. Pathogens were detected using the Clear Labs sequencing platform, as well as with traditional culture. Through the sequencing of multiple regions, the bacteria present in the samples were detected and quantified (in relation to each other) at the species level.

[0100] The data was supplemented by alpha diversity measurements including Shannon's entropy, number of OTUs observed, and Faith's phylogenetic diversity measure. The quantification of bacteria in the samples and these supplemented measurements, produced coordinates for the data points used in the final classification. The distance between the data points was computed as a combination of the UniFrac distance and the Euclidean distance restricted to the supplemented coordinates.

[0101] The data points were divided into training and test subsets. A 10-fold stratified cross-validation was used to train the machine model of the support vector in the training set. The model's performance was measured in the previously separated test set. Scores regarding the detection of some of the pathogens are shown in Fig 5.

[0102] The machine classifier coefficients of the support vector were used to determine bacteria that play a role in determining the presence or absence of pathogens and, thus, provide signatures that can be used regardless of the model. This analysis determined a set of non-pathogenic microorganisms that showed a statistically significant correlation with the presence of pathogenic organisms, including members of the genus Enterobacter. Enterobacter asburiae, Enterobacter bugandensis, Enterobacter cancerogenus, Enterobacter cloacae, Enterobacter endosymbiont, Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii, and Enterobacter soli were among the 9 main examples of non-pathogenic bacteria associated with our set of pathogenic bacteria. For example, Yersinia pseudotuberculosis has been associated with Enterobacter asburiae; Vibrio vulnificus was associated with Enterobacter bugandensis, Enterobacter endosymbiont and Enterobacter soli; Escherichia coli, Salmonella enterica, and Shigella boydii were associated with Enterobacter cancerogenus, Enterobacter cloacae, and Enterobacter hormaechei; Staphylococcus Aureus was associated with Enterobacter kobei; and Yersinia pseudotuberculosis was associated with Enterobacter asburiae and Enterobacter ludwigii.

[0103] Without being limited to theory, a variety of other samples described here can be analyzed as described. In summary, a sample can be searched with any of the methods described here and a plurality of nucleic acid sequences can be obtained. Numerous sequences within said plurality of nucleic acid sequences can be correlated by a machine learning algorithm with a variety of microorganisms. A forecast can then be created and a visual result of such a forecast, such as that illustrated on a thermal map, can be created by detecting statistically significant correlations. For example, a thermal map created by the machine learning algorithm can illustrate a correlation between the presence of E. coli, Salmonella enterica, and Shigella boydii of one or more non-pathogenic microorganisms of the genus Enterobacter, such as Enterobacter cancerogenus, Enterobacter cloacae, and Enterobacter hormaechei or any other bacterial genera. In some ways, a machine learning algorithm, including the machine learning algorithms described here, can be used to create such predictions.

[0104] A statistical analysis can be performed to identify the main non-pathogenic species / food ingredients associated with the presence of

Vibrio / Staphylococcus / Yersinia / Shigella / Salmonella / Escherichia (an illustrative cluster-based representation of such an analysis is shown in Fig. 5). This analysis determined a set of non-pathogenic microorganisms that showed a statistically significant correlation with the presence of pathogenic organisms, including members of the genus Enterobacter. Enterobacter asburiae, Enterobacter bugandensis, Enterobacter cancerogenus, Enterobacter cloacae, Enterobacter endosymbiont, Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii, and Enterobacter soli were among the 9 main examples of non-pathogenic bacteria associated with our set of pathogenic bacteria. For example, Yersinia pseudotuberculosis has been associated with Enterobacter asburiae; Vibrio vulnificus has been associated with Enterobacter bugandensis, Enterobacter endosymbiont, and Enterobacter soli; Escherichia coli, Salmonella enterica, and Shigella boydii were associated with Enterobacter cancerogenus, Enterobacter cloacae, and Enterobacter hormaechei; Staphylococcus aureus was associated with Enterobacter kobei; and Yersinia pseudotuberculosis was associated with Enterobacter asburiae and Enterobacter ludwigii.

[0105] Food is a chemically complex matrix. Predicting whether, or how fast, microorganisms will grow in a food, or how quickly a food can spoil, is difficult. For example, most foods contain enough nutrients to support microbial growth. In addition, there are many additional factors that encourage, prevent or limit the growth of microorganisms in foods including pH, temperature and relative humidity. In some respects, this report recognizes that the presence of any microorganism, whether pathogenic or not, can be correlated with an expiration date, that is, a date when the food would be spoiled. In some respects, the report provides a method characterized by the fact that it comprises: detecting the presence or absence of a microorganism in a food sample or in a sample from the environment of a food processing facility; and prediction, by a computer system, of a risk presented by said food sample or by said food processing facility based on said presence or said absence of said microorganism.

[0106] Fig. 6 illustrates a process for predicting the shelf life of a food based on machine learning. In summary, Fig. 6 illustrates a sample scan, such as a scan of a plurality of nucleic acid sequencing readings. Subsequently, a machine learning algorithm is used to create a risk profile, so the said risk profile associates the presence of some microorganism with a low or high possibility of food deterioration, thus predicting the food's shelf life.

[0107] A machine learning algorithm can be used to associate any number of sequencing readings with the presence of a microorganism in a food sample, a food related sample, or another sample. Similarly, a machine learning algorithm may be able to associate any number of sequencing readings with the presence of a pathogenic microorganism, even if the sequence readings themselves are not from the pathogenic microorganism. Computer-implemented methods for generating a classifier based on machine learning in a system may require a number of input data sets in order for the classifier to produce highly accurate predictions. Depending on the microorganism, the matrix, and the amount of microorganisms in the actual matrix samples, the data can be in the range of 100, 1000, 10000, 100000, 1000000, 10000000, 100000000 sequencing readings. A machine learning algorithm is selected from the group consisting of: a support vector machine (SVM), a Naïve Bayes classification, a random forest, logistic regression and a neural network. Tuning a Test Resolution

[0108] It is possible to tune the resolution for the detection of a microorganism based on the source of the sample, for example, food versus surface mop; and the sensitivity of the assay itself, for example, genus, species, serotype, versus strain (obtained through complete genome sequences). Fig. 7 is a diagram illustrating the tunable resolution of several tests. In summary, one or more assays can be used sequentially to obtain a desired level of sensitivity, such as to determine a genus, a species, a serotype, a sub-serotype, or a strain of said microorganism. The tests can be identical or they can be different. Fig. 7 illustrates that a sequencing assay can be used to identify a strain or a sub-serotype of a microorganism, whereas a PCR reaction may be able to identify a species or, in some cases, a serotype of a particular micro-organism .

[0109] In some respects, the report provides a Method characterized by the fact that it comprises: obtaining a plurality of nucleic acid sequences from a food sample, from a sample from the environment or from another sample not derived from food from a food processing facility or another facility; carrying out a first test on said plurality of nucleic acid sequences of said food sample, whereby said test provides for the presence or foresees the absence of a microorganism in said food sample; and determination, based on said predicted presence or said expected absence of said microorganism from the first test, of whether it is necessary to perform a second test, so a sensitivity of said second test is selected in order to determine a genus, a species , a serotype, a sub-serotype, or a strain of said microorganism.

[0110] There are several approaches for processing nucleic acids from food samples or from ambient samples, such as polymerase chain reaction (PCR) and sequencing. In some cases, said assay is a sequencing assay that provides the ability to obtain real-time sequencing readings, such as a pore sequencing assay. Sequencing can be performed by several systems currently available, such as, without limitation, a sequencing system from Illumina®, Pacific Biosciences (PacBio®), Oxford Nanopore®, Genia (Roche) or Life Technologies (Ion Torrent®). Alternatively or in addition, sequencing can be performed using nucleic acid amplification, polymerase chain reaction (PCR) (for example, digital PCR, quantitative PCR, or real-time PCR), or isothermal amplification.

[0111] Several strategies can be used for amplification. In some cases, nucleic acid amplification, polymerase chain reaction (PCR) (for example, digital PCR, quantitative PCR, or real-time PCR), or isothermal amplification involves amplification with completely or partially degenerate primers. In some cases, nucleic acid amplification, polymerase chain reaction (PCR) (for example, digital PCR, quantitative PCR, or real-time PCR), or isothermal amplification involves targeted amplification of a particular gene or genomic regions. In some cases, targeted amplification for a particular gene or genomic regions involves targeted amplification of regions containing and / or circumscribing SNPs, RFLPs, STRs, VNTRs, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, repeats simple, indelible, and / or insertion elements associated with or variables between individual microorganisms or microorganism serotypes. Amplification oriented towards the particular gene or genomic regions may involve the use of sets of oligonucleotide primers that are partially or completely complementary to the flank regions of SNPs, RFLPs, STRs, VNTRs, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats , tetranucleotide repetitions, simple, indelible sequence repetitions, and / or insertion elements. In some embodiments, targeted amplification uses at least one, 100, 200, 300, 400, 500, 600, 700, or 800 pairs of oligonucleotide primers to amplify a particular gene or genomic regions of the nucleic acids.

[0112] In some cases, the assay is a serotyping assay. The serotyping assay can comprise an enzyme-linked immunosorbent assay (ELISA) or an enzyme-linked fluorescent assay (ELFA). A serotype or serovar is a distinct variation within a species of bacteria or virus. These microorganisms can be classified together based on their cell surface antigens, allowing the epidemiological classification of microorganisms at the subspecies level. A group of serovars with antigens in common is called a serogroup or sometimes a serocomplex. In some respects, the report provides methods for performing a sequencing assay on a plurality of nucleic acids derived from a sample and then conducting a serotyping assay on a derivative of said sample. Fig. 8 is a schematic illustrating various serotypes of various microorganisms that can be detected by analyzing a plurality of nucleic acid sequences as described herein and further validating by a serotyping assay. Differentiation of Living versus Dead Microorganisms

[0113] Analytical methods targeting nucleic acid, such as PCR, provide only limited information about the activities and physiological states of microorganisms in samples and cannot distinguish viable cells from dead cells. In some respects, the report provides methods for distinguishing a living microorganism in a food sample or in another sample, from a dead microorganism in the same sample. Fig. 9 is a schematic illustrating a process for distinguishing a living microorganism from a food food or a sample from the environment. In summary, Fig. 9 illustrates that an amount of a microorganism in a sample can be increased, that is, enriched 901, by growing the microorganism in a rich medium over a period of time. A reagent, such as a photo-reactive DNA binding dye, a DNA intercalating reagent, or another suitable reagent can be added to the enriched sample 901. Such reagents distinguish live microorganisms 902 from dead microorganisms 903 by interacting with the sequence of nucleic acid only from dead microorganisms. In some cases, the report contemplates the use of monoazide propidium or a derivative of it as a dye. The modified sample can be prepared for a subsequent 904 reaction, such as a 905 sequencing reaction.

[0114] In some cases, the report provides a method comprising adding a reagent to a plurality of nucleic acid molecules from a food sample, or food-related sample or another sample described here, thereby forming a plurality modified nucleic acid molecules, whereby said reagent (i) interacts with and modifies a structure of a plurality of nucleic acid molecules derived from one or more dead microorganisms; and (ii) does not interact with or modify a structure of a nucleic acid molecule derived from one or more living microorganisms; thereby providing a plurality of modified nucleic acid molecules; and sequencing said plurality of modified nucleic acid molecules, thereby distinguishing one or more living organisms from said food sample or another sample.

[0115] In other respects, the report provides a method comprising performing a pore sequence or other DNA sequence or hybridization assay on a plurality of nucleic acid molecules in a food sample or another sample, so said pore sequencing reaction distinguishes one or more nucleic acid molecules derived from a dead microorganism from one or more nucleic acid molecules derived from a living microorganism based on a methylation pattern or other epigenetic pattern from said one or more molecules nucleic acid derived from said dead microorganism.

[0116] In some embodiments, epigenetic patterns, such as methylation, can be detected in DNA derived from food or a sample from the environment by chemical or enzymatic selection methods prior to sequencing. Such methods include, but are not limited to, disulfide sequencing (including targeted disulfide sequencing, see, for example, Ziller et al. Epigenetics Chromatin. 2016 Dec 3; 9: 55 and Masser et al. J Vis Exp. 2015; (96): 52488) and digestion by sensitive restriction and methylation (see, for example, Bitinaite et al. US patent 9,034,597).

[0117] In some embodiments, epigenetic patterns can be detected in DNA derived from food or samples from environments by characteristic changes in the ion current during nanopore sequencing (see, for example, Wescoe et al. J Am Chem Soc. 2014 Nov 26; 136 (47): 16582-7 and Laszlo et al. Proc Natl Acad Sci US A. 2013 Nov 19; 110 (47): 18904-9). Barcodes

[0118] Unique identifiers, such as bar codes, can be added to one or more nucleic acids isolated from a sample from a food processing facility, hospital or clinic, or from another source. Barcodes can be used to associate a sample with a source; for example, to associate a sample of the environment with a specific food processing facility or with a particular location in said food processing facility. Barcodes can also be used to identify processing of a sample, as described in US patent publication 2016/0239732, which is fully incorporated here by reference.

[0119] In some respects, the report provides a method comprising adding a first barcode to a first plurality of nucleic acid sequences in a sample, thereby providing a first plurality of nucleic acid sequences with codes of bars; carrying out a first sequencing reaction on said first plurality of barcode nucleic acid sequences, wherein said sequencing reaction is carried out in a sequencing apparatus comprising a flow cell; adding a second barcode to a second plurality of nucleic acid sequences from a second sample, thereby providing a second plurality of nucleic acid sequences with barcodes; and carrying out a second sequencing reaction on said second plurality of barcode nucleic acid sequences, wherein said second sequencing reaction is carried out on said sequencing apparatus comprising said flow cell, thus reusing said flow cell . Fig. 10 illustrates a process for reusing flow cells with different indexes as described here. As shown in Fig. 10 two different indices, 1001 and 1002, such as two different bar codes, can be added to different samples before sequencing 1003. Since a first sample can be associated with a first index 1001 and a second sample can be associated with a second index 1002 this process effectively allows the reuse of a flow cell. Fig. 18 and Fig. 19 demonstrate the reuse of MinION / GridION flow cells. Example 21 demonstrates how certain primer design schemes, such as a non-periodic design, can reduce interference in situations with high multiplexing or closely related sequences, as can happen with the reuse of flow cells.

[0120] One or more bar codes or bar code blocks can be added to a nucleic acid sequence from a food sample or another sample from a food processing facility, such as a first, second, one third or any subsequent sample. In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 identical bar codes are added such samples. In other cases, separate bar codes are added to such samples. In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 separate bar codes are added and such samples. The serial addition of two or more bar codes, whether identical in sequence or distinct in sequence, can provide an indexing of a sample that is used in its analysis. The presence of additional barcode or barcode blocks makes the system more robust against any errors in the manufacture of the barcode and can also significantly reduce the chance of cross contamination between barcodes. In some cases, a bar code is added to a nucleic acid sequence comprising complementary DNA (cDNA) sequences, ribonucleic acid (RNA) sequences, genomic deoxyribonucleic acid (gDNA) sequences, or a mixture of cDNA, RNA sequences and gDNA.

[0121] Bar codes can have a variety of lengths. In some cases, a bar code is about 3 to about 25 nucleotides in length, from about 3 to about 24 nucleotides in length, from about 3 to about 23 nucleotides in length, from about 3 to about 22 nucleotides in length, from about 3 to about 21 nucleotides in length, from about 3 to about 20 nucleotides in length, from about 3 to about 19 nucleotides in length, from about 3 to about 18 nucleotides in length, from about 3 to about 17 nucleotides in length, from about 3 to about 16 nucleotides in length, from about 3 to about 15 nucleotides in length, from about 3 to about 14 nucleotides in length, from about 3 to about 13 nucleotides in length, from about 3 to about 12 nucleotides in length, from about 3 to about 11 nucleotides in length, from about 3 to about 10 nucleotides in length, from about 3 to about 9 nucleotides of c length, from about 3 to about 8 nucleotides in length, or from about 3 to about 7 nucleotides in length.

[0122] In other cases, a barcode is from about 4 to about 25 nucleotides in length, from about 4 to about 24 nucleotides in length, from about 4 to about 23 nucleotides in length, about from 4 to about 22 nucleotides in length, from about 4 to about 21 nucleotides in length, from about 4 to about 20 nucleotides in length, from about 4 to about 19 nucleotides in length, from about 4 to about 18 nucleotides in length, from about 4 to about 17 nucleotides in length, from about 4 to about 16 nucleotides in length, from about 4 to about 15 nucleotides in length, from about 4 to about 14 nucleotides in length, from about 4 to about 13 nucleotides in length, from about 4 to about 12 nucleotides in length, from about 4 to about 11 nucleotides in length, from about 4 to about 10 nucleotides in length, from about 4 to about 9 nucleotides ids in length, from about 4 to about 8 nucleotides in length, or from about 4 to about 7 nucleotides in length.

[0123] A bar code is from about 5 to about 25 nucleotides in length, from about 5 to about 24 nucleotides in length, from about 5 to about 23 nucleotides in length, from about 5 to about 22 nucleotides in length, from about 5 to about 21 nucleotides in length, from about 5 to about 20 nucleotides in length, from about 5 to about 19 nucleotides in length, from about 5 to about 18 nucleotides in length, from about 5 to about 17 nucleotides in length, from about 5 to about 16 nucleotides in length, from about 5 to about 15 nucleotides in length, from about 5 to about 14 nucleotides in length, from about 5 to about 13 nucleotides in length, from about 5 to about 12 nucleotides in length, from about 5 to about 11 nucleotides in length, from about 5 to about 10 nucleotides in length, about 5 to about 9 nucleotides in length therefore, from about 5 to about 8 nucleotides in length, or from about 5 to about 7 nucleotides in length.

[0124] A barcode is from about 6 to about 25 nucleotides in length, from about 6 to about 24 nucleotides in length, from about 6 to about 23 nucleotides in length, from about 6 to about 22 nucleotides in length, from about 6 to about 21 nucleotides in length, from about 6 to about 20 nucleotides in length, from about 6 to about 19 nucleotides in length, from about 6 to about 18 nucleotides in length, from about 6 to about 17 nucleotides in length, from about 6 to about 16 nucleotides in length, from about 6 to about 15 nucleotides in length, from about 6 to about 14 nucleotides in length, from about 6 to about 13 nucleotides in length, from about 6 to about 12 nucleotides in length, from about 6 to about 11 nucleotides in length, from about 6 to about 10 nucleotides in length, about 6 to about 9 nucleotides in length therefore, from about 6 to about 8 nucleotides in length, or from about 3 to about 7 nucleotides in length. Device

[0125] An automated nucleic acid sequencing apparatus can provide a robust platform for generating nucleic acid sequencing readings. Unfortunately, many devices have a high failure rate, that is, a high error rate of the sequencing reaction itself, which requires manual intervention in such cases, such as reloading samples in the flow cells. In some respects, the report provides an automated nucleic acid sequencing apparatus that does not require manual intervention in the event of a failure of a sequencing reaction. In some respects, the report provides a nucleic acid sequencing apparatus comprising: a nucleic acid library preparation compartment comprising two or more cameras configured to prepare a plurality of nucleic acids for a sequencing reaction, where said compartment is connected operationally to a nucleic acid sequencing camera; a nucleic acid sequencing chamber, wherein said nucleic acid sequencing chamber comprises: (i) one or more flow cells comprising a plurality of pores configured for passage of a nucleic acid strip, wherein said two or more flow cells are juxtaposed with each other; and an automated platform, where said automated platform is programmed to move robotically from said nucleic acid library preparation compartment to said nucleic acid sequencing camera. Fig. 11 illustrates an automated report sequencing device. 1101 is a diagram of the apparatus comprising nucleic acid sequencing compartment 1102. Nucleic acid library preparation compartment 1103 shows a variety of cameras configured to prepare a plurality of nucleic acids for a sequencing reaction in close proximity to a sequencing 1104, which comprises one or more flow cells. In summary, an automated reporting apparatus is programmed to move one or more samples from library preparation cameras 1103 to a sequencing camera 1104 in detecting a failure in a sequencing reaction. This provides a sequencing process without chicks of human interference after a sample has been added to the library preparation chamber, as illustrated in Fig. 12. Fig. 12 illustrates an embodiment in which a sample from a food processing facility , from a hospital or clinical setting, or from another source can be processed manually between 6 am to 6 pm or any shorter or longer incubation window by incubating the sample in the presence of a growth medium (for example , enrichment) and automatically processed after the sample has been added to a 1103 nucleic acid preparation chamber.

[0126] The apparatus described is programmed in such a way that said automated platform moves one or more samples from said nucleic acid library preparation compartment to said nucleic acid sequencing chamber. Upon detection of a failure and a sequencing reaction, the automated platform moves one or more samples from the failing flow cell or device to the next flow cell or sequencing device. In many cases, such samples comprise nucleic acid sequences that include one or more bar codes. In some cases, a plurality of mutually exclusive bar codes is added to a plurality of nucleic acids in said two or more cameras in the nucleic acid library preparation compartment 1103, thereby providing a plurality of nucleic acids with codes of mutually exclusive bars on the device. In some cases, the automated platform robotically displaces two or more of said mutually exclusive nucleic acid bar codes to said nucleic acid sequencing camera, in some cases by displacing said mutually exclusive nucleic acid bar codes to the same flow cell of said one or more flow cells.

[0127] This report describes an apparatus for the automatic detection of foodborne pathogens by sequencing genomic libraries of samples introduced into the instrument. In some ways, the device can comprise four main components: cameras for library preparation, fluid handling systems, sequencing flow cells, and automation systems. Within the scope of this report, there are numerous possible uses of the pathogen detection system. Library Cameras

[0128] This report describes a device comprising one or more library cameras. Each library camera may be capable of a wide range of functions including, but not limited to, sample preparation, sample enrichment, nucleic acid amplification, and cleaning. In some respects, library preparation can be done entirely within a single library camera. In other respects, each library camera can be reserved for a separate function in the library preparation process. Depending on the processes required for library preparation, library cameras can be operationally connected to each other and to one or more flow cells, or they can only have operational connections to a sequencing flow cell.

[0129] A library camera can comprise one or more cameras and a fixable hatch. The hatch allows user access or an automatic sample loading system. The hatch opening and closing mechanism can be manual or electrically operated. In some respects, a library camera may comprise a main and a secondary compartment for pre-loading a sample. Pre-loading can comprise a decontamination process or other processes to prevent external contaminants such as dust and pollen from entering the device. After decontamination, the specimen can be transferred to the main compartment. In other respects, samples can be loaded into a single library camera and the entire library camera will be decontaminated.

[0130] Library cameras can be configured to accommodate a wide variety of samples. When screening for the appearance and spread of a foodborne pathogen, many possible types of samples can be tested, including, but not limited to, soil samples, culture samples, tissue samples, cloth swabs, dung samples , and fluid samples. In some respects, this report may provide a dynamic platform that is capable of enriching a detectable amount of nucleic acids in a sample. In some respects, the library camera may comprise a fixed unit of the apparatus described in this report and is capable of repeated reuse. In some respects, a reporting device comprises at least 5, at least 10, at least 15, at least 20, or another suitable number of library cameras 1103. In other aspects, the library camera may comprise a cartridge for the sample collection in the field. In such an embodiment, the library camera would be loaded manually into the sequencing apparatus prior to the start of automatic library preparation and sequencing testing.

[0131] For the present report, a library camera can be configured in multiple ways depending on how it will be used. A library camera can comprise one or more ports of entry for the addition of reagents, gases, or any other materials necessary for the preparation of the library. The entrance doors can be physically positioned anywhere in the library camera depending on the function of the entrance door. In some respects, a library camera may comprise one or more input ports. In some respects, a library camera may comprise 1 to 10 input ports, 2 to 10 input ports, 3 to 10 input ports, 4 to 10 input ports, 5 to 10 input ports, 6 to 10 ports 7-10 entry doors, 8-10 entry doors, or 9-10 entry doors. In some aspects, an entry port can be configured for the introduction of gases, liquids or solids. In some respects, an entry door can be positioned close to the top of the library camera for uses such as adding liquid media. In other respects, an entry door can be positioned close to the bottom of the library chamber for uses such as gas bubbling or spraying. A library camera can also comprise one or more output ports. In some ways, a library camera can comprise one or more output ports. In some respects, a library camera may comprise 1 to 10 outgoing ports, 2 to 10 outgoing ports, 3 to 10 outgoing ports, 4 to 10 outgoing ports, 5 to 10 outgoing ports, 6 to 10 ports outgoing, 7 to 10 outgoing ports, 8 to 10 outgoing ports, or 9 to 10 outgoing ports. In some respects, an exit port can be configured to remove gases, liquids or solids.

[0132] The preparation of nucleic acid libraries may require the enrichment of a sample by culturing such samples in a nutrient medium that supports the enrichment of a sufficient amount of nucleic acids in order to perform a sequencing assay. In some ways, the library camera can work to enrich a sample by functioning as a cell culture camera. In such a configuration, the library chamber can be filled with cell growth medium and any other reagents necessary to promote cell growth. In some ways, the library camera can be connected to thermal regulation modules, including both heating and cooling, in order to promote optimal cell growth. The library camera may be capable of aerobic or anaerobic operation. In some ways, the aerobic operation may comprise the bubbling or spraying of oxygen or air. In other respects, the anaerobic operation may comprise bubbling or spraying the library camera with a gas without oxygen, not limited to nitrogen, helium, carbon dioxide or hydrogen. Mechanical agitation of the cell culture may be necessary to prevent cell sedimentation. In some respects, agitation can be provided by sufficient bubbling of gases through the library chamber. In other respects, a micro propellant can provide mechanical mixing for the library camera. In some respects, a micro propellant may comprise a propeller blade connected to an engine by means of a sealed bearing on a surface of the library camera. In other respects, a micro propellant may comprise a propeller blade and shaft entirely within the library chamber. In such an embodiment, the propeller blade and shaft may comprise a magnetically susceptible material in such a way that the operation of an electromagnet near the surface of the library can induce the rotation of the blade. In some ways, a library camera can be used to lyse cells as a method for releasing nucleic acids contained within cells. In some respects, cell lysis and nucleic acid capture can be performed inside a library camera. In other respects, cell lysis and nucleic acid capture can be performed in successive library chambers through a series of tests and material transfer between library chambers.

[0133] In some ways, a library camera in this report may comprise a DNA amplification and manipulation device. The library camera can be a platform for any DNA amplification technique, including, but not limited to, emulsion PCR. As a PCR platform, the library camera can include a thermal cycler. The library camera may also comprise a device for a variety of other DNA manipulation techniques including, but not limited to, restriction assays and binding assays. In some respects, the present report may comprise a means to amplify a nucleic acid library. The library camera can be used to fragment larger genomic DNA and add identification strings such as bar codes to nucleic acid fragments. All DNA manipulations can be performed in a single library camera or multiple assays can be performed in one or more successive library cameras.

[0134] A library camera can be comprised of a variety of materials depending on the tests to be performed on it. The library can be comprised of materials such as metal, glass, ceramic or plastic.

Library cameras can comprise metals that are non-magnetic, paramagnetic or ferromagnetic.

In this report, library cameras can comprise metals such as aluminum, tungsten, tungsten oxide, austenitic stainless steel, or ferritic stainless steel.

A library camera can comprise a thermoplastic or machinable plastic.

The library camera can comprise a plastic such as polyethylene, polypropylene, polyester or polycarbonate.

The camera material can be chosen for a variety of properties including, but not limited to, biocompatibility, resistance and corrosion, chemical reactivity, surface energy, electrical capacitance, electrical resistivity, electrical conductivity, magnetic properties, ductility, durability, elasticity , flexibility, hardness, malleability, mass density, tension resistance, surface roughness, machinability, light absorbance, light transmittance, refractive index, light emissivity, thermal expansion, specific heat and thermal conductivity.

In some respects, a library camera may be composed of a single material that is acceptable for all intended uses.

In other respects, a library camera can be composed of multiple materials, for example, a glass camera with metallic inserts for connections to the entrance and exit doors.

In some aspects, the surfaces of the library camera may comprise a chosen material with an applied coating.

Such coatings can be used for a variety of purposes including, but not limited to, anti-corrosion, anti-friction, hydrophobicity, hydrophilicity, anti-agglomeration, anti-adsorption, pro-adsorption, anti-fouling, anti-static, chemical reactivity and chemical inertia.

In this report, the library preparation portion of the apparatus may comprise multiple library cameras arranged in parallel or in series for a variety of purposes.

In any case, each library camera in the apparatus may comprise a different material design specifically chosen for the intended application of the library camera.

[0135] Library cameras can be designed to include online detection. The purpose of detection systems can include measurement system properties or failed detection tests. Detection systems can be used to measure a variety of system properties, including, but not limited to, cell density, nucleic acid concentration, nucleic acid purity, pH, temperature, pressure, oxygen concentration, fluid density , fluid viscosity, dielectric constant, absorption spectrum and thermal capacity. In some respects, a library camera may include an optical port comprised of an optically opaque material such as quartz glass. In some aspects, the transmittance, absorption, reflection or refraction of visible, infrared, microwave or ultraviolet light sources can be measured using built-in optical ports. In some respects, library cameras may include mechanical doors for the insertion of measuring devices including, but not limited to, pH probes, thermocouples, pressure gauges and dielectric probes. In some respects, library cameras can be designed to allow fluid to be drawn from the fluid inlet and outlet ports for measurement in downstream instrumentation. Fluid Handling Systems

[0136] In this report, fluid transfer may occur between one or more library chambers and one or more sequencing flow cells. The apparatus may comprise systems to ensure the precise transfer of fluids. Fluid transfer may also be involved in other aspects of device operation, including, but not limited to, cell culture, cell lysis, nucleic acid purification, nucleic acid amplification, nucleic acid binding, acid fragmentation nucleic acid sequencing, flow cell sequence initiation, camera mixing, camera cleaning and camera emptying. The sequencing flow cell, as designed, must maintain a gas-free operational state for its entire life. In many of the achievements in this report, the fluid handling system will be designed to ensure that no gas can be transferred from the library preparation system to the sequencing flow cell system.

[0137] Numerous fluids can be involved in the detection of the foodborne pathogen described in this report. The liquids used can include buffers, acids, bases, surfactants, emulsions, suspensions, chelating agents and solutions. The liquids used may include, but are not limited to, deionized water, HCl, H2SO4, HNO3, NaOH, KOH, NaCl, KCl, CaCl2, MgCl2, EDTA, ethanol and methanol. The gases used can include inert gases, oxidizing gases and reducing gases. The gases used can include, but are not limited to, N2, air, O2, He, Ar, H2, and CO2. Commonly used liquids can be stored in the device. In some ways, liquids can be stored in "onboard" cameras. In other respects, liquids can be stored in cartridges that can be placed or removed manually or using an automated system. In some aspects, the gases can be released through external tanks or probed lines through entrance ports on the device.

[0138] Fluids can be displaced by a foodborne pathogen detection device by a variety of mechanisms. Fluid displacement devices can include pumps, compressors, regulators, blowers and fans. The apparatus in this report may comprise one or more pumps for transferring liquid. These pumps can be responsible for displacing fluids to library cameras, emptying library cameras, transferring fluids from library cameras to sequencing flow cells, displacing fluids through flow cells, preventing sedimentation of solids, cleaning filters and draining waste fluids from the device. Depending on the specific applications, the pumps included in the described apparatus may comprise positive displacement pumps, peristaltic pumps, gear pumps, rotary pumps, screw pumps, piston pumps or diaphragm pumps. Pumps and compressors can also be used for gas transfer.

Regulators and compressors can be used to adjust the gas pressures in the device. Vacuum pumps can be used to empty library chambers during emptying operations. Fluid transfer can also be achieved through passive mechanisms such as gravity feeding and capillary action. A pathogen or non-pathogenic microorganism detection device may comprise one or more valves for fluid control. The valves can be located on any flow line, including inlet and outlet ports for library cameras, inlets and outlets for sequencing flow cells, and inlet and drain ports for the device. The valves can be subject to manual control or automated control. Fluid transfer can be controlled by devices such as mass flow controllers and flow meters. Regulation of fluid transfer can be achieved through manual controls on the device, analog or digital control systems on the device, or through computer systems interfacing with a remote sequencing device.

[0139] For this report, connectivity between device inputs, library cameras, sequencing flow cells, and drain ports can be achieved. Connectivity can be achieved through direct coupling of components to joints that are somehow sealed or joints that have mobile openings. Connectivity between components can be achieved by any suitable method, including, but not limited to, combining flanges, compression adjustments, friction adjustments, hose barbs and magnetic couplings. In some respects, a seal may be required between two connected components. Depending on the design, a seal may need to be airtight, leak-free, detachable or permanent. A seal may comprise a gasket, O-ring, metal compression adjustment or plastic compression adjustment. Seals can be chosen from a variety of materials including, but not limited to, polypropylene, polycarbonate, rubber, copper or graphite. Connectivity can also comprise piping or tubes between system components. The tubing or tubes can comprise any material suitable for the chosen application. Materials can be chosen for properties including anti-fouling, anti-friction, hydrophobicity, hydrophilicity, durability, flexibility, resistance, cost and biocompatibility. Pipe or tube materials may include, but are not limited to, stainless steel tubes, copper tubes, aluminum tubes, bronze tubes, rigid plastic tubes or flexible plastic tubes. In some ways, the tubing or tubes can be permanently attached to the device. In other respects, piping lines or tubes may be disposable.

[0140] Other devices may necessarily be part of the flow system for the device described in this report. In some respects, several flow lines can be equipped with one or more filters. The filter can be for liquids or gases. Filters can be for a variety of purposes including capture cells or cellular components, maintaining the sterility of external fluid sources, capturing any contaminating particles, or any other debris that should be excluded from the library chambers or sequencing flow cells. In some respects, one or more bubblers can be included in the connection between a library camera and an external gas line. Bubblers can comprise a porous metal, porous glass or any other porous material that distributes gas in flux. In some aspects, a separation device may comprise a connection between units in the described apparatus. The separation devices can be used to perform ultrafiltration, adsorption, reverse osmosis, extraction, chromatography, sedimentation, sieving or vapor-liquid separation. In some ways, a device can be placed between a library camera and a sequencing flow cell to ensure the removal of all gas bubbles.

[0141] In some ways, the sterility of the sequencing apparatus can be maintained to promote a more efficient operation or a sequencing that is exposed to less contaminants. The internal and automated parts of the device can be placed inside a sealed socket in order to prevent the intrusion of any particles originating in the air and debris such as dust, mold, mold, pollen, bacteria, viruses and lint. The sealed housing can be purged of ambient air using a vacuum pump or can be maintained under positive pressure by means of a fixed source of compressed gas. Certain external parts of the device may require frequent exposure to the external environment, presenting potential sources of contamination, for example, library cameras during sample insertion. Any external door, entrance or camera can be maintained under positive pressure in order to minimize the chances of unwanted debris or biological entities being deposited in the system during operation. Any and all of the cameras, cells and fluid transfer systems can be subjected to one or more washing, cleaning or purging processes to remove contaminants or residual matter from normal operations. Washing, cleaning and purging may include the use of detergents, surfactants, acids, bases, alcohols, deionized water, DNAses, RNAses, proteases, lipases, or any other cleaning method. Washing, cleaning or purging may involve heat treatments or vacuum evacuation. Any and all fluid transfer systems can comprise materials with anti-fouling or biocide coatings or surface functionalization in order to minimize the deposition of contaminants, especially in regions with fluid stagnation. The fluid flow can be laminar to increase the residence time in one of the apparatus, for example, an extended cleaning step, or it can be turbulent to reduce the residence time or reduce the mixture, for example, rapid cell movement to reduce sedimentation. Automation Systems

[0142] The foodborne pathogen detection apparatus described in this report is intended for autonomous or semi-autonomous operation. In some aspects, the instrument may require only manual intervention for the entry of samples and reagents, and all additional operations can be conducted using an automated software / hardware system. In other aspects, the device may require manual entry of information, instructions or physical materials, such as reagents, at particular times in the instrument's operations. The device can operate using algorithms customized for each operation or it can use standard algorithms. The algorithms can be entered manually via onboard control systems or sent from a remote computer system. The device can be connected to an external computer system or communicate wirelessly. The sequencing apparatus may be able to export data in packets or transmit data in real time as sequencing is performed. In some ways, the device will automatically detect flawed operations including, but not limited to, faulty bacterial enrichment, faulty DNA amplification or purification, and flawed sequencing. In some respects, the system may include analytical or diagnostic devices at the entry or exit ports, library cameras, or any flow line to provide data on the status of ongoing operations.

[0143] The device in this report can operate by means of electrical supply from an external power source, for example, a wall outlet, or operate by means of a self-contained battery system. Field-portable versions of the device can be designed to work in conjunction with portable power systems such as solar panels or portable generators. In some ways, the device will comprise all the electrical components necessary to accept DC or AC power, depending on the power supply source dictates.

[0144] The sequencing device can use robotics for automated operation. In some respects, robotics may be responsible for any and all internal operations, including, but not limited to, displacing fluids, opening and closing valves, adding reagents, performing cleaning operations, performing and monitoring cleaning operations. bacterial growth, carrying out and monitoring of DNA amplification and purification operations, carrying out sequencing tests, initiation or reconditioning of flow cells, and disposal of waste from the device. In some aspects, all the components of a sequencing device can be fixed in position, with robotics used primarily to control the movement of liquids, gases and other materials through the system. In other respects, robotics can be used to move library cameras to a point of direct connectivity with another part of the system, for example, a sequencing flow cell.

[0145] In some respects, fluid transfer operations can be mediated by one or more automated pipette systems. In some respects, a pipette system can comprise a single pipette. In other respects, a pipette system may comprise a set of pipettes arranged in a multiplexed manner. One or more pipettes may be able to dispense fluids by means of a positive pressure flow or to remove fluids by means of a negative pressure differential (vacuum). In some ways, one or more pipettes can be configured to dispense or withdraw fluids individually. In other respects, one or more pipettes can be configured to dispense or withdraw fluids simultaneously. Fluids can be dispensed or removed continuously or measured. In some respects, a metered pipette may dispense with or withdraw fluid volumes of about 0.1 µl to about 1000 µl. In some aspects a measured pipette can dispense or withdraw fluid volumes of about 0.1 µl to 10 µl, 0.1 µl to 20 µl, 0.1 µl to 30 µl, 0.1 µl to 40 µl, 0, 1 µl to 50 µl, 0.1 µl to 60 µl, 0.1 µl to 70 µl, 0.1 µl to 80 µl, 0.1 µl to 90 µl, 0.1 µl to 100 µl, 1 µl to 10 µl, 1 µl to 20 µl, 1 µl to 30 µl, 1 µl to 40 µl, 1 µl to 50 µl, 1 µl to 60 µl, 1 µl to 70 µl, 1 µl to 80 µl, 1 µl to 90 µl, 1 µl to 100 µl, 10 µl to 20 µl, 10 µl to 30 µl, 10 µl to 40 µl, 10 µl to 50 µl, 10 µl to 60 µl, 10 µl to 70 µl, 10 µl to 80 µl, 10 µl at 90 µl, 10 µl at 100 µl, 10 µl at 200 µl, 10 µl at 300 µl, 10 µl at 400 µl, 10 µl at 500 µl, 10 µl at 1000 µl, 100 µl at 200 µl, 100 µl at 300 µl, 100 µl to 400 µl, 100 µl to 500 µl, 100 µl to 1000 µl, 250 µl to 500 µl, or about 250 µl to 1000 µl. In some respects, each pipette may comprise a separate pressure driver. In other respects, two or more pipettes can be controlled by the same pressure driver. Pipette tips can be permanent or disposable. In some respects, disposable pipette tips may comprise a hollow piece of plastic that combines with a permanent surface. Disposable pipette tips can be attached to the permanent surface by pressing down on the permanent surface over the plastic. A fluid transfer system may comprise an automated method for removing disposable pipette tips.

[0146] A set of pipettes with pressure actuators or connectivity to pressure actuators can be mounted in an automated translation stage capable of movement in one or more dimensions. In some ways, an automated translation stage may be capable of three-dimensional movement. In some respects, an automated translation stage may be capable of rotary motion. In some aspects, an automated translation system can be coupled to one or more motors, pneumatic devices, or any other method of producing linear motion. The translation can be produced continuously or incrementally, step by step. The translation movements can be produced in the order of about 1 µm, 2 µm, 3 µm, 4 µm, 5 µm, 6 µm, 7 µm, 8 µm, 9 µm, 10 µm, 20 µm, 30 µm, 40 µm , 50 µm, 60 µm, 70 µm, 80 µm, 90 µm, 100 µm, 200 µm, 300 µm, 400 µm, 500 µm, 600 µm, 700 µm, 800 µm, 900 µm, 1 mm, 5 mm, 10 mm, 20 mm, 30 mm, 40 mm, 50 mm, 100 mm, 200 mm, 300 mm, 400 mm, 500 mm, 600 mm, 700 mm, 800 mm, 900 mm, or 1000 mm. Automated Initiation and Library Loading Device

[0147] Oxford Nanopore flow cells have a flow path as shown in Fig. 25, (which shows a schematic cross section along the flow path, top, and the corresponding characteristics in a flow cell representation , bottom) and are pre-filled with a conditioning liquid to protect the nanopore membrane during storage. A further description of such flow cells can be found, for example, in WO 2018007819A1. However, the commercial form of the Oxford Nanopore flow cell (for example, GridIONx5TM cell) is not provided in a form in which all steps of preparation for sequencing can be carried out by an automatic process. In particular, all steps of replacing the storage buffer and initiating the flow of liquid through the flow cell are difficult to automate in view of a removable flat plastic seal that covers the sample inlet port (the presence and absence of which it is shown in Fig. 14 and Fig, 15 that it cannot be conveniently removed by automated processes.

[0148] The nanopore flow cell device comprises a sensor provided in a detection camera (2505); a flow path comprising a detection camera inlet (2509) and a detection camera outlet (2504) connected to the detection camera for the respective passage of liquid into and out of the detection camera, and an inlet port sample (2506) in fluid communication with the input; and a liquid collection channel (2503) downstream of the outlet. The device additionally contains an interruption of the flow path (2502, for example, a valve activated by an actionable lever accessible from the upper surface of the device) between the outlet of the detection chamber (2501) and the liquid collection channel ( 2503), preventing liquid from flowing into the upstream liquid collection channel (2503), and the device can be activated by completing the flow path between the sample inlet port (2506) and the liquid collection channel (2503), such as by opening a valve when a valve is placed at the interruption of the flow path (2502). When provided by the manufacturer as a new flow cell, the conditioning liquid is filled from the sample inlet port (2506) to interrupt the flow path (2502) in such a way that the sensor (within 2505) is covered through the liquid and is not exposed to a gas or gas / liquid interface. The device additionally features a buffer inlet port (2507) in fluid communication with the detection camera input (2509), an interruption of the flow path (2502), for example, a valve activated by an actionable lever accessible from the upper surface of the device, which is the same as the valve that controls the flow between the outlet of the detection camera and the liquid collection channel), and a removable flat plastic plug that covers the sample inlet port (2506) .

[0149] Before use, the conditioning liquid that fills the flow cell must be replaced with an initiation buffer suitable for operating the device, however the buffer must be inserted in such a way that it does not allow the sensor in the detection chamber ( 2505) contact bubbles or the gas / liquid interface, which would damage the sensor. Thus, the normal method for replacing the plug involves removing the obstruction (s) (2506) from the flow path, also known as activation (which allows liquid to flow through the device), followed by the introduction of the plug through the port. buffer inlet (2507), which displaces the conditioning liquid inside the device. The flat plastic plug is then removed from the inlet port (2506), and initiation buffer is applied through the inlet port which in this way is a continuous fluid channel from the inlet port (2506) through the camera outlet detection (2501) that is ready to receive the sample. Since it is a continuous fluid channel from the inlet port (2506) through the detection camera outlet (2501), the application of one or more volumes of test liquid to the wet surface of the inlet port provides a sufficient liquid conduction to introduce the one or more volumes of the test liquid into the device and move the buffer liquid to the liquid collection channel (2503), allowing the normal operation of the device (for example, flow of nucleic acids in the test through the detection camera).

[0150] In view of the interruptions in the flow path (2506) to be provided as a valve with a lever that can be activated horizontally on the surface of the device, the opening of the valve during the initiation process can be automated, for example, by means of a robotic arm. Removing the flat plug from the sample inlet port is difficult to automate.

[0151] The replacement of the flat plug of the sample inlet port with an alternative plug that is capable of robotic removal, however, allows the opening of the sample inlet port to be opened during an automatic process. In one example, the reciprocating plug is cylindrical, consisting of a first flat end and a second tapered end, where the tapered end tapers to a size sufficient to completely fill and obstruct the sample inlet port. Such a cylindrical plug protrudes a sufficient distance (for example, 1-3 cm, or 1.0, 1.5, 2.0, 2.5, or 3 cm) above the surface of the flow cell when used to seal the sample inlet port through its conical end in such a way that it can be conveniently removed by a robotic arm without touching the surface of the flow cell. Such an exemplary alternative plug is shown in Fig. 26. In some cases, the alternative plug has a shape that is not cylindrical (for example, rectangular, square, triangular), but that projects at least a sufficient distance (for example, 1-3 cm, or 1.0, 1.5, 2.0, 2.5, or 3 cm) above the flow cell surface when used to close the sample inlet port which can be removed without disturbing the surface flow cell and at least taper to a size sufficient to close the sample inlet port. In some embodiments, the alternative plug is constructed of a ferromagnetic material such as ferritic stainless steel, in such a way that the manipulation (displacement and / or removal) of the plug can be performed with an electromagnet. In some embodiments, the alternative plug comprising a metallic material. In some embodiments, the alternate plug comprises tungsten, aluminum, austenitic stainless steel, ferritic stainless steel, or another material that is resistant to diluted nitric acid (HNO3), 1M NaOH, or diluted NaOCl to remove RNA / DNA contamination / RNAse. In some embodiments, the alternative plug comprises polypropylene or polycarbonate.

[0152] Automatic removal of the alternate plug can be incorporated into the process of Example 13 in order to achieve complete automatic initiation of the nanopore pore cell and loading of the sample from one or multiple flow cells simultaneously. An exemplary automated process involving the use of such an alternative plug described above involves first replacing the flat plastic plug of the sample inlet port (the “SpotON” plug shown in Fig. 14) with an implementation of the alternative plug described above (for example, example, manually). The flow cell / valve (2506) path interruptions of the flow cell are opened and the device is placed inside an automated sequencing apparatus as described above, in Example 13, or in Fig.

13. In some embodiments, interruptions in the flow path / valves (2506) of the flow cell are opened by an automated process after the flow cell has been manually placed in the automated sequencing apparatus. The automated sequencing apparatus then provides a starting plug for the plug inlet port (2506), just like the plugs described in Example 13, and after the plug conditioning in the flow cell has been moved, the alternate plug is removed (for example, by a robotic arm) and sample is provided to the sample entry port by means of the automated sequencing apparatus. The sequencing process then proceeds as otherwise described in Example 13 with automatic manipulation and addition of fluid. In some embodiments, the alternate plug is replaced after the sequencing is complete in such a way that the flow cell can be washed and restarted for at least one additional run. In this way, nanopore flow cells can be repeatedly restarted and reused, for example, to process more samples in the same flow cell, or to process sample repetitions where flow sequencing has failed, data recording has failed, or PCR amplification of nucleic acids derived from the food or from the environment sample failed. In some embodiments, the flow cells in the automated sequencing apparatus are restarted and reused at least several times (for example, at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Importantly, the alternate plug provides a way to restart the cell on demand without manual intervention. This is particularly important in the event of sequencing / amplification / data transmission failure, as it allows for automatic repetition of sample processing after hours or at other times / places when the automated sequencing apparatus is not supervised.

Settings and Operation Methods

[0153] Methods for operating an automated sequencing apparatus for detecting foodborne pathogens are described here. Such a system can take many forms. In some respects, the device may be a fixed device with minimal moving parts. In other respects, the device may be a dynamic robotic system with numerous moving parts.

[0154] Regardless of the configuration, a sequencing operation can comprise the steps of sample loading, library generation, library transfer, sequencing and data communication. Each stage can comprise numerous achievements. The methods described here are exemplary and do not restrict the possible mode of operation for any realization of the foodborne pathogen detection system.

[0155] Sample loading can comprise any process for placing a specimen in a library camera. In some ways, a specimen can be placed manually in the library camera. In other respects, a sample can be loaded by an automated system. In some respects, samples can be captured in cartridges that can be loaded into a library camera or an automated sample manipulation system. Sample loading may comprise processes for decontamination of the library cameras from environmental contaminants from the loading process.

[0156] The library generation may comprise a sequence of tests and methods depending on the methodology of the library generation. The assays may include cell culture, cell lysis, DNA amplification, DNA purification, washes, extractions, purifications, dilutions, concentrations, buffer changes, restriction assays, barcode placement and any other biochemical method necessary for generation of a DNA library. In some ways, a single library camera can be used for all processing steps. In other respects, a sample transferred between multiple cameras for each processing step with empty library cameras being subjected to washing procedures in order to remove excess reagents.

[0157] The DNA library sequence can occur in one or more flow cells. A DNA library can be distributed in multiple libraries in order to speed up the processing of a sample. Sequencing may comprise the real-time transmission of data or the staged transmission of data packets. Data processing can take place on one or more onboard processors on the sequencing device, or it can take place on a remote terminal.

[0158] Although many details of the operation can be found in all realizations of the foodborne pathogen detection system, there can be numerous methods for configuring the system to obtain the desired level of performance and accuracy within the permissible scenario. The configuration can be motivated in response to the application of the system. In some respects, the sequencing apparatus can be portable in the field for rapid deployment in difficult environments such as farm fields or restaurant kitchens. Such a device may have a limited feature with space for a few library cameras or sequencing flow cells. In other respects, the device may comprise a laboratory scale fixation with an effectively unrestricted feature. Such an instrument can comprise hundreds to thousands of library cameras and sequencing flow cells with a robotic system for sample management. Below are several exemplary achievements of the device's configurations. Other achievements are possible within the scope of this report. Static Operation

[0159] A foodborne pathogen detection device may comprise a fixed or static device. In such a configuration, library cameras can be positioned permanently in relation to the sequencing flow cells. The connectivity between the library cameras can comprise a piping system, pumps and valves. The fluid flow system must be able to perform all necessary fluid transfer operations during operation without manual intervention. In some respects, one or more library cameras may comprise a sequencing apparatus. Library cameras can be arranged in series or in parallel.

[0160] A sequencing apparatus may comprise a single library camera and one or more sequencing flow cells. In some aspects, the connection between the library camera and the sequencing cell may comprise a flow line and one or more valves. Such an embodiment may comprise the simplest device with the most compact feature.

[0161] Two or more library cameras arranged in parallel may comprise a sequencing apparatus. In some respects, a plurality of library cameras may feature connectivity to a single flow cell. In other respects, each library can be connected to a single flow cell. In some respects, the parallel operation of library cameras may comprise performing all aspects of sample preparation, library generation and generation within a single library camera. After library generation, nucleic acid from a library camera can be transferred to one or more flow sequencing cells. Multiple flow cells can be used for a single DNA library in order to speed up the sequencing process.

[0162] Two or more cameras can also be arranged in a series manner. A serial operation can comprise a staged operation with each specialized library camera to perform a specific operation within the device's methodology. A series arrangement of library cameras may comprise a feature greater than a system comprising a single library camera or library cameras in parallel. A series arrangement of the cameras can comprise a more complicated flow system with additional valves and pumps needed to trigger all of the necessary fluid transfer steps. A series configuration can offer a more efficient operation since each library camera is specifically designed for its function. Transport Operation

[0163] In some respects, a foodborne pathogen detection device may comprise a series of two or more library cameras in a transport system. The carrier can comprise a linear or circular system. A sequencing apparatus may comprise one or more transport systems. Each transport unit can be coupled to one or more sequencing flow cells. The purpose of the transport system is to move a library camera for connectivity to a sequencing cell when the nucleic acid library was prepared. Each library camera in the transport system can have connectivity with the necessary components to carry out the library preparation procedures. When library preparation is complete, the library chamber can be moved into position by the carrier and attached to the sequencing flow cell. By completing the transfer of fluid from the library camera to the sequencing cell, the carrier can move the completed library camera out and replace the flow cell plug or place a new library camera in connectivity with the flow cell. In some respects, the transport system comprises a circular conveyor with four library cameras and two flow cells mounted along an axis. In this configuration, two library cameras feature flow cell connectivity while two library cameras conduct library preparation procedures. When a new library is ready for sequencing, the carrier can rotate 90º to connect the new library cameras, while the cameras previously sequenced being new library preparation. Compartment Operation

[0164] A compartment-type sequencing apparatus may comprise a system of hundreds or thousands of library cameras in one large device. In some respects, a library camera may comprise a cartridge that is loaded with an external specimen for the sequencing apparatus. The cartridge can be transferred to the apparatus and then moved to an docking station comprising one or more connecting ports by a plurality of robotic means of transport. Docking ports can provide all necessary fluid transfer operations in order to complete library preparation inside the library chamber. When library preparation is complete, the cartridge can be transferred to an available sequencing flow cell by a plurality of robotic transport means. In some ways, a cartridge-type library camera can be simultaneously connected to fluid transfer ports and to a sequencing flow cell to create a semi-robotic system with reduced feature.

[0165] Fig. 11 illustrates an automated report compartmentalized sequencing device with a desktop feature. 1101 is a diagram of the apparatus comprising the nucleic acid sequencing compartment 1102. The nucleic acid library preparation compartment 1103 shows a variety of cameras configured to prepare a plurality of nucleic acids for a sequencing reaction near the sequencing camera 1104 , which comprising one or more flow cells. In summary, an automated reporting apparatus is programmed to move one or more samples from library preparation cameras 1103 to a sequencing camera 1104 in detecting a failure in a sequencing reaction. This provides a sequencing process without points of human interference after a sample has been added to the library preparation chamber, as illustrated in Fig. 12. Fig. 12 illustrates an embodiment in which a sample from a food processing facility, from a hospital or clinical facility, or from another source, can be manually processed between 6 am and 6 pm or any shorter or longer incubation window by incubating the sample in the presence of a growth medium (for example, enrichment) and processed automatically after the sample has been added to a 1103 nucleic acid preparation chamber.

[0166] The apparatus described is programmed in such a way that said automated platform moves one or more samples from said nucleic acid library preparation compartment to said nucleic acid sequencing chamber. Upon detecting a failure of a sequencing reaction, the automated platform moves one or more samples from the failing sequencing flow cell or apparatus to the next sequencing flow cell or apparatus. In many cases, such samples comprise nucleic acid sequences that include one or more bar codes. In some cases, a plurality of mutually exclusive bar codes is added to a plurality of nucleic acids in said two or more cameras in the nucleic acid library preparation compartment 1103, thereby providing a plurality of nucleic acids with codes of mutually exclusive bars inside the device. In some cases, the automated platform robotically moves two or more of said mutually exclusive barcode nucleic acids to said nucleic acid sequencing camera, in some cases displacing said mutually exclusive nucleic acid barcodes to the same cell of said one or more flow cells. Classification

[0167] The microbiome data (data representing the presence or absence of particular microbial species or serotypes as determined by sequencing) of the invention can be used to classify a sample. For example, a sample can be classified as, or predicted as: a) containing a particular pathogen, b) containing a particular pathogen microbe serotype, and / or c) contaminated with at least one pathogen microbe species / serotype. Many statistical classification techniques are known to those skilled in the art. In supervised learning approaches, a group of samples from two or more groups (for example,

contaminated with a pathogen and not) are analyzed using a statistical classification method. Data on the presence / absence of the microbe can be used as a classifier that differentiates between the two or more groups. A new sample can then be analyzed in such a way that the classifier can associate the new sample with one of two or more groups. Supervised classifiers commonly used include, without limitation, the neural network (multi-layer perceptron), support vector machines, k-nearest neighbors, mixed model Gaussian, Gaussian, naive Bayes, decision tree and basic radial function (RBF). Linear classification methods include Fisher's linear discriminant, logistic regression, naive Bayes classifieds, perceptron, and support vector machines (SVMs). Other classifiers for use with the invention include quadratic, k-nearest neighbor, amplification classifiers, decision trees, random forests, neural networks, pattern recognition, Bayesian networks and Hidden Markov models. One skilled in the art will observe that these and other classifiers, including improvements to any of these, are included in the scope of the invention.

[0168] Classification using supervised methods is generally carried out by one of the following methodologies:

[0169] In order to solve a given problem of supervised learning (for example, learning to recognize handwriting) it is necessary to consider several steps:

1. Put together a training set. These may include, for example, samples that are from a food or environment contaminated or not contaminated with a particular microbe, samples that are contaminated with different serotypes of the same microbe, samples that are or are not contaminated with a combination of different species and serotypes of microbes, etc. The training samples are used to “train” the classifier.

2. Determine the representation of the input “characteristic” of the learned function. The accuracy of the learned function depends on how the input object is represented. Typically, the input object is transformed into a characteristic vector, which contains a number of characteristics that are descriptive of the object. The number of characteristics should not be very large, considering the dimensionality course; but it must be large enough to accurately predict the outcome. Characteristics should include a set of bacterial species or serotypes present in a derived food or environment sample as described here.

3. Determine the structure of the learned function and corresponding algorithm and learning. A learning algorithm is chosen, for example, artificial neural networks, decision trees, Bayes classifiers or support vector machines. The learning algorithm is used to build the classifier.

4. Build the classifier (for example, classification model). The learning algorithm is run on the attached training set. The parameters of the learning algorithm can be adjusted by optimizing performance in a subset (called a validation set) of the training set, or by means of cross-validation. After parameter setting and learning, the performance of the algorithm can be measured in a test set of virgin samples that is separate from the training set.

[0170] Once the classifier (for example, classification model) has been determined as described above, it can be used to classify a sample, for example, the food or environment sample being analyzed by the methods of the invention.

[0171] Unsupervised learning approaches can also be used with the invention. Agglomeration is an unsupervised learning approach in which an agglomeration algorithm correlates a series of samples without the use of labels. The most similar samples are classified as “clusters”. A new sample can be classified in a cluster and, thus, classified with other members that are more closely associated. Digital Processing Device

[0172] In some respects, the foregoing provides quality control methods or methods for assessing a risk associated with a food, a hospital, a clinic or any other place where the presence of a bacterium represents a certain risk for one or more individuals. In many cases, the systems, platforms, software, networks, and methods described here include a digital processing device, or the use of it. In additional embodiments, the digital processing device includes one or more central processing units (CPUs), that is, processors that perform the functions of the device, such as the automated sequencing apparatus described here or a computer system used in the analysis of a plurality of nucleic acid sequencing readings from samples derived from a food processing facility or any other facility, such as a hospital, clinic or other. In still further embodiments, the digital processing device further comprising an operating system configured to carry out executable instructions. In some embodiments, the digital processing device is connected to a computer network. In additional realizations, the digital processing device is optionally connected to the Internet, in such a way that it accesses the World Wide Web. In still additional realizations, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device. In other embodiments, the digital processing device can be deployed on premise or deployed remotely in the cloud.

[0173] According to the description here, suitable digital processing devices include, as non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet applications,

smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those skilled in the art will recognize that many smartphones are suitable for use in the system described here. Those skilled in the art will also recognize that selected televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described here. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those skilled in the art. In many respects, the report contemplates any suitable digital processing device that can be deployed in a food processing facility, that is, used within said food processing facility to process and analyze a variety of nucleic acids from a variety of samples.

[0174] In some embodiments, a digital processing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, that manage the device's hardware and provide services for running applications. Those skilled in the art will recognize that suitable operating systems include, as non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those skilled in the art will recognize that suitable personal computer operating systems include, as non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those skilled in the art will recognize that suitable mobile smartphone operating systems include, but are not limited to, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS , Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.

[0175] In some embodiments, a digital processing device includes a storage and / or memory device. The storage and / or memory device is one or more physical devices used to store data or programs on a temporary or permanent basis. In some embodiments, the device is a volatile memory and requires energy to keep the information stored. In some embodiments, the device is a non-volatile memory and maintains information stored when the digital processing device is not turned on. In additional embodiments, the non-volatile memory comprises a flash memory. In some embodiments, non-volatile memory comprises random dynamic access memory (DRAM). In some embodiments, non-volatile memory comprises ferroelectric random access memory (FRAM). In some embodiments, the non-volatile memory comprises phase change random access memory (PRAM). In other embodiments, the device is a storage device including, as non-limiting examples, devices for CD-ROMs, DVDs, flash memory, magnetic disk drives, magnetic tape drives, optical disk drives, and computer-based storage in the cloud. In additional embodiments, the storage and / or memory device is a combination of devices such as those described here.

[0176] In some embodiments, a digital processing device includes a display to send visual information to a user. In some embodiments, the display is a cathode ray tube (CRT). In some embodiments, the display is a liquid crystal display (LCD). In additional realizations, the display is a thin-film liquid crystal transistor (TFT-LCD). In some embodiments, the display is an organic light-emitting diode (OLED). In several additional embodiments, the OLED display is either a passive OLED matrix display (PMOLED) or an active OLED matrix (AMOLED). In some embodiments, the display is a plasma display. In other embodiments, the display is a video projector. In still further embodiments, the display is a combination of devices such as those shown here.

[0177] In some embodiments, a digital processing device includes an input device for receiving information from a user. In some embodiments, the input device is a keyboard. In some embodiments, the input device is a pointing device including, as non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or pen. In some embodiments, the input device is either a touch screen or a multiple touch screen. In other embodiments, the input device is a microphone to capture voice or other sound. In other embodiments, the input device is a video camera to capture movement or visual input. In still further embodiments, the input device is a combination of devices such as those described here.

[0178] In some embodiments, a digital processing device includes a digital camera. In some embodiments, a digital camera captures digital images. In some embodiments, the digital camera is an autofocus camera. In some embodiments, a digital camera is a camera with a charge coupling device (CCD). In additional realizations, a digital camera is a CCD video camera. In other embodiments, a digital camera is a camera with a complementary metal oxide semiconductor (CMOS). In some embodiments, a digital camera captures still images. In other embodiments, a digital camera captures video images. In various embodiments, suitable digital cameras include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and more megapixel cameras, including increments of these. In some embodiments, a digital camera is a standard definition camera. In other embodiments, a digital camera is an HD video camera. In additional realizations, an HD video camera captures images at least about 1280 x about 720 pixels or at least about 1920 x about 1080 pixels. In some embodiments, a digital camera captures color digital images. In other embodiments, a digital camera captures digital images in grayscale. In various embodiments, digital images are stored in any suitable digital image format. Suitable digital image formats include, as non-limiting examples, Joint Photographic Experts Group (JPEG), JPEG 2000, interchangeable image file format (Exif), Tagged Image File Format (TIFF), RAW, Portable Web Graphics (PNG), Graphics Interchange Format (GIF), Windows® bitmap (BMP), portable pixmap (PPM), portable graymap (PGM), portable bitmap file format (PBM), and WebP. In various embodiments, digital images are stored in any suitable digital video format. Suitable digital video formats include, but are not limited to, AVI, MPEG, Apple® QuickTime®, MP4, AVCHD®, Windows Media®, DivX ™, Flash Video, Ogg Theora, WebM, and RealMedia. Non-Transient Computer Readable Storage Medium

[0179] In many respects, the systems, platforms, software, networks and methods described here include one or more non-transitory, computer-readable storage media encoded with a program including instructions executable by the operating system of an optionally networked digital processing device. . For example, in some ways, the methods comprise the creation of data files associated with a plurality of sequencing readings from a plurality of samples associated with a food processing facility. In additional embodiments, a computer-readable storage medium is a tangible component of a digital processing device. In still further embodiments, a computer-readable storage medium is optionally removable from a digital processing device. In some embodiments, a computer-readable storage medium includes, as non-limiting examples, CD-ROM devices, DVDs, flash memory, solid-state memory, magnetic disk drives, magnetic tape drives, optical disk drives, systems cloud computing and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently or not transiently encoded in the media. Computer program

[0180] In some embodiments, the systems, platforms, software, networks, and methods described here include at least one computer program. A computer program includes a sequence of instructions, executable on the CPU of the digital processing device, written to perform a specific task. In the light of the report provided here, those skilled in the art will recognize that a computer program can be written in several versions of various languages. In some embodiments, a computer program comprises a sequence of instructions. In some embodiments, a computer program comprises a plurality of instruction sequences. In some embodiments, a computer program is provided with a location. In other embodiments, a computer program is provided with a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in full, one or more network applications, one or more mobile applications, one or more stand-alone applications, one or more network browser plug-ins, extensions, add- ins or add-ons, or combinations of these. Network Application

[0181] In some embodiments, a computer program includes a network application. In the light of the report provided here, those skilled in the art will recognize that a network application, in various embodiments, uses one or more software structures and one or more database systems. In some embodiments, a network application is created in a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a network application uses one or more database systems including, as non-limiting examples, relational, non-relational, object-oriented, associative and XML database systems. In additional realizations, suitable relational database systems include, as non-limiting examples, Microsoft® SQL Server, mySQL ™ and Oracle®. Those skilled in the art will recognize that a network application, in various embodiments, is written in one or more versions of one or more languages. A network application can be written in one or more markup languages, presentation definition languages, client-oriented scripting languages, server-oriented coding languages,

database query languages or combinations thereof. In some embodiments, a network application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or eXtensible Markup Language (XML). In some embodiments, a network application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a network application is written to some extent in a client-oriented scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, a network application is written to some extent in a server-oriented coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java ™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python ™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, a network application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a network application integrates such enterprise server products as IBM® Lotus Domino®. A network application to provide a career development network for artists that allows artists to upload information and media files, in some achievements, includes a media player element. In a number of additional realizations, a media player element uses one or more of many suitable multimedia technologies including, but not limited to, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java ™, and Unity ®. Mobile App

[0182] In some embodiments, a computer program includes a mobile application provided for a mobile digital processing device. In some embodiments, the mobile application is provided for a mobile digital processing device at the time of manufacture. In other embodiments, the mobile application is provided for a mobile digital processing device via the computer network described here.

[0183] In view of the report provided here, a mobile application is created by techniques known to those skilled in the art using hardware, languages, and development environments known in the art. Technicians in the field will recognize that mobile apps are written in several languages. Suitable programming languages include, as non-limiting examples, C, C ++, C #, Objective-C, Java ™, Javascript, Pascal, Object Pascal, Python ™, Ruby, VB.NET, WML, and XHTML / HTML with or without CSS , or combinations of these.

[0184] Suitable mobile app development environments are available from a variety of sources. Commercially available development environments include, as non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments available at no cost include, as non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute developer software kits including, but not limited to, the iPhone and iPad (iOS) SDK, Android ™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

[0185] Technicians in the field will recognize that several commercial forums are available for mobile application distribution including, as non-limiting examples, Apple® App Store, Android ™ Market, BlackBerry® App World, App Store for Palm, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia®, Samsung® Apps, and Nintendo® DSi Shop. Standalone Applications

[0186] In some embodiments, a computer program includes a stand-alone application, which is a program that is run in an independent computer process, not an add-on to an existing process, for example, not a plug-in. Those skilled in the art will recognize that stand-alone applications are often compiled. A compiler is a computer program that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, as non-limiting examples, C, C ++, Objective-C, COBOL, Delphi, Eiffel, Java ™, Lisp, Python ™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often done, at least in part, to create an executable program. In some embodiments, a computer program includes one or more compiled executable applications. Software Modules

[0187] The systems, platforms, software, networks, and methods described here include, in various embodiments, software, server, and database modules. In view of the report provided here, software modules are created by techniques known to those skilled in the art using machines, software, and languages known in the art. The software modules described here are implemented in several ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In several additional embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, as non-limiting examples, a network application, a mobile application, and a stand-alone application. In some embodiments, the software modules are in a computer program or application. In other embodiments, the software modules are in more than one computer program or application. In some embodiments, the software modules are hosted on a machine. In other embodiments, the software modules are hosted on more than one machine. In additional realizations, the software modules are hosted on cloud computing platforms. In some embodiments, the software modules are hosted on one or more machines in one location. In other embodiments, the software modules are hosted on one or more machines in more than one location.

ACHIEVEMENTS

[0188] ACHIEVEMENT 1. An accomplishment comprising: (a) implantation of a test for one or more food processing facilities; (b) carrying out a sequencing reaction on a food sample or a sample from the environment of said one or more food processing facilities; (c) transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample from said one or more food processing facilities to a server; and (d) scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with a microorganism.

[0189] REALIZATION 2. The realization method 1, wherein said scanning scans less than 0.001%, 0.01%, 0.1%, 1% of said data set transmitted to one or more genes associated with said microorganism .

[0190] REALIZATION 3. The realization method 1, wherein said sequencing reaction is a pore sequencing reaction, a sequencing reaction of the next generation, a shotgun sequencing of the next generation or Sanger sequencing.

[0191] REALIZATION 4. The realization method 3, where said sequencing reaction is a pore sequencing reaction.

[0192] REALIZATION 5. The realization method 4, where the said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0193] REALIZATION 6. The realization method 5, where said epigenetic pattern is a methylation pattern.

[0194] REALIZATION 7. The realization method 1, where said microorganism is pre-selected by a client.

[0195] REALIZATION 8. The realization method 1, additionally comprising scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with two or more microorganisms.

[0196] REALIZATION 9. The realization method 1, where said microorganism is selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria, and a microorganism of the genus Escherichia.

[0197] REALIZATION 10. The realization method 1, where said food sample is perishable item.

[0198] REALIZATION 11. The realization method 10, where said product is a perishable item is meat.

[0199] REALIZATION 12. The realization method 11, where said meat is a bird, a red meat, a fish or a pig.

[0200] REALIZATION 13. The realization method 8, where said product is perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0201] REALIZATION 14. The realization method 1, wherein said environment sample is a surface mop or surface rinse of said one or more food processing facilities.

[0202] REALIZATION 15. The realization method 1, wherein said environment sample is a food storage container, a food handling equipment from said one or more food processing facilities, or a piece of clothing from one one or more food processing facilities.

[0203] REALIZATION 16. The method of realization 1, further comprising comprising the amplification or enrichment of one or more nucleic acids of said food sample or said environment sample before carrying out said sequencing reaction.

[0204] REALIZATION 17. The method of realization 1, further comprising adding at least one bar code to one or more nucleic acids of said food sample or said environment sample before carrying out said sequencing reaction.

[0205] REALIZATION 18. The realization method 17, additionally comprising the creation, on a computer, of a data file that associates said at least one bar code with a source of said food sample or said environment sample .

[0206] REALIZATION 19. The realization method 17, further comprising adding a plurality of mutually exclusive bar codes to a plurality of food processing facilities.

[0207] REALIZATION 20. The realization method 1, wherein said scanning comprises scanning said data set transmitted to one or more polymorphic genetic regions.

[0208] REALIZATION 21. The realization method 20, wherein said one or more polymorphic regions comprise one or more single nucleotide polymorphisms (SNP's), one or more restriction fragment length polymorphisms (RFLP's), one or more repetitions short tandem (STRs), one or more variable numbers of tandem repetitions (VNTR's), one or more hypervariable regions, one or more minisatellites, one or more dinucleotide repetitions, one or more trinucleotide repetitions, one or more repetitions of tetranucleotide, one or more repetitions of simple sequence, one or more indelible, or one or more insertion elements.

[0209] REALIZATION 22. The realization method 20, where one or more polymorphic regions comprise one or more single nucleotide polymorphisms (SNPs).

[0210] REALIZATION 23. The realization method 1, where said sequencing reaction differentiates a living microorganism from a dead microorganism.

[0211] REALIZATION 24. The realization method 1, where said sequencing reaction differentiates a resident microorganism in comparison with a transient microorganism.

[0212] REALIZATION 25. The realization method 1, where said method distinguishes a microorganism from an Escherichia genus from a microorganism from a Citrobacter genus or a Shiga Toxin-producing E. coli (STEC) from a non-STEC E. coli.

[0213] ACHIEVEMENT 26. An accomplishment comprising: (a) implantation of a test for one or more food processing facilities; (b) carrying out a sequencing reaction on a food sample or a sample from the environment of said one or more food processing facilities; (c) transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample from said one or more food processing facilities to a server; and (d) scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with a microorganism. Copy language of the published claim.

[0214] ACHIEVEMENT 27. An accomplishment comprising: (a) implantation of a test for one or more food processing facilities; (b) carrying out a sequencing reaction on a food sample or a sample from the environment of said one or more food processing facilities, wherein said sample comprises a target nucleic acid comprising a periodic or a non-periodic bar code ; (c) transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample from said one or more food processing facilities to a server; and (d) scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with a microorganism, where said fraction is compared to a data set of a substantially complete sequencing reaction , wherein said fraction of said transmitted data set comprises a number of sequencing readings or a number of sequenced nucleotide bases.

[0215] REALIZATION 28. An embodiment comprising: (a) obtaining a plurality of nucleic acid sequences from a sample; (b) scanning, by a computer, of at least a fraction of said plurality of said nucleic acid sequences to a plurality of nucleic acid regions of one or more microorganisms selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria and a microorganism of the genus Escherichia, where said scanning characterizes said one or more microorganisms with sensitivity above 99.5%.

[0216] REALIZATION 29. The realization method 28, wherein said sample is a food sample or a sample of the environment associated with said food sample.

[0217] REALIZATION 30. The realization method 28, where said sample is not a food sample.

[0218] REALIZATION 31. The realization method 28, wherein said sample comprises blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid.

[0219] REALIZATION 32. The realization method 28, where said scanning characterizes said one or more microorganisms with sensitivity above 99%.

[0220] REALIZATION 33. Realization method 32, where said scanning characterizes said or more microorganisms with sensitivity above 99.9%, 99.99%, or 99.999%.

[0221] REALIZATION 34. The realization method 33, where said scanning characterizes said one or more microorganisms with specificity above 99.5%.

[0222] REALIZATION 35. Realization method 34, where said scanning characterizes said one or more microorganisms with specificity above 99%.

[0223] REALIZATION 36. Realization method 35, where said scanning characterizes said one or more microorganisms with specificity above 99.9%, 99.99%, or 99.999%.

[0224] ACHIEVEMENT 37. The method of accomplishment 28, where said scanning characterizes said one or more microorganisms with sensitivity above 99.5% and specificity above 99%.

[0225] REALIZATION 38. The realization method 28, where a scan of no more than 0.001%, 0.01%, 0.1%, or 1% of the nucleic acid regions in said plurality of nucleic acid sequences characterizes the said one or more microorganisms with sensitivity above 99.5%.

[0226] REALIZATION 39. The realization method 38, where a scan of no more than 0.001%, 0.01%, 0.1%, or 1% of the nucleic acid regions in said plurality of nucleic acid sequences characterizes the said one or more microorganisms with sensitivity above 99.9%.

[0227] REALIZATION 40. The realization method 28, where a scan of no more than 0.001%, 0.01%, 0.1%, or 1% of the nucleic acid regions in said plurality of nucleic acid sequences characterizes the said one or more microorganisms with specificity above 99.5%.

[0228] REALIZATION 41. The realization method 40, where a scan of no more than 0.001%, 0.01%, 0.1%, or 1% of the nucleic acid regions in said plurality of nucleic acid sequences characterizes the said one or more microorganisms with specificity above 99.9%.

[0229] REALIZATION 42. The realization method 41, where said method presents less than 0.1% of a false positive identification rate.

[0230] REALIZATION 43. The realization method 28, wherein said plurality of nucleic acid sequences comprises complementary DNA (cDNA) sequences.

[0231] Embodiment 44. Embodiment 28, wherein said plurality of nucleic acid sequences comprises ribonucleic acid (RNA) sequences.

[0232] REALIZATION 45. The realization method 28, wherein said plurality of nucleic acid sequences comprises deoxyribonucleic acid sequences (gDNA).

[0233] REALIZATION 46. The method of realization 28, wherein said plurality of nucleic acid sequences comprises a mixture of cDNA, RNA, and gDNA sequences.

[0234] REALIZATION 47. The realization method 28, wherein said scanning comprises scanning said plurality of said nucleic acid sequences for one or more polymorphic genetic regions associated with said microorganisms.

[0235] REALIZATION 48. The realization method 47, wherein said one or more polymorphic regions comprise a gene coding region associated with said microorganisms.

[0236] REALIZATION 49. The realization method 47, wherein said one or more polymorphic regions comprise a regulatory region associated with said microorganisms.

[0237] REALIZATION 50. The realization method 47, wherein said one or more polymorphic regions are selected from the group consisting of one or more single nucleotide polymorphisms (SNPs), one or more restriction fragment length polymorphisms (RFLPs) , one or more short tandem repeats (STRs), one or more variable numbers of tandem repeats (VNTRs), one or more hypervariable regions, one or more minisatellites, one or more dinucleotide repeats, one or more trinucleotide repeats, one or more repetitions of tetranucleotide, one or more repetitions of simple sequence, one or more insertion elements, or one or more epigenetic modifications.

[0238] REALIZATION 51. The method of realization 28, wherein said obtaining said plurality of nucleic acid sequences comprises sequencing or hybridizing said plurality of nucleic acid sequences.

[0239] REALIZATION 52. Realization method 51, where said sequencing reaction is a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing.

[0240] REALIZATION 53. The realization method 52, wherein said sequencing reaction is a pore sequencing reaction.

[0241] REALIZATION 54. The realization method 53, wherein said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0242] REALIZATION 55. The realization method 54, where said epigenetic pattern is a methylation pattern.

[0243] REALIZATION 56. Realization method 28, in which said microorganism of the genus Salmonella presents a serotype selected from the group consisting of: Enteritidis, Typhimurium, Newport, Javiana, Children, Montevideo, Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup, Paratyphi B var. L (+) Tartarato +, Agona, Thompson, and Kentucky.

[0244] REALIZATION 57. The realization method 56, where said microorganism of the genus Salmonella is of the serotype Enteritidis.

[0245] REALIZATION 58. The realization method 56, where said microorganism of the genus Salmonella is of the Typhimurium serotype.

[0246] REALIZATION 59. Realization method 56, where said microorganism of the genus Salmonella is of the Newport serotype.

[0247] REALIZATION 60. Realization method 56, where the said microorganism of the genus Salmonella is of the Javiana serotype.

[0248] REALIZATION 61. Realization method 56, where said microorganism of the genus Escherichia presents a serotype selected from the group consisting of: O103, O111, O121, O145, O26, O45, and O157.

[0249] REALIZATION 62. The realization method 61, where said microorganism of the genus Escherichia is E. coli O157: H7.

[0250] REALIZATION 63. The realization method 28, where said scanning distinguishes said microorganism from the genus Escherichia from a microorganism from the genus Citrobacter.

[0251] REALIZATION 64. The realization method 28, where said microorganism of the genus Listeria presents a serotype selected from the group consisting of: 2a, 1 / 2b, 1 / 2c, 3a, 3b, 3c, 4a, 4b, 4ab, 4c, 4d, and 4e.

[0252] REALIZATION 65. The realization method 28, where said microorganism of the genus Campylobacter and C. jejuni. C. lari, and C. coli.

[0253] REALIZATION 66. An embodiment comprising: (a) sequencing a plurality of nucleic acid sequences from a food sample or from a sample of the environment associated with said food sample over a period of time; and (b) performing a test on said food sample or in said environment associated with said food sample if said sequencing for said period of time identifies a limit level of nucleic acid sequences of a microorganism in said food sample .

[0254] ACHIEVEMENT 67. The method of realization 66, where said time period is less than 30 minutes.

[0255] ACHIEVEMENT 68. The method of realization 66, where said time period is less than 20 minutes.

[0256] REALIZATION 69. The realization method 66, wherein said limit is not greater than 0.001%, 0.01%, 0.1%, or 1%, of nucleic acid sequences of said microorganism.

[0257] REALIZATION 70. The realization method 66, further comprising carrying out an amplification reaction on said plurality of nucleic acid sequences before sequencing.

[0258] REALIZATION 71. The realization method 66, where said sequencing is a pore sequencing reaction.

[0259] REALIZATION 72. Realization method 66, where said assay is a serotyping assay, a culture assay, a pulsed field gel electrophoresis assay (PFGE), a RiboPrinter® assay, a q-PCR assay, a Sanger sequencing assay, an ELISA assay, a Total Genome Sequencing (WGS) assay, a targeted sequencing assay, or a shotgun metagenomic assay.

[0260] REALIZATION 73. The realization method 66, where said microorganism is selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria and a microorganism of the genus Escherichia.

[0261] REALIZATION 74. Realization method 73, where said microorganism of the genus Salmonella presents a serotype selected from the group consisting of: Enteritidis, Typhimurium, Newport, Javiana, Children, Montevideo, Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup, Paratyphi B var. L (+) Tartarato +, Agona, Thompson, and Kentucky.

[0262] REALIZATION 75. Realization method 73, where said microorganism of the genus Escherichia presents a serotype selected from the group consisting of: O103, O111, O121, O145, O26, O45, and O157.

[0263] REALIZATION 76. The realization method 73, where said microorganism of the genus Escherichia is E. coli O157: H7.

[0264] REALIZATION 77. The realization method 73, where said microorganism of the genus Listeria presents a serotype selected from the group consisting of: 2a, 1 / 2b, 1 / 2c, 3a, 3b, 3c, 4a, 4b, 4ab, 4c, 4d, and 4e.

[0265] REALIZATION 78. The realization method 73, where said microorganism of the genus Campylobacter is C. jejunis, C. lari, or C. coli.

[0266] REALIZATION 79. The realization method 66, wherein said plurality of nucleic acid sequences comprises complementary DNA (cDNA) sequences.

[0267] REALIZATION 80. The realization method 66, wherein said plurality of nucleic acid sequences comprises ribonucleic acid (RNA) sequences.

[0268] REALIZATION 81. The realization method 66, wherein said plurality of nucleic acid sequences comprises genomic deoxyribonucleic acid (gDNA).

[0269] REALIZATION 82. The realization method 66, wherein said plurality of nucleic acid sequences comprises a mixture of cDNA, RNA, and gDNA sequences.

[0270] REALIZATION 83. The realization method 66, where said food sample is a perishable item.

[0271] REALIZATION 84. The realization method 83, where said perishable item is meat.

[0272] REALIZATION 85. The realization method 84, where said meat is a bird, a red meat, a fish or a pig.

[0273] REALIZATION 86. The realization method 83, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0274] REALIZATION 87. Realization method 66, wherein said environment sample is a surface mop or surface rinse of said environment.

[0275] REALIZATION 88. Realization method 66, wherein said environment sample is a food storage container, food handling equipment, or a piece of clothing from a worker in said environment associated with said sample of food.

[0276] REALIZATION 89. An embodiment comprising: (a) obtaining a first plurality of nucleic acid sequences from a first sample of a food processing facility; (b) creating a data file on a computer that associates one or more of said first plurality of nucleic acid sequences with said food processing facility; (c) obtaining a second plurality of nucleic acid sequences from a second sample of said food processing facility; and (d) scanning a plurality of sequences from said second plurality of nucleic acid sequences to one or more sequences associated with said food processing facility in (b).

[0277] REALIZATION 90. The realization method 89, where said data file associates a strain of said microorganism with said food processing facility.

[0278] REALIZATION 91. The realization method 89, wherein said first sample, said second sample, or both comprise a plurality of sequences from a plurality of microorganisms.

[0279] REALIZATION 92. The realization method 89, where at least one of the said plurality of microorganisms is a non-pathogenic microorganism.

[0280] REALIZATION 93. The realization method 89, where at least one of the said plurality of microorganisms is a pathogenic microorganism.

[0281] REALIZATION 94. The realization method 93, where said pathogenic microorganism is selected from the group consisting of a gram-negative bacterium, a gram-positive bacterium, a protozoan, a virus, and a fungus.

[0282] REALIZATION 95. The realization method 94, where said gram-negative bacteria is a Salmonella bacterium.

[0283] REALIZATION 96. The realization method 94, where said gram-negative bacterium is an Escherichia bacterium.

[0284] REALIZATION 97. The realization method 94, where said gram-positive bacterium is a Listeria bacterium.

[0285] REALIZATION 98. The realization method 94, where said gram-negative bacterium is a Campylobacter bacterium.

[0286] CARRYING OUT 99. The method of carrying out 89, further comprising obtaining a third plurality of nucleic acid sequences from an additional sample of said food processing facility.

[0287] REALIZATION 100. The realization method 89, where said first sample, said second sample, or both is a perishable item.

[0288] REALIZATION 101. The realization method 100, where said perishable item is a meat.

[0289] REALIZATION 102. The realization method 100, where said meat is a bird, a red meat, a fish, or a pig.

[0290] REALIZATION 103. The realization method 101, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0291] REALIZATION 104. The realization method 89, wherein said first sample, said second sample, or both is a surface mop or a surface rinse of said environment.

[0292] ACHIEVEMENT 105. The method of embodiment 89, wherein said first sample, said second sample, or both is a food storage container, food handling equipment, or a piece of clothing from said worker environment associated with said food sample.

[0293] REALIZATION 106. The realization method 89, where at least one bar code is added to said first plurality of nucleic acid sequences, said second plurality of nucleic acid sequences or both.

[0294] REALIZATION 107. Realization method 106, wherein said at least one bar code is associated with said data file of (b), thus associating said at least one bar code with said processing facility of food.

[0295] CARRYING OUT 108. The method of carrying out 89, wherein obtaining said first plurality of nucleic acid sequences, said second plurality of nucleic acid sequences, or both comprising carrying out a sequencing reaction or a hybridization assay .

[0296] REALIZATION 109. The realization method 105, wherein said sequencing reaction is a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing.

[0297] REALIZATION 110. The realization method 109, wherein said sequencing reaction is a pore sequencing reaction.

[0298] REALIZATION 111. The realization method 110, wherein said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0299] REALIZATION 112. Realization method 110, where said epigenetic pattern is a methylation pattern.

[0300] ACHIEVEMENT 113. An embodiment comprising: (a) obtaining a first sample from a food processing facility; (b) sequencing said first sample from said food processing facility, thereby generating a first set of sequencing data from said food processing facility; (c) obtaining a second sample from said food processing facility; (d) sequencing said second sample from said food processing facility, thereby generating a second set of sequencing data from said food processing facility; and (e) comparing said second set of sequencing data with said first set of sequencing data; and (d) decontaminating said food processing facility if said comparison identifies a pathogenic microorganism in said food processing facility.

[0301] REALIZATION 114. An embodiment comprising (a) obtaining a first plurality of nucleic acid sequences from a first sample of a food processing facility; (b) obtaining a second plurality of nucleic acid sequences from a second food sample from said food processing facility; and (c) carrying out sequence alignments on a computer between said first plurality of nucleic acid sequences and said second plurality of nucleic acid sequences, thereby determining a similarity between said first sample and said second sample of said food processing facility.

[0302] REALIZATION 115. An embodiment comprising: (a) adding a reagent to a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said food sample,

thus forming a plurality of modified nucleic acid molecules, whereby said reagent (i) modifies a structure of or interacts with a plurality of nucleic acid molecules derived from one or more dead microorganisms; and (ii) does not modify a structure of a nucleic acid molecule derived from one or more living microorganisms; thereby providing a plurality of modified nucleic acid molecules; and (b) sequencing by a sequencing reaction of said plurality of modified nucleic acid molecules, thereby distinguishing one or more living organisms from said food sample or said environment sample associated with said food sample.

[0303] REALIZATION 116. The realization method 113, wherein said sequencing reaction comprises pore sequencing.

[0304] REALIZATION 117. The realization method 113, wherein said food sample is stressed, shocked or processed before adding said reagent to said plurality of nucleic acid molecules.

[0305] CARRYING OUT 118. Carrying out method 113, further comprising incubating said food sample in a growth medium prior to carrying out said sequencing reaction.

[0306] REALIZATION 119. The realization method 113, wherein said reagent is a photoreactive DNA-binding dye.

[0307] REALIZATION 120. The realization method 119, wherein said photoreactive DNA binding dye is monoazide propidium or a derivative thereof.

[0308] REALIZATION 121. The realization method 113, wherein said reagent is a DNA intercalation reagent.

[0309] PERFORMANCE 122. The method of embodiment 113, further comprising carrying out an amplification reaction prior to sequencing said plurality of modified nucleic acid molecules.

[0310] REALIZATION 123. The realization method 113, where said food sample is a perishable item.

[0311] REALIZATION 124. The realization method 123, where said perishable item is meat.

[0312] REALIZATION 125. The realization method 124, where said meat is a bird, a red meat, a fish, or a pig.

[0313] REALIZATION 126. The realization method 123, where said perishable item is a fruit, an egg, a vegetable or a vegetable.

[0314] REALIZATION 127. Realization method 115, wherein said environment sample is a surface mop or surface rinse of said environment.

[0315] REALIZATION 128. The realization method 115, wherein said environment sample is a food storage container, food handling equipment, or a piece of clothing of a worker from said environment associated with said sample of food.

[0316] REALIZATION 129. An embodiment comprising carrying out a pore sequencing reaction on a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said food sample, whereby said reaction pore sequencing system distinguishes one or more nucleic acid molecules derived from a dead microorganism from one or more nucleic acid molecules derived from a living microorganism based on a methylation pattern or another epigenetic pattern from said one or more acid molecules nucleic derivatives of said dead microorganism.

[0317] REALIZATION 130. The realization method 129, wherein said pore sequencing reaction is a nanopore sequencing reaction.

[0318] ACHIEVEMENT 131. A Method characterized by the fact that it comprises: (a) obtaining a plurality of nucleic acid sequences from a food sample or a sample from the environment of a food processing facility; (b) carrying out a first assay on said plurality of nucleic acid sequences of said food sample, whereby said assay provides for a presence or foresees an absence of a microorganism in said food sample; and (c) determining, based on said predicted presence or said predicted absence of said microorganism, (b) whether a second test is necessary, so the sensitivity of said second test is selected to determine a gender, a species, a serotype, a sub-serotype, or a strain of said microorganism.

[0319] REALIZATION 132. The realization method 131, where said first trial and said second trial are identical.

[0320] REALIZATION 133. The realization method 131, where said first test and said second test have different sensitivities.

[0321] REALIZATION 134. The realization method 131, wherein said first trial, said second trial or both comprise a sequencing trial.

[0322] REALIZATION 135. The realization method 134, wherein said sequencing test comprises a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing.

[0323] REALIZATION 136. The realization method 134, wherein said sequencing reaction is a pore sequencing reaction.

[0324] REALIZATION 137. The realization method 134, wherein said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0325] REALIZATION 138. The realization method 137, where said epigenetic pattern is a methylation pattern.

[0326] REALIZATION 139. The realization method 131, wherein said first assay, said second assay, or both comprise a polymerase chain reaction (PCR) assay.

[0327] Embodiment 140. Embodiment 131, wherein said first assay, said second assay, or both comprise an enzyme-linked immunosorbent assay (ELISA).

[0328] REALIZATION 141. The realization method 131, wherein said first assay, said second assay, or both comprise an enzyme-linked fluorescent assay (ELFA).

[0329] REALIZATION 142. The realization method 131, wherein said first trial, said second trial, or both comprise a serotyping trial.

[0330] REALIZATION 143. The realization method 131, where said microorganism is selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria, a microorganism of the genus Escherichia, the virus, a parasite, and a fungus.

[0331] REALIZATION 144. The realization method 131, where said food sample is a perishable item.

[0332] REALIZATION 145. Realization method 144, where said perishable item is meat.

[0333] REALIZATION 146. Realization method 145, where said meat is a bird, red meat, fish, or pig.

[0334] REALIZATION 147. The realization method 144, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0335] REALIZATION 148. The realization method 144, wherein said environment sample is a surface mop or surface rinse of said environment.

[0336] REALIZATION 149. The realization method 144, wherein said environment sample is a food storage container, food handling equipment, or a piece of clothing of a worker from said environment associated with said installation of food processing.

[0337] REALIZATION 150. The realization method 131, where said realization of said first assay and said realization of said second assay provide for said presence or foresee said absence of said microorganism with sensitivity above 90%, 95%, 98%, 99%, 99.9%, 99.99% or above 99.999%.

[0338] REALIZATION 151. An realization comprising: (a) detecting the presence or absence of a non-pathogenic microorganism in a sample; (b) prediction, by a computer system, of the presence or absence of a pathogenic microorganism in said sample based on said presence or said absence of said non-pathogenic microorganism.

[0339] ACHIEVEMENT 152. The realization method 151, where said prediction is performed by a machine learning algorithm on a computer.

[0340] ACHIEVEMENT 153. The realization method 152, where said machine learning algorithm is selected from the group consisting of: a support vector machine (SVM), a Naïve Bayes classification, a random forest, Logistic Regression, and a neural network.

[0341] REALIZATION 154. The realization method 152, wherein said sample is a food sample or a sample of the environment associated with said food sample.

[0342] REALIZATION 155. The realization method 154, where said food sample is a perishable item.

[0343] REALIZATION 156. Realization method 155, where said perishable item is meat.

[0344] REALIZATION 157. The realization method 156, where said meat is a bird, red meat, fish, or pig.

[0345] REALIZATION 158. Realization method 155, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0346] REALIZATION 159. Realization method 154, wherein said environment sample is a surface mop or surface rinse of said environment.

[0347] REALIZATION 160. The realization method 154, wherein said environment sample is a food storage container, food handling equipment, or a piece of clothing of a worker from said environment associated with said installation of food processing.

[0348] REALIZATION 161. The realization method 151, where said sample is not a food sample.

[0349] REALIZATION 162. The realization method 151, wherein said sample comprises blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid.

[0350] REALIZATION 163. The realization method 151, where said microorganism is non-pathogenic.

[0351] REALIZATION 164. The realization method 151, wherein said non-pathogenic microorganism is selected from the group consisting of: Enterobacter asburiae, Enterobacter bugandensis, Enterobacter cancerogenus, Enterobacter cloacae, Enterobacter endosymbiont, Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii mori, and Enterobacter soli.

[0352] REALIZATION 165. The realization method 151, where said pathogenic microorganism is selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria and a microorganism of the genus Escherichia.

[0353] REALIZATION 166. The realization method 151, wherein said pathogenic microorganism is selected from the group consisting of Vibrio parahaemolyticus, Vibrio cholera, Vibrio vulnificus, Escherichia coli, Salmonella enterica, Shigella boydii, Campylobacter jejuni, Staphylococcus aureus, Listeria monocyyy Clostridium botulinum, Yersinia pseudotuberculosis, Clostridium perfringens, Yersinia enterocolitica, Coxiella burnetii, Yersinia pseudotuberculosis, Vibrio parahaemolyticus, Bacillus cereus, Mycobacterium tuberculosis, Shigella flexneri, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia, Shigella dysentia.

[0354] REALIZATION 167. The realization method 151, where said detection comprises a nucleic acid characterization assay selected from the group consisting of a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing , Sanger sequencing, or hybridization assay.

[0355] REALIZATION 168. The realization method 167, said nucleic acid characterization assay is a pore sequencing reaction.

[0356] REALIZATION 169. The realization method 168, wherein said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0357] REALIZATION 170. The realization method 151, additionally comprising carrying out a test to confirm the prediction of (b).

[0358] REALIZATION 171. The realization method 170, where said test is a serotyping reaction.

[0359] REALIZATION 172. The realization method 170, wherein said assay is a polymerase chain reaction (PCR).

[0360] REALIZATION 173. The realization method 172, wherein said assay is an enzyme-linked immunosorbent assay (ELISA).

[0361] REALIZATION 174. The realization method 172, wherein said assay is an enzyme fluorescent assay (ELFA).

[0362] REALIZATION 175. An realization comprising: (a) detecting the presence or absence of a microorganism in a sample or in an installation associated with said sample; and (b) prediction, by a computer system, of a risk presenting by said installation based on said presence or said absence of said microorganism.

[0363] REALIZATION 176. The realization method 175, wherein said sample is a food sample or a sample of the environment associated with said food sample.

[0364] REALIZATION 177. Realization method 176, where said food sample is a perishable item.

[0365] REALIZATION 178. Realization method 177, where said perishable item is meat.

[0366] REALIZATION 179. The realization method 178, where said meat is a bird, red meat, fish or pig.

[0367] REALIZATION 180. The realization method 177, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0368] REALIZATION 181. The realization method 176, wherein said environment sample is a surface mop or a surface rinse of said environment.

[0369] REALIZATION 182. The realization method 176, wherein said environment sample is a food storage container, food handling equipment, or a piece of clothing of a worker from said environment associated with said installation of food processing.

[0370] REALIZATION 183. Realization method 175, where said sample is not a food sample.

[0371] REALIZATION 184. The realization method 175, wherein said sample comprises blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid.

[0372] REALIZATION 185. Realization method 175, where said installation is a food processing installation.

[0373] REALIZATION 186. Realization method 175, where said facility is a hospital or clinic.

[0374] REALIZATION 187. Realization method 175, where said method provides for the said presence or the said absence of said microorganism with sensitivity above 90%, 95%, 98%, 99%, 99.9%, 99, 99% or 99.999%.

[0375] REALIZATION 188. Realization method 175, where said method provides for said presence or said absence of said microorganism with specificity above 90%, 95%, 98%, 99%, 99.9%, 99, 99% or 99.999%.

[0376] REALIZATION 189. Realization method 175, where said risk informs security for said installation.

[0377] REALIZATION 190. The realization method 175, where said microorganism is a pathogenic or a non-pathogenic microorganism.

[0378] REALIZATION 191. The realization method 175, wherein said detection comprises a sequencing reaction or a hybridization assay.

[0379] REALIZATION 192. The realization method 191, where said sequencing reaction is selected from the group consisting of a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, Sanger sequencing.

[0380] REALIZATION 193. The realization method 192, where said sequencing reaction is a pore sequencing reaction.

[0381] REALIZATION 194. The realization method 193, where said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0382] REALIZATION 195. The realization method 175, further comprising carrying out a test to confirm the prediction of (b).

[0383] REALIZATION 196. The realization method 195, where said test is a serotyping reaction.

[0384] REALIZATION 197. The realization method 195, wherein said assay is a polymerase chain reaction (PCR) assay.

[0385] REALIZATION 198. The realization method 195, wherein said assay is an enzyme-linked immunosorbent assay (ELISA).

[0386] REALIZATION 199. The realization method 195, wherein said assay is an enzyme-linked fluorescent assay (ELFA).

[0387] REALIZATION 200. An embodiment comprising: (a) the addition of a first barcode to a first plurality of nucleic acid sequences in a sample, thereby providing a first plurality of nucleic acid sequences with codes of bars; and (b) carrying out a first sequencing reaction on said first plurality of barcode nucleic acid sequences, wherein said sequencing reaction is carried out in a sequencing apparatus comprising a flow cell; (c) adding a second barcode to a second plurality of nucleic acid sequences from a second sample, thereby providing

a second plurality of barcode nucleic acid sequences; and (d) carrying out a second sequencing reaction on said second plurality of barcode nucleic acid sequences, wherein said second sequencing reaction is carried out on said sequencing apparatus comprising said flow cell, thereby reusing the said flow cell.

[0388] REALIZATION 201. The realization method 200, wherein said first barcode and said second barcode are between 1 nucleotide and 18 nucleotides in length.

[0389] REALIZATION 202. The realization method 200, wherein said first barcode and said second barcode are about 9 nucleotides in length.

[0390] REALIZATION 203. The realization method 200, wherein said first barcode and said second barcode have identical sequences.

[0391] REALIZATION 204. The realization method 200, wherein said first barcode and said second barcode have different sequences.

[0392] REALIZATION 205. The method of realization 200, further comprising adding a third barcode to a third plurality of nucleic acid sequences from a third food sample or a third environment sample associated with said third sample of food, thus providing a third plurality of nucleic acid sequences with bar codes.

[0393] REALIZATION 206. The method of realization 205, further comprising carrying out a third sequencing reaction on said third plurality of nucleic acid sequences with bar codes, where said third sequencing reaction is performed on said sequencing apparatus comprising said flow cell, thereby reusing said flow cell for a third time.

[0394] REALIZATION 207. The realization method 200, wherein said first barcode, said second barcode and said third barcode have identical sequences.

[0395] REALIZATION 208. The realization method 200, wherein said first barcode, said second barcode and said third barcode have different sequences.

[0396] REALIZATION 209. The realization method 200, further comprising carrying out a nucleic acid amplification or enrichment reaction on said plurality of nucleic acid sequences prior to the sequencing of (b), (d), or both.

[0397] REALIZATION 210. Realization method 200, where said sequencing is selected from the group consisting of a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing.

[0398] REALIZATION 211. The realization method 200, wherein said sequencing reaction is a pore sequencing reaction.

[0399] REALIZATION 212. The realization method 211, wherein said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample.

[0400] REALIZATION 213. The realization method 211, where said epigenetic pattern is a methylation pattern.

[0401] REALIZATION 214. The realization method 200, wherein said plurality of nucleic acid sequences comprises complementary DNA (cDNA) sequences.

[0402] Embodiment 215. The method of Embodiment 200, wherein said plurality of nucleic acid sequences comprises ribonucleic acid (RNA) sequences.

[0403] Embodiment 216. Embodiment 200 method, wherein said plurality of nucleic acid sequences comprises genomic deoxyribonucleic acid (gDNA) sequences.

[0404] REALIZATION 217. The realization method 200, wherein said plurality of nucleic acid sequences comprises a mixture of cDNA, RNA, and gDNA sequences.

[0405] REALIZATION 218. The realization method 200, wherein said first sample is a first food sample or a first sample of the environment associated with said first food sample.

[0406] REALIZATION 219. The realization method 200, wherein said second sample is a second sample of food or a second sample of the environment associated with said first sample of food.

[0407] REALIZATION 220. The method of realizations 218 or 219, where said first food sample, said second food sample, or both are a perishable item.

[0408] REALIZATION 221. The realization method 220, where said perishable item is a meat.

[0409] REALIZATION 222. The realization method 221, where said meat is a bird, a red meat, a fish, or a pig.

[0410] REALIZATION 223. The realization method 221, where said perishable item is a fruit, an egg, a vegetable, a product or a vegetable.

[0411] REALIZATION 224. The method of realizations 218 or 219, where said first sample of the environment, said second sample of the environment, or both are a surface mop or a surface rinse of said environment.

[0412] ACHIEVEMENT 225. The method of embodiments 218 or 219, where said first environment sample, said second environment sample, or both are a food storage container, food handling equipment, or a piece of clothing of a worker from said environment associated with said food processing facility.

[0413] REALIZATION 226. The realization method 200, where said sample is not a food sample.

[0414] REALIZATION 227. The realization method 226, wherein said sample comprises blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid.

[0415] REALIZATION 228. A nucleic acid sequencing apparatus comprising: (a) a nucleic acid library preparation compartment comprising two or more cameras configured to prepare a plurality of nucleic acids from a sample for a sequencing reaction, where said compartment is operationally connected to a nucleic acid sequencing camera; (b) a nucleic acid sequencing chamber, wherein said nucleic acid sequencing chamber comprises: (i) one or more flow cells comprising a plurality of pores or sequencing cartridges configured for the passage of a nucleic acid strand , where two or more of the one or more flow cells are juxtaposed; and (c) an automated platform, where said automated platform is programmed to robotically move a sample from said nucleic acid library preparation compartment to said nucleic acid sequencing chamber.

[0416] REALIZATION 229. The nucleic acid sequencing apparatus of realization 228, wherein said automated platform moves a second sample from said nucleic acid library preparation compartment or from a failed sequencing camera to said nucleic acid sequencing in the detection of a failure of a sequencing reaction.

[0417] REALIZATION 230. The nucleic acid sequencing apparatus of realization 228, wherein said automated platform moves a second sample from said nucleic acid library preparation compartment to said nucleic acid sequencing camera in the detection of a completion of a sequencing reaction.

[0418] REALIZATION 231. The nucleic acid sequencing apparatus of embodiment 228, further comprising adding a bar code for a plurality of nucleic acids in said two or more cameras (a), providing,

in this way, a plurality of nucleic acids with bar codes for said sequencing reaction.

[0419] REALIZATION 232. The nucleic acid sequencing apparatus of realization 228, further comprising adding a plurality of mutually exclusive bar codes to a plurality of nucleic acids in said two or more cameras (a), thereby providing , a plurality of nucleic acids with mutually exclusive bar codes.

[0420] REALIZATION 233. The nucleic acid sequencing apparatus of realization 232, wherein said automated platform robotically displaces two or more of said mutually exclusive nucleic acids with barcodes to said nucleic acid sequencing camera.

[0421] REALIZATION 234. The nucleic acid sequencing apparatus of realization 232, wherein said automated platform robotically moves two or more of said mutually exclusive nucleic acids with barcodes to the same flow cell of said one or more cells of flow.

[0422] REALIZATION 235. The nucleic acid sequencing apparatus of realization 232, wherein said sample is a food sample or a sample of the environment.

[0423] REALIZATION 236. The nucleic acid sequencing apparatus of realization 232, wherein said sample is not a food sample.

[0424] REALIZATION 237. The nucleic acid sequencing apparatus of realization 236, wherein said sample comprises blood, plasma, urine, tissue, feces, marrow, saliva or cerebrospinal fluid.

[0425] REALIZATION 238. An embodiment comprising: (a) adding a first molecular index to a first plurality of nucleic acid sequences in a sample, thereby providing a first plurality of indexed nucleic acid sequences; and (b) adding a second molecular index to said first plurality of nucleic acid sequences of said first sample, thereby providing a second plurality of indexed nucleic acid sequences; and (c) adding a third molecular index to said first plurality of nucleic acid sequences of said first sample, thereby providing a third plurality of indexed nucleic acid sequences; (d) carrying out a sequencing reaction on said third plurality of nucleic acid sequences; and (e) demultiplexing, by a computer system, said third plurality of nucleic acid sequences comprising said first molecular index, said second molecular index, and said third molecular index.

[0426] REALIZATION 239. The realization method 238, wherein said first molecular index, said second molecular index, and said third molecular index are between 1 nucleotide and 18 nucleotides in length.

[0427] REALIZATION 240. The realization method 238, wherein said first molecular index, said second molecular index, and said third molecular index are about 9 nucleotides in length.

[0428] REALIZATION 241. The realization method 238, wherein said first molecular index, said second molecular index, and said third molecular index have identical sequences.

[0429] REALIZATION 242. The realization method 238, wherein said first molecular index, said second molecular index, and said third molecular index have different sequences.

[0430] REALIZATION 243. The realization method 238, wherein said first plurality of indexed nucleic acid sequences, said second plurality of indexed nucleic acid sequences, and said third plurality of indexed nucleic acid sequences form a code of bars showing a periodic block design.

[0431] REALIZATION 244. The realization method 243, wherein said periodic block design has a Levenshtein distance defined between each said first plurality of indexed nucleic acid sequences, said second plurality of indexed nucleic acid sequences, and said third plurality of indexed nucleic acid sequences.

[0432] REALIZATION 245. The realization method 238, wherein said first plurality of indexed nucleic acid sequences, said second plurality of indexed nucleic acid sequences, and said third plurality of indexed nucleic acid sequences form a code of bars showing a non-periodic block design.

[0433] REALIZATION 246. The realization method 245, wherein said non-periodic block design has a defined Levenshtein distance between each said first plurality of indexed nucleic acid sequences, said second plurality of indexed nucleic acid sequences, and said third plurality of indexed nucleic acid sequences.

[0434] REALIZATION 247. The realization method 246, wherein said Levenshtein distance between each said first plurality of indexed nucleic acid sequences, said second plurality of indexed nucleic acid sequences, and said third plurality of indexed nucleic acid sequences is the maximum possible Levenshtein distance.

[0435] REALIZATION 248. An automated microfluidic device for the analysis of a test liquid comprising: a sensor provided in a detection camera; a flow path comprising an input from the detection camera and an output from the detection camera connecting to the detection camera for, respectively, the passage of liquid into and out of the detection camera, and a communicating sample input port fluid with inlet; a liquid collection channel downstream of the outlet; an interruption of the flow path between the exit of the detection camera and the liquid collection channel, preventing the liquid from flowing into the upstream liquid collection channel, whereby the device can be activated by completing the flow path between the sample inlet port and the liquid collection channel; a conditioning liquid filling from the sample inlet port to interrupt the flow path in such a way that the sensor is covered by the liquid and is not exposed to a gas or gas / liquid interface; where the device is configured in such a way that after activation of the device, the sensor remains not exposed to a gas or gas / liquid interface and the application, respectively, of one or more volumes of the test liquid to a wet surface of the inlet provides sufficient liquid actuation force to introduce one or more volumes of the test liquid into the device and move the buffer liquid to the liquid collection channel, where the device additionally comprises a removable seal for the sample inlet port, where the removable seal has a body that protrudes at least 1 cm above the surface of the microfluidic device when placed in the sample inlet port.

[0436] REALIZATION 249. The device of realization 248, where the removable seal protrudes at least 1, 2.0, 2.5, 3, or 3.5 cm above the surface of the device.

[0437] REALIZATION 250. The device of realization 248, where the removable seal is cylindrical, with a first flat end and a second tapered end that tapers to a size sufficient to seal the sample inlet port on the device.

[0438] REALIZATION 251. The device of realization 248, where the removable seal comprises a metallic material.

[0439] REALIZATION 252. The device of realization 249, where the removable seal comprises tungsten, aluminum, austenitic stainless steel or ferritic stainless steel.

[0440] REALIZATION 253. The device of realization 249, where the removable seal is resistant to decontamination in diluted nitric acid, 1M NaOH or diluted sodium hypochlorite.

[0441] REALIZATION 254. The device of realization 249, where the removable seal comprises polypropylene or polycarbonate.

[0442] REALIZATION 255. The nucleic acid sequencing apparatus of embodiment 228, wherein the one or more flow cells comprising a plurality of pores or sequencing cartridges is the automated microfluidic device of any of the embodiments 248-255.

EXAMPLES EXAMPLE 1: Preparation of food and environmental samples

[0443] Samples of food and the environment can be processed for various purposes, such as enriching one or more microorganisms in the sample, or isolating one or more microorganisms from the sample. The following protocol was used in the preparation of several food and environmental samples including: carcass rinses, stainless steel, boot covers from primary production, dry pet food and egg shells.

Table 1: Sample preparation of Food and Environment Table 1 Sample preparation of Food and Environment Matrix Size of Incubation Quantity Sample Enrichment determined by volume or weight Rinse 30 ± 0.6 mL sample 20 ± 0.5 mL of clear medium 42 ± 1 ° C per Salmonella rinse fluid (CSM) carcass 9-24 h Stainless steel 1 pre-sponge 10 ± 0.5 mL clear medium 42 ± 1 ° C by moistening with 10 Salmonella (CSM) ) 9-24 h mL of tris-buffered saline solution Boot Covered 1 boot of 50 ± 1 mL of clear medium of 42 ± 1 ° C by Environmental Sampling Salmonella (CSM) 9-24 h environmental pre-moistened with 10 mL of skimmed milk Ration of 25 ± 0.5 g 100 ± 1 mL of clear medium 42 ± 1 ° C per Salmonella Animal (CSM) 9-24 h Estimation

Eggshells 100 ± 2 g 200 ± 2 mL of clear medium 42 ± 1 ° C for Salmonella (CSM) 9-24 h EXAMPLE 2: obtaining a carcass sample

[0444] In this example, carcass samples are generated by aseptically draining excess fluid from a carcass and transferring the carcass to a large sterile sampling bag. 100 mL of an enriched broth was poured, in this case, clear medium of Salmonella (CSM) in the cavity of the carcass in the sample bag. The carcass was rinsed inside and out with a rocking motion for about a minute, while ensuring that all surfaces (inside and outside of the carcass) were rinsed. About 20 ± 0.5 mL of CSM were added to the sample bag and homogenized by massaging the sample bag for approximately 1.5-2 min. The sample was incubated at 42 ± 1 ° C for 9-24 h, providing an enriched sample. EXAMPLE 3: Taking a sample of the stainless steel surface environment

[0445] In this example, a sample of the stainless steel surface environment was generated by humidifying a sterile sampling sponge in 10 mL of Dey-Engley broth prior to sampling, or by using a pre-moistened sponge on it. The sponge was used to touch, scrub, or otherwise contact the stainless steel surface and I subsequently place it in a sample bag. About 10 ± 0.5 mL of CSM was added to the sampling sponge. Subsequently, the sponge was pressed to expel the collected broth into the CSM solution. The sample was incubated at 42 ± 1 ° C for 9-24 h, providing an enriched sample. EXAMPLE 4: Obtaining a sample of a boot cover environment

[0446] In this example, a sample of a boot cover environment was first pre-moistened with skimmed milk. About 50 ± 1 mL of CSM was then added to the sampling bag containing the boot cover environment sample. The contents were mixed thoroughly for approximately 1.5 - 2 min, and incubated at 42 ± 1 ° C for 9-24 h, thus providing an enriched sample. The enriched sample was removed from the incubator and quickly mixed. EXAMPLE 5: Obtaining a sample of pet food

[0447] In this example, about 25 ± 0.5 g of a pet food sample was added to a filtered sample bag. About 100 ± 1 ml of CSM was then added to the sample bag containing said feed. The contents were mixed thoroughly for approximately 1.5 - 2 min, and incubated at 42 ± 1 ° C for 9-24 h, thus providing an enriched sample. The enriched sample was removed from the incubator and immediately mixed. EXAMPLE 6: Obtaining a sample of egg shells

[0448] In this example, about 100 ± 2 g of a homogenized sample of eggshells was added to a filtered sample bag. About 200 ± 2 mL of CSM were then added to the sample bag containing said homogenized sample of egg. The contents were mixed thoroughly for approximately 1.5 - 2 min, and incubated at 42 ± 1 ° C for 9-24 h, thus providing an enriched sample. The enriched sample was removed from the incubator and immediately mixed. EXAMPLE 7: Treatment with photoreactive DNA binding dye

[0449] In this example, a photoreactive DNA-binding dye, namely monoazide propidium (PMA) was added to various samples of food and the environment, including the samples described in Examples 1-6. In general, 5 μL of a PMAxx solution was added to a 200 μL well of a 96-well PCR plate. Approximately 45 μL of each sample enriched from the sample bags described in Examples 1-6 was added to individual wells on the PCR plate containing PMAxx. The samples were thoroughly mixed by gentle pipetting and placed in the dark for 10 min at room temperature. Subsequently, the plates were incubated under blue LED light for 20 min. 10 μL of each sample was then diluted with 90 μL of Lysis Buffer in an additional 200 μL of 96-well PCR plate. The plate was then incubated in a thermal cycler as shown below. Alternatively, the sample could be incubated in a water bath.

Stage Temperature Time 1 37 ° C 20 min 2 95 ° C 10 min EXAMPLE 8: PMAxx-induced removal of floating DNA

[0450] This example demonstrates that adding a solution of the photoreactive DNA binding dye PMAxx to a sample solution reduced the number of free and contaminating DNA in said sample. Specifically, 45 μL of each sample enriched from the sample bags as described in Examples 1-7 was added to individual wells of the 96-well PCR plate containing 25 μL of PMAxx solution. The sample solutions were mixed thoroughly by gentle pipetting and placed in the dark for 10 min at room temperature. Subsequently, the plates were incubated under blue LED light for 20 min. 10 μL of each sample was then diluted with 90 μL of Lysis Buffer in a new 96-well plate of 200 μL PCR. The plate was then incubated in a thermal cycler as shown below. Analysis of the sample readings showed that the addition of PMAxx solution (25 μL) to the sample solution was sufficient to reduce the number of floating DNA by at least 2 orders of magnitude, as shown in Fig. 13. EXAMPLE 9: Reaction amplification

[0451] In this example, the samples described in Examples 1-8 were subjected to an amplification reaction. In summary, 15 μL of a mixture of primer and master polymerase mixture was added to individual wells of a 96-well plate of 200 μL PCR. About 5 μl of each sample treated with a photoreactive DNA binding dye was added to the respective wells containing the master polymerase mixture. The solution was mixed gently by pipetting up and down and placed in a thermocycler under the conditions described below.

Step Temperature Time 1 95 ° C 3 min 2 95 ° C 30 sec 3 57 ° C 1 min 4 72 ° C 1 min 5 Go to step 2, 37 times 6 72 ° C 10 min 7 10 ° C Stops EXAMPLE 10: Preparation from the library

[0452] In this example, Solid Phase Reversible Immobilization (SPRI) Magnetic Spheres were used to purify and quantify one or more of the samples described in Examples 1-9. In summary, the SPRI spheres were removed from storage at 4 ° C and allowed to reach room temperature for approximately 15 min. About 1 mL of 80% ethanol was prepared by combining 800 μL of ethanol and 200 μL of molecular biology grade water. Equal volumes of each sample amplification product (described in Example 9) were used to obtain at least 100 μL of pooled products, which were purified using SPRI beads together with standard manufacturing protocols. In summary, 100 μL of vortexed PCR product was pipetted into a 0.2 mL PCR tube and 60 μL of SPRI beads were added. The tube was mixed thoroughly by pipetting up and down approximately 10 times and incubated at room temperature for 5 min. The sample / sphere mixture was placed on a magnetic support and the spheres were allowed to pellet in a ring for approximately 30-60 s, leaving a clear supernatant. The supernatant was discarded, leaving the tube in the magnetic support while placing the pipette tip in the lower center of the tube when aspiration in order to avoid disturbance of the spheres. 190 μL of 80% ethanol was then added to the tube, which was incubated for 5-10 s. The tube was completely aspirated and the ethanol solution was discarded. The process was repeated twice. The sample was allowed to dry for 3-5 min at room temperature, or until no ethanol remained visible. Once completely dry, the tube was removed from the magnetic support and resuspended in 50 μL of 10 mM RSB in the tube. The tube was mixed thoroughly by gently pipetting up and down approximately 10 times and incubated at room temperature for 2 min. The tube was moved to a magnetic support and incubated at room temperature for 2 min to allow pelleting of the spheres. 50 μL of the eluate was removed and retained. EXAMPLE 11: End Repair

[0453] In this example, the terminal ends of the nucleic acid fragment described in Example 10 were repaired as described below. First, the following reagents were combined and pipetted up and down approximately 10 times. Reagent Volume Libraries Gathered Purified 45 µL NEB Ultra II end-prep reaction buffer 7 µL NEB Ultra II End-prep enzyme mix 3 µL ONT DNA CS (DCS) 5 µL Total 60 µL

[0454] The samples were then spun for approximately 5 s using a bench top mini-centrifuge. The end repair was carried out in a thermal cycler with the following conditions: Stage Temperature Time 1 20 ° C 5 min 2 65 ° C 5 min 3 25 ° C 5 min

[0455] Subsequently, the samples were rotated for approximately 5 s using a bench top mini-centrifuge. 60 μL of SPRI spheres were added to the repaired tip product and mixed by pipetting up and down approximately 10 times. The samples were incubated for 5 min at room temperature. The sample / spheres mixture was placed on a magnetic support and the spheres were allowed to pellet in a ring around the middle part of the tube for approximately 30-60 s, leaving a clear supernatant. The supernatant was discarded, leaving the tube in the magnetic support while placing the tip of the pipette in the lower center of the tube when aspirating to avoid disturbing the spheres. 190 μL of 80% ethanol was added to the samples. The 80% ethanol solution was incubated in the tube for 5-10 s, and the ethanol was aspirated and discarded. This process was repeated twice. The sample was allowed to dry for 5 min at room temperature, or until no ethanol remained visible. The spheres were resuspended with 31 μL of molecular biology grade water and mixed gently by pipetting up and down approximately 10 times and incubated for 2 min at room temperature. The tube was moved to a magnetic support and the spheres were allowed to pellet for approximately 30-60 s. The eluate was retained as the "repaired tip product". EXAMPLE 12: Connection

[0456] In this example, using the repaired end product from Example 11, the following reagents were combined: Reagent Volume Repaired end product 30 µL ONT Adapter Mix (AMX 1D) 20 µL NEB Blunt / TA Ligase Master Mix 50 µL Total 100 µL

[0457] The reagents were gently mixed by pipetting up and down approximately 10 times and were incubated at room temperature for 10 min. About 40 μL of SPRI beads were added to the mixture, gently mixed and incubated at room temperature for 5 min. The sample / sphere mixture was placed on a magnetic support and the spheres were allowed to pellet in a ring around the middle part of the tibo for approximately 30-60 s, leaving a clear supernatant. The supernatant was discarded leaving the tube in the magnetic support while placing the tip of the pipette in the lower center of the tube when aspirating to avoid disturbance of the spheres. The tube was removed from the magnetic support and 140 μL of ONT-Adapter Bead Binding buffer was pipetted over the spheres. The sample was mixed by gentle pipetting up and down approximately 10 times in order to resuspend the pellet. The tube was returned to the magnetic support and the spheres were left to pellet in a ring around the middle part of the tube for approximately 30-60 s, leaving a clear supernatant. The supernatant was discarded leaving the tube in the magnetic support while placing the tip of the pipette in the lower center of the tube when aspirating in order to avoid disturbing the spheres. The tube was then removed from the magnetic support and an additional 140 μL of Adapter Bead Binding buffer was added and pipetted up and down to suspend the pellet. The sample / sphere mixture was placed on a magnetic support and the spheres were allowed to pellet in a ring around the middle part of the tube for approximately 30-60 s, leaving a clear supernatant. The supernatant was discarded leaving the tube in the magnetic support while placing the tip of the pipette in the lower center of the tube when aspiration in order to avoid the disturbance of. The tube was then removed from the magnetic support. About 15 μL of elution buffer (ELB) was added to the beads, and the beads were mixed thoroughly by pipetting up and down approximately 10 times and incubated for 10 minutes at room temperature for 5 min. The tubes were moved to a magnetic support and the spheres were allowed to pellet for approximately 30-60 s. About 15 μL of eluate was removed and retained as the “final bound product” for sequencing. EXAMPLE 13: Pore sequencing

[0458] In this example, a food sample or an environment sample was processed by pore sequencing using the manufacturer's standard protocols. In summary, one or more flow cells were initialized by combining the following reagents per flow cell: Volume ONT-Running Buffer Reagent with Fuel Mix (RBF) 480 µL H2O molecular grade 520 µL Total 1,000 µL

[0459] A load library has been prepared by combining the following reagents:

ONT-Running Buffer Volume Reagent with 35 µL Fuel Mix (RBF) ONT-Library Loading Bead (LLB) 25.5 µL Final bound product 12 µL H2O molecular grade 2.5 µL Total 75 µL

[0460] The initiation port on the flow cell was gently opened and approximately 50 μL of the conservation buffer and any small bubbles were removed, as shown in Fig. 14. About 800 μL of initiation mixture was added through the initiation port flow cell. Subsequently, 200 μL of the initiation mixture was placed through the initiation port. The final load library was mixed thoroughly and 75 μL was added via the SpotON port, as shown in Fig. 15. The pore sequencing device lid was closed and the sequencing was performed. EXAMPLE 14: Analysis and interpretation of data

[0461] In this example, an electronic communication comprising a set of data associated with the sequencing reaction described in Example 13 was transmitted in the cloud for analysis. The results of the analysis were reported back to the customer. In Fig. 16 in this particular example, the customer requested an analysis of the sample for the presence or absence of Listeria, Salmonella, Campylobacter, e. coli, which required simultaneous guidance for multiple pathogens. EXAMPLE 15: Identification of a microorganism in a food, environment sample, or in a non-food sample associated by microbiome metagenomics and supervised learning

[0462] In this example, pore sequencing data was used to identify foodborne disease-causing microorganisms. In summary, the methods and processes described in Examples 1-13 were used to identify samples of food and environments comprising one or more of the organisms shown below.

Table 2: Exemplary Pathogenic Microorganisms Identified By The Methods According To This Report Table 2 Organism Common Name Time of Signs & Disease Appearance-Symptoms After Duration Sources of Ingestion of the Disease food Bacillus cereus Poisoning 10-16 hours Colic 24-48 Meat , B. cereus in sit-ups, hours soaked, food sauces, watery diarrhea, vanilla nausea sauce Campylobacter Campilo- 2-5 days Diarrhea, 2-10 days Raw bird and colic jejuni bacteriosis, fever, and undercooked, vomiting; non-diarrhea milk can be pasteurized, be blood-contaminated slow water Clostridium Botulism 12-72 hours Vomiting, Variable Food botulinum diarrhea, improper-blurry vision, double-vision mind, canned, difficulty especially swallowing, vegetables canned weakness in muscle. home, fish May result in fermented, respiratory failure and cooked potatoes in aluminum foil death

Perfringens Poisoning 8–16 hours Normal Colic- Meat, poultry, for food with abdominally 24 sauces, Intense Perfringens, hours dry or pre-watery cooked diarrhea, food over time and / or temperature Cryptosporidium Cryptosporidiosis 2-10 days Diarrhea Can be Non-intestinal food (usually remitting and cooked or watery), recurrent colic food by stomach contaminant, from a stomach weeks to a disturbed handler, months after mild fever cooking, contaminated drinking water Cyclospora Ciclosporiase 1-14 days, Diarrhea Can be Various types cayetanensis normally (usually remitting and at least 1 aqueous product), fresh relapse loss (berries week of appetite, forward for imported, lettuce loss, substantial weeks of basil) weight, colic stomach months, nausea, vomiting , fatigue E. coli E-infection 1-3 days Diarrhea 3-7 or Water or (aqueous Escherichia coli, colic plus coli food days) (common cause of abdominal, co ntaminated producing “diarrhea from some vomiting with traveling toxin feces”) human E. coli O157: H7 Colitis 1-8 days Severe diarrhea 5-10 days Hemorrhagic beef (often undercooked or infection by mind (especially E. coli O157 : H7 bloody- and hamburger), slow), milk pain and abdominal juice and not vomiting. pasteurized, usually fruits and little or no raw vegetables (for example, fever is sprouts), and present.

More common water in contaminated children aged 4 years and under.

It can lead to kidney failure.

Hepatitis Hepatitis 28 days in Diarrhea, Variable urine, 2 Raw product, medium (15-50 dark, weeks at drinking water days) yellowed- 3 months contaminated, ment, and foods not symptoms cooked and similar to flu foods, ie cooked fever, headache, nausea reheated, and pain after abdominal contact with an infected food handler; seafood from contaminated waters Listeria Listeriosis 9-48 hours Fever, pain Variable Non-monocytogenes milk for muscle and pasteurized symptoms, gastro-nausea or intestinal soft cheeses, 2-6 diarrhea. Made of milk weeks for non-pasteurized pregnant women, invasive disease can ready meats show flu-like symptoms for consumption, and the infection can lead to premature or stillbirth.

The elderly or patients with immune compromise may develop bacteremia or meningitis.

Norovirus Also 12-48 hours Nausea, 12-60 Raw product, called vomiting, colic hours drinking water, contaminated, viral abdominal gas, diarrhea, diarrhea, fever, bad winter foods, headache. cooked and non-gastroenteritis Diarrhea is acute bacterial food, most cooked non-poisoning prevalent in reheated food, and adults, after contact vomiting infection more with a common food in handler children. infected; seafood from contaminated waters

Salmonella Salmonellosis 6-48 hours Diarrhea, 4-7 days Eggs, poultry, fever, meat, milk or abdominal colic, pasteurized non-vomiting juice, cheese, contaminated raw fruits and vegetables Shigella Shigellose or 4-7 days Colic 24-48 Raw product , abdominal bacillary dysentery, hours drinking water, fever and contaminated, diarrhea.

Food stools cannot contain cooked and blood and mucus foods. stews that have not been reheated after contact with an infected handler Staphylococcus Poisoning 1-6 hours Appearance 24-48 Meat, potato aureus with sudden hours and non-Staphylococcus eggs nausea and chilled vomiting or inappropriate. Cooled, abdominal colic. salads, Diarrhea cakes and cream fever may be present.

Vibrio V. Infection 4-96 hours diarrhea 2-5 days Seafood parahaemolyticu parahaemolyticu watery undercooked ss (occasional- or raw, such bloody-minded as slow), abdominal cramping, nausea, vomiting, fever Vibrio vulnificus infection V. 1-7 days Vomiting, 2-8 days Seafood vulnificus diarrhea, undercooked abdominal pain, or raw, such infection as originating in the blood seafood. Fever, (especially bleeding and oysters) on the skin, ulcers requiring surgical removal. It can be fatal for people with liver disease or weakened immune systems.

[0463] Firstly, a database was built using data from approximately 35000 foods or samples from the environment (of which about 10% contained traces of pathogenic microorganisms as shown in Table 3) using two components: presence of microorganism and chemical composition. Pore sequencing in combination with the use of characteristic polymorphic genetic regions (comprising SNP's, RFLP's, STRs, VNTR's, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence indices, indices, and element elements insertion) associated with a wide diversity of microorganisms were used to analyze each sample for the presence or absence of 17800 different bacterial species (representing both pathogenic and non-pathogenic bacterial species). In addition, data on sample composition were collected for 4600 food ingredients in each environmental / food sample.

[0464] The data using the main bacteria associated with pathogen contamination (shown in Fig. 5) were used to train a classification model, which was tested for overfitting by machine learning techniques.

[0465] In addition, the performance of the model was tested by testing a set of foods or samples from unknown environments (50% of each). The complete results of the ROC analysis of the accuracy of the classification models are shown in Table 3. In the cases of all pathogens in Table 3, the classification model based on metagenomics showed accuracy above 95% and accuracy above 97% for detecting the pathogen. Table 3: Independent Validation of Pathogen Prediction in Unknown Samples Table 3 Pathogen Accuracy Accuracy Vibrio parahaemolyticus 99.78% 96.55% Staphylococcus aureus 99.67% 100.00% Yersinia pseudotuberculosis 99.45% 100.00% Vibrio vulnificus 99 , 12% 100.00% Shigella boydii 99.12% 100.00%

Salmonella enterica 96.16% 94.39% Escherichia coli 97.48% 98.40% EXAMPLE 16: In silico evaluation of the sensitivity and specificity of the primer

[0466] This example describes the in silico evaluation of the sensitivity and specificity of the primer for pathogen detection in PCR assays. First, a pair of candidate primers was mapped against inclusion and exclusion sequences in sequence databases. Second, the identified hits are tabulated based on the predicted amplification patterns in order to then determine the sensitivity and specificity of the in silico primer pair.

[0467] Specifically, a pair of primers was designed to target Salmonella Montevideo and Salmonella Oranienburg. The composition of the sequence database for in silico evaluation contained 7,705 Salmonella genomes, including 98 Montevideo / Oranienburg genomes, and 1707 non-Salmonella genomes (total of 9412 genomes). Tabulation of the results of the analysis showed that the exact number of 98 genomes of Salmonella Montevideo and Oranienburg was identified as true as true positive hits. The remaining 9314 genomes (which equals the total number of 9412 genomes minus the 98 identified true positives) were characterized as true negative results. The results are shown in Fig. 17. EXAMPLE 17: Reuse of flow cells

[0468] This example shows that the MinION / GridION flow cell can be reused for sample sequence analysis at least 2 times. Between each sample analysis (50 samples analyzed in each analysis) the flow cell was washed with a buffer system resulting in 30,000 readings and 26,000 readings per sample during the second and third reuse, respectively, compared to the 36,000 readings per sample when using a new flow cell (Fig. 18). Fig. 19 illustrates that the number of readings per sample for reused MinION / GridION flow cells was well above the minimum acceptable limit of 10,000 (10 K) readings per sample. EXAMPLE 19: Automated detection of pathogenic risk

[0469] A significant source of data confusion in detecting pathogenic risk is contamination of samples by microorganisms residing in human handlers. Likewise, a Biomek-based sample sequencing platform was implemented that does not require human manipulation after enrichment (see Fig. 11 and Fig. 12) to implement the methods in Examples 10-13 and 15. Automation included all steps preparation of the library after incubating the samples as in Examples 1-6, and included cell lysis, PCR, cleaning and sequencing. An automated handling system is illustrated in Fig. 11.

[0470] In order to determine the performance of the automated handling system, samples were analyzed that received 10 different Salmonella serotypes (Enteritidis, Thyphimurium, I 4_ [5] _12: i: -, Newport, Javiana, Infantis, Montevideo, Heidelberg, Muenchen) by automated or manual manipulation. The results are shown in Fig. 20. Serotype detection was revealed to be 100% between automated and manual manipulation, and a Student T test of the number of sequencing readings generated did not indicate a significant difference between manual and automated manipulation. . EXAMPLE 20: Detection of the metagenomics of the food product microbiome with expired shelf life / shelf life

[0471] A significant limitation of existing environmental pathogen detection methods is that they involve culture, which involves the use of multiple different specialized means to detect different classes of pathogens (for example, autotrophic bacteria for one or more nutrients vs those that are not ). This severely limits the ability to detect contamination in food during storage. Likewise, we applied our environmental sampling / pore sequencing technique as outlined in Examples 1-13 on 100 samples of chicken wings and 100 samples of ground franc. Each sample was analyzed for the presence / absence of 17800 pathogenic and non-pathogenic bacteria.

[0472] We apply a principal component analysis to the complete or ground chicken data sets, which is represented in Figs. 21 and Fig. 22. Sample data points for both whole and ground chicken over a discernible trajectory more than 2 days before the expiration date (see movement along PC2 in the whole chicken sample and PC1 / PC3 in the ground chicken sample), although data points for 1-2 days of expiration begin to diverge rapidly.

[0473] Principal component analysis suggested that a classification model could be constructed to detect whether or not a sample of whole or ground chicken had expired. The data regarding the presence / absence of 17800 pathogenic and non-pathogenic bacteria were used to generate a classification model. When tested on an independent sample data set, this classifier showed 97% accuracy in detecting samples after their expiration date using a ROC analysis. EXAMPLE 21: Comparison of periodic and non-periodic block design for the sequencing of sample bar codes; interference reduction using non-periodic block initiator design.

[0474] In order to improve the detection of desired sequences during the sequencing processes, the performance of different barcode placement designs in sequence detection was tested. Unique nucleotide sequences were generated with maximum Levenschtein distances between each and used to generate two barcode formats to be applied to the sequences during library preparation: a) a periodic block design, in which each barcode consisted in a single block sequence repeated 3 times, and b) a non-periodic block design, in which 3 single blocks were combined in tandem for each bar code sequence.

[0475] These periodic and non-periodic block designs were tested together with a conventional barcode design (which were designed barcodes provided by our sequencing platform provider) when applied to the same samples in the test sequencing processes (see Fig. 23). In summary, a defined Levenshtein distance between each “building block” or molecular index can be used to form larger bar codes. Such larger bar codes may feature a periodic block design, such as bar codes created by repeating each block multiple times with the largest possible Levenshtein distance between the individual blocks (see Fig. 23). Alternatively, such bar codes may also feature non-periodic block design, such as bar codes created by multiple concatenated blocks that are unique to each bar code with the largest possible Levenshtein distance between the individual blocks (see Fig. 23) .

[0476] We ran 10 ONT MinION races and the average% of the sequences retained and the interference for each race were determined. The results are shown in Table 4. Both periodic and non-periodic barcode designs showed improvements in retention and interference versus the conventional design, with non-periodic designs being the best in both metrics.

[0477] Both barcode designs have distinct advantages. Both increase the number of sequences retained and allow an adjustable precision by choosing 1, 2, or 3 blocks in demultiplexing, but the periodic design requires fewer repeated blocks and presents less complexity in demultiplexing, while the non-periodic design allows the prevention of interference. The prevention of increased interference from non-periodic design suggests a method for reducing interference during highly multiplexed processes or when a flow cell is reused.

Table 4: Performance of Conventional Barcode Design vs Non-Periodic Block Design Table 4

Design Block design Periodic unconventional block design Sequences 85% 96% 98% retained Interference 6% 5% 2% EXAMPLE 22: Detection of transient vs resident microbes by metagenomics.

[0478] Food and environment samples containing Listeria were prepared, libraries were built, and sequencing was performed as in Examples 1-13 and 15. The samples were analyzed for the presence of Listeria by analyzing highly polymorphic markers. A major component analysis of Listeria sequences isolated from sequencing (see Fig. 24) identified clusters of closely related bacteria probably originating from the same source. EXAMPLE 23: Detection of microbial serotype in advance in the sequencing process

[0479] The length of time for the complete sequencing process represents an important limitation in the speed of detection or serotyping of pathogenic bacterial strains by high-throughput sequencing. It was hypothesized that the use of “live” detections during sequencing processes (which can be performed as soon as 1 hour for ONT MinION and GridION, and 5 hours for Illumina MiSeq) would allow certain bacteria to be detected / serotyped on a permanent basis based on sequencing, with confirmation of follow-up by other tests not based on sequencing (for example, Q-PCR).

[0480] A test analysis was performed on 50 environment samples with about 15% positive for one of the pathogens identified in Table 3; the samples received Salmonella, Listeria, E. coli, and Campylobacter (2 samples each) of the main strains / serotypes of the main known pathogens. The species of the pathogen was detected by the detection of characteristic genomic markers. The accuracy of detection and serotyping of species at "live" and complete points in time for the sequencing processes was compared. The results are shown in Table 5. Early detection (1 hour for ONT MinION, and 5 hours for Illumina MiSeq) was 100% accurate for both formats, while MinION showed increased accuracy for serotyping.

Table 5: Detection “Early call” of Species and Bacterial Serotype Table 5 Platform Call Sequences Call of calls in “early detection serotyping serotyping call” final MiSeq 425,000 100% 20% 100% MinION 630,000 100% 60% 100% EXAMPLE 24 : Cell concentration of microbial samples prepared from food or environmental

[0481] Samples of food or microbial environments prepared as in Examples 1-6 are ideally subjected to a concentration step in order to maximize the concentration of pathogenic-associated nucleic acids (for example, represented in CFU / µl) and increase downstream detection by sequencing. A filterless method involving phase separation is used to maximize the result in sample preparation.

[0482] In summary, a small volume of a liquid formulation that is designed to be a) not miscible with the medium enrichment medium; b) have a mass density similar to that of the desired cell type; c) not reactive with downstream applications; d) spontaneously separate into a separate layer after mixing with the medium enrichment medium from the process of Examples 1-6 is added to the enrichment medium, and the sample is allowed to equilibrate in a conical tube in order to achieve the state shown in Fig. 27, which illustrates the process with microspheres instead of cells. In some embodiments, equilibrium occurs with or without centrifugation-assisted phase separation. The aqueous liquid formulation added can be a mixture of polymers capable of forming density gradients (for example, Ficoll, PEG, glycerol). The desired cellular material, for example, the microspheres shown in Fig. 27), is then collected by direct pipetting of the desired layer and collected using a fraction flow method. EXAMPLE 25: Reuse of flow cells

[0483] Three groups of 96 samples (including a mixture of samples for positive pathogen as positive samples or not for pathogen as negative samples) were prepared according to the methods described in examples 7-

12. The samples received barcodes by transferring the libraries to 96-well plates containing barcode uniquely indexed specifically for each well of the 96-well plate. Each group of samples from the 96-well plates was pooled in a single solution and each sample was processed successively in an Oxford Nanopore flow cell. Each cell was washed with buffer between runs. Different numbers of positive and negative samples for Salmonella were provided between runs in order to introduce variety of sequence in each group. These samples were distributed in different wells of plates indexed with bar codes. The plate index and bar code assignments for each group are shown in the table below. Tables 6-8 illustrate in a 96-well grid the sample assignment (positive or negative) for each well / unique barcode index for each of 3 successive runs.

[0484] Table 6 illustrates the index and well allocations of positive and negative samples of Salmonella samples for each run in the same nanoporous flow cell. Table 6 illustrates a first run, plate IP1.

Table 6 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + + B - - - - - - - - - - - -

C + + + + + + + + + + + + D + + + + + + + + + + + + E + + + + + + + + + + + + F + + + + + + + + + + + + G - - - - - - - - - - - - H + + + + + + + + + + + +

Table 7 Illustrates a second run, IP2 card. Table 7 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + + B + + + + + + + + + + + + C - - - - - - - - - - - - - D - - - - - - - - - - - - - - - - - - - - - - - - - F - - - - - - - - - - - - - G + + + + + + + + + + + + H + + + + + + + + + + + +

Table 8 Illustrates a third run, plate IP3. Table 8 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + + + + + + B + + + + + + + + + + + + C + + + + + + + + + + + + D - - - - - - - - - - - - E - - - - - - - - - - - - F + + + + + + + + + + + + G + + + + + + + + + + + + H + + + + + + + + + + + +

[0485] The data from the sequencing runs were analyzed and are shown in Table 9. Table 9 summarizes the performance parameters for each run, showing the number of multiplexed samples, whether the samples were identified as positive or negative for Salmonella, the number of active pore nanopore sequencing available in each run, the number of total readings generated for each run and the number of false positives (FP) or false negatives (FN) for the presence of Salmonella in each run.

[0486] Table 9 illustrates the sample classification as positive or negative for Salmonella and the performance of the nanopore sequencing for every 3 successive runs in the same flow cell.

Table 9 Corr. Total Posit. Negat. Pore cell Readings FP FN Id samples plate flow total assets 1 96 IP1 72 24 New 1485 1.85 M 0 0 2 96 IP2 48 48 Running 1 1104 1.22 M 0 0 washed 3 96 IP3 72 24 Running 2 865 1.03 M 0 0 washed

[0487] Surprisingly, high numbers of readings (1.03-1.85 million) were generated for each run (well above the minimum acceptable limit of 10K readings per sample). In addition, the data for each race allowed 100% accuracy in the correct classification of the samples as positive or negative for the presence of Salmonella (for example, zero false positive or false negative ratings) and the accuracy in the ratings did not decline between runs.

[0488] The results in Table 9 thus show, unexpectedly, that the claimed method is able to correctly distinguish as many as 96 samples with single barcodes stacked / multiplexed together in a single sequence procedure in a nanoporous flow cell, and that this can be repeated in the same nanoporous flow cell up to 3 times without a functional decline in data quality.

[0489] Although the preferred embodiments of the present invention have been shown and described here, such embodiments are provided by way of example only.

The invention is not intended to be limited by the specific examples provided in the report.

Although the invention has been described with reference to the report mentioned above, the descriptions and illustrations of the achievements here are not to be considered in a limiting sense.

Numerous variations, changes and substitutions will occur to those skilled in the art without departing from the invention.

In addition, it should be understood that all aspects of the invention are not limited to the specific representations, configurations or relative proportions presented here that depend on a variety of conditions and variables.

It is to be understood that various alternatives to the embodiments of the invention described herein can be employed in the practice of the invention.

Thus, it is contemplated that the invention must also cover such alternatives, modifications, variations or equivalents.

It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents are covered by them.

Claims (42)

1. Method characterized by the fact that it comprises: (a) the implementation of a test for one or more food processing facilities; (b) carrying out a sequencing reaction on a food sample or a sample from the environment of said one or more food processing facilities; (c) transmitting an electronic communication comprising a set of data associated with said sequencing reaction of said food sample or said environment sample from said one or more food processing facilities to a server; and (d) scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with a microorganism. 2. Method according to claim 1, characterized in that said scanning scans 0.001%, 0.01%, 0.1%, 1% of said data set transmitted to one or more genes associated with said microorganism. 3. Method according to claim 1, characterized in that said sequencing reaction is a pore sequencing reaction, a next generation sequencing reaction, a next generation shotgun sequencing, or Sanger sequencing. Method according to claim 3, characterized in that said sequencing reaction is a pore sequencing reaction. 5. Method according to claim 4, characterized in that the said pore sequencing reaction distinguishes an epigenetic pattern in a nucleic acid from said food sample or said environment sample. 6. Method according to claim 5, characterized in that said epigenetic pattern is a methylation pattern. 7. Method according to claim 1, characterized by the fact that said microorganism is previously selected by a customer. 8. Method according to claim 1, characterized in that it additionally comprises scanning, by a computer, of at least a fraction of said data set transmitted to one or more genes associated with two or more microorganisms. 9. Method according to claim 1, characterized in that said microorganism is selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of the genus Campylobacter, a microorganism of the genus Listeria, and a microorganism of the genus Escherichia. 10. Method according to claim 1, characterized in that said food sample is a perishable item. 11. Method according to claim 10, characterized in that said perishable item is meat. 12. Method according to claim 11, characterized in that said meat is a bird, a red meat, a fish, or a pig. 13. Method according to claim 8, characterized in that said perishable item is a fruit, an egg, a vegetable, a product or a vegetable. Method according to claim 1, characterized in that said sample of the environment is a surface mop or a surface rinse of said one or more food processing facilities. Method according to claim 1, characterized in that said sample of the environment is a food storage container, a food handling equipment of said one or more food processing facilities, or a piece of clothing from a one or more food processing facilities. 16. Method according to claim 1, characterized in that it comprises in addition to the amplification or enrichment of one or more nucleic acids of said food sample or said environment sample before carrying out said sequencing reaction. 17. Method according to claim 1, characterized in that it additionally comprises the addition of at least one bar code to one or more nucleic acids of said food sample or said environment sample before carrying out said sequencing reaction . 18. Method according to claim 17, characterized in that it additionally comprises the creation, on a computer, of a data file that associates said at least one bar code with a source of said food sample or said sample of the environment. 19. Method according to claim 17, characterized in that it further comprises the addition of a plurality of mutually exclusive bar codes to a plurality of food processing facilities. 20. Method according to claim 1, characterized in that said scanning comprises scanning said data set transmitted to one or more polymorphic genetic regions. 21. Method according to claim 20, characterized in that said one or more polymorphic regions comprise one or more single nucleotide polymorphisms (SNP's), one or more restriction fragment length polymorphisms (RFLP's), one or more repetitions short tandem (STRs), one or more variable numbers of tandem repetitions (VNTR's), one or more hypervariable regions, one or more minisatellites, one or more dinucleotide repetitions, one or more trinucleotide repetitions, one or more repetitions of tetranucleotide, one or more repetitions of simple sequence, one or more indelible, or one or more insertion elements. 22. Method according to claim 20, characterized in that said one or more polymorphic regions comprise one or more single nucleotide polymorphisms (SNP's). 23. Method according to claim 1, characterized by the fact that said sequencing reaction differentiates a living microorganism from a dead microorganism. 24. Method according to claim 1, characterized in that said sequencing reaction differentiates a resident microorganism in comparison with a transient microorganism. 25. Method according to claim 1, characterized in that said method distinguishes a microorganism of an Escherichia genus from a microorganism of a Citrobacter genus or a Shiga toxin-producing E. coli (STEC) from a non-STEC E. coli. 26. Method characterized by the fact that it comprises: (a) obtaining a plurality of nucleic acid sequences from a sample; (b) scanning, by a computer, of at least a fraction of said plurality of said nucleic acid sequences to a plurality of nucleic acid regions of one or more microorganisms selected from the group consisting of: a microorganism of the genus Salmonella, a microorganism of Campylobacter genus Campylobacter, a microorganism of the genus Listeria, and a microorganism of the genus Escherichia, where said scanning characterizes said one or more microorganisms with sensitivity above 99.5%. 27. Method characterized by the fact that it comprises: (a) the sequencing of a plurality of nucleic acid sequences from a food sample or from a sample of the environment associated with said food sample for a period of time; and (b) performing a test on said food sample or said environment associated with said food sample if said sequence for said period of time identifies a limit level of nucleic acid sequences of a microorganism in said food sample. 28. Method according to claim 27, characterized in that said time period is less than 30 minutes or less than 20 minutes. 29. Method according to claim 27, characterized in that said limit is not greater than 0.001%, 0.01%, 0.1%, or 1%, of the nucleic acid sequences of said microorganism. 30. Method characterized by the fact that it comprises: (a) obtaining a first plurality of nucleic acid sequences from a first sample of a food processing facility; (b) creating a data file on a computer that associates one or more of said first plurality of nucleic acid sequences with said food processing facility; (c) obtaining a second plurality of nucleic acid sequences from a second sample of said food processing facility; and (d) scanning a plurality of sequences from said second plurality of nucleic acid sequences to one or more sequences associated with said food processing facility in (b). 31. Method characterized by the fact that it comprises: (a) obtaining a first sample from a food processing facility; (b) sequencing said first sample from said food processing facility, thereby generating a first set of sequencing data from said food processing facility; (c) obtaining a second sample from said food processing facility; (d) sequencing said second sample from said food processing facility, thereby generating a second set of sequencing data from said food processing facility; and (e) comparing said second set of sequencing data with said first set of sequencing data; and (d) decontaminating said food processing facility if said comparison identifies a pathogenic microorganism in said food processing facility. 32. Method characterized by the fact that it comprises: (a) obtaining a first plurality of nucleic acid sequences from a first sample of a food processing facility; (b) obtaining a second plurality of nucleic acid sequences from a second food sample from said food processing facility; and (c) carrying out sequence alignments on a computer between said first plurality of nucleic acid sequences and said second plurality of nucleic acid sequences, thereby determining a similarity between said first sample and said second sample of said food processing facility. 33. Method characterized by the fact that it comprises: (a) the addition of a reagent to a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said food sample, thus forming a plurality of modified nucleic acid molecules, whereby said reagent: (i) modify a structure or interact with a plurality of nucleic acid molecules derived from one or more dead microorganisms; and (ii) does not modify a structure of a nucleic acid molecule derived from one or more living microorganisms; thereby providing a plurality of modified nucleic acid molecules; and (b) sequencing by a sequencing reaction of said plurality of modified nucleic acid molecules, thereby distinguishing one or more living organisms from said food sample or said environment sample associated with said food sample. 34. Method characterized by the fact comprising carrying out a pore sequencing reaction on a plurality of nucleic acid molecules from a food sample or from a sample of the environment associated with said food sample, whereby said sequencing reaction of pore distinguishes one or more nucleic acid molecules derived from a dead microorganism from one or more nucleic acid molecules derived from a living microorganism based on a methylation pattern or another epigenetic pattern from said one or more nucleic acid molecules of said dead microorganism. 35. Method according to claim 34, characterized in that said pore sequencing reaction is a nanopore sequencing reaction. 36. Method characterized by the fact that it comprises: (a) obtaining a plurality of nucleic acid sequences from a food sample or a sample from the environment of a food processing facility; (b) carrying out a first test on said plurality of nucleic acid sequences of said food sample, whereby said test provides for the presence or foresees the absence of a microorganism in said food sample; and (c) determining, based on said expected presence or said expected absence of said microorganism, from (b) whether it is necessary to perform a second test, so that a sensitivity of said second test is selected in order to determine a gender, a species, a serotype, a sub-serotype, or a strain of said microorganism. 37. Method characterized by the fact that it comprises: (a) the detection of the presence or absence of a microorganism in a sample or in an installation associated with said sample; and (b) prediction, by a computer system, of a risk presented by said installation based on said presence or said absence of said microorganism. 38. Method characterized by the fact that it comprises: (a) the addition of a first barcode to a first plurality of nucleic acid sequences in a sample, thereby providing a first plurality of nucleic acid sequences with barcodes ; and (b) carrying out a first sequencing reaction on said first plurality of barcode nucleic acid sequences, wherein said sequencing reaction is performed on a sequencing apparatus comprising a flow cell; (c) adding a second barcode to a second plurality of nucleic acid sequences from a second sample, thereby providing a second plurality of nucleic acid sequences with barcodes; and (d) carrying out a second sequencing reaction on said second plurality of barcode nucleic acid sequences, wherein said second sequencing reaction is performed on said sequencing apparatus comprising said flow cell, thereby reusing the said flow cell. 39. Nucleic acid sequencing apparatus characterized by the fact that it comprises: (a) a nucleic acid library preparation compartment comprising two or more cameras configured to prepare a plurality of nucleic acids from a sample for a sequencing reaction, where the said compartment is operationally connected to a nucleic acid sequencing camera; (b) a nucleic acid sequencing chamber, wherein said nucleic acid sequencing chamber comprises: (i) one or more flow cells comprising a plurality of pores or sequencing cartridges configured for the passage of a nucleic acid strand , where two or more of the one or more flow cells are juxtaposed; and (c) an automated platform, where said automated platform is programmed to robotically move a sample from said nucleic acid library preparation compartment to said nucleic acid sequencing chamber. 40. Method characterized by the fact that it comprises: (a) the addition of a first molecular index to a first plurality of nucleic acid sequences in a sample, thus providing a first plurality of indexed nucleic acid sequences; and (b) adding a second molecular index to said first plurality of nucleic acid sequences of said first sample, thereby providing a second plurality of indexed nucleic acid sequences; and (c) adding a third molecular index to said first plurality of nucleic acid sequences from said first sample, providing, thus, a third plurality of indexed nucleic acid sequences; (d) carrying out a sequencing reaction on said third plurality of nucleic acid sequences; and (e) demultiplexing, by a computer system, said third plurality of nucleic acid sequences comprising said first molecular index, said second molecular index, and said third molecular index. 41. Automated microfluidic device for the analysis of a test liquid characterized by the fact that it comprises: - a sensor provided in a detection camera; - a flow path comprising a detection camera inlet and a detection camera outlet that connect to the detection camera to, respectively, pass liquid into and out of the detection camera, and - a sample inlet port in fluid communication with the input; - a liquid collection channel downstream of the outlet; - an interruption of the flow path between the outlet of the detection camera and the liquid collection channel, preventing liquid from flowing into the upstream liquid collection channel, so that the device can be activated by completing the flow path between the sample inlet port and the liquid collection channel; - a conditioning liquid that fills the sample inlet port to interrupt the flow path in such a way that the sensor is covered by the liquid and not exposed to a gas or gas / liquid interface; where the device is configured in such a way that after activation of the device, the sensor remains not exposed to a gas or gas / liquid interface and the application of, respectively, one or more volumes of test liquid to a wet surface of the inlet provides sufficient liquid actuation force to introduce the one or more volumes of test liquid into the device and move the buffer liquid to the liquid collection channel; where the device further comprises a removable seal for the sample inlet port, where the removable seal has a body protruding at least 1 cm above the surface of the microfluidic device seated on the sample inlet port. 42. Device according to claim 41, characterized in that the removable seal protrudes at least 1, 2.0, 2.5, 3, or 3.5 cm above the surface of the device.