WO2018053443A1 - Méthode et système de caractérisations de panel - Google Patents

Méthode et système de caractérisations de panel Download PDF

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Publication number
WO2018053443A1
WO2018053443A1 PCT/US2017/052098 US2017052098W WO2018053443A1 WO 2018053443 A1 WO2018053443 A1 WO 2018053443A1 US 2017052098 W US2017052098 W US 2017052098W WO 2018053443 A1 WO2018053443 A1 WO 2018053443A1
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Prior art keywords
species
panel
genus
taxa
features
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PCT/US2017/052098
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English (en)
Inventor
Daniel Almonacid
Laurens KRAAL
Francisco OSSANDON
Juan Pablo CARDENAS
Jessica RICHMAN
Zachary APTE
Elisabeth BIK
Audrey Goddard
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uBiome, Inc.
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Priority claimed from US15/606,743 external-priority patent/US10803991B2/en
Priority to KR1020197010012A priority Critical patent/KR102486181B1/ko
Priority to CA3036994A priority patent/CA3036994A1/fr
Priority to EP17851735.5A priority patent/EP3512421A4/fr
Priority to SG11201901726QA priority patent/SG11201901726QA/en
Priority to EA201990520A priority patent/EA201990520A1/ru
Application filed by uBiome, Inc. filed Critical uBiome, Inc.
Priority to BR112019005025A priority patent/BR112019005025A8/pt
Priority to CN201780057144.2A priority patent/CN109715059A/zh
Priority to AU2017326564A priority patent/AU2017326564A1/en
Priority to JP2019514295A priority patent/JP7114091B2/ja
Publication of WO2018053443A1 publication Critical patent/WO2018053443A1/fr
Priority to ZA201901371A priority patent/ZA201901371B/en
Priority to CONC2019/0003713A priority patent/CO2019003713A2/es

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/04—Drugs for disorders of the alimentary tract or the digestive system for ulcers, gastritis or reflux esophagitis, e.g. antacids, inhibitors of acid secretion, mucosal protectants
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
    • A—HUMAN NECESSITIES
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    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/10—Laxatives
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/12—Antidiarrhoeals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/14—Prodigestives, e.g. acids, enzymes, appetite stimulants, antidyspeptics, tonics, antiflatulents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P13/00—Drugs for disorders of the urinary system
    • A61P13/12—Drugs for disorders of the urinary system of the kidneys
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/22—Anxiolytics
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P29/00—Non-central analgesic, antipyretic or antiinflammatory agents, e.g. antirheumatic agents; Non-steroidal antiinflammatory drugs [NSAID]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P3/00—Drugs for disorders of the metabolism
    • A61P3/04—Anorexiants; Antiobesity agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P3/00—Drugs for disorders of the metabolism
    • A61P3/08—Drugs for disorders of the metabolism for glucose homeostasis
    • A61P3/10—Drugs for disorders of the metabolism for glucose homeostasis for hyperglycaemia, e.g. antidiabetics
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P9/00—Drugs for disorders of the cardiovascular system
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
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    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
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    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G16B50/30—Data warehousing; Computing architectures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • Provisional Application serial number 62/147,362 filed 14-APR-2015
  • U.S. Provisional Application serial number 62/146,855 filed 13-APR-2015
  • U.S Provisional Application serial number 62/206,654 filed 18-AUG-2015, which are each incorporated in their entirety herein by this reference.
  • This application also claims the benefit of U.S. Provisional Application serial number 62/395,939, filed 16-SEP-2017, U.S. Provisional Application serial number 62/520,058, file 15-JUN-2017 and U.S. Provisional Application serial number 62/525,379, filed 27-JUN-2017, which are each incorporated in their entirety herein by this reference.
  • This invention relates generally to the field of microbiology and more specifically to a new and useful method and system for characterizing a panel of conditions in the field of microbiology.
  • FIGURES 1A-1B are flowchart representations of variations of an embodiment of a method for characterizing a panel of conditions;
  • FIGURE 2 is a flowchart representation of variations of an embodiment of a method for characterizing a panel of conditions;
  • FIGURE 3 is a schematic representation of an embodiment of a system
  • FIGURE 4 is a schematic representation of variations of an embodiment of a method.
  • FIGURE 5 is a schematic representation of processes in variations of a method for characterizing a panel of conditions
  • FIGURE 6 is a chart representation of an example of optimization parameters for determining target taxa
  • FIGURE 7 is a graph representation of an example of validation of a characterization process
  • FIGURE 8 is a chart representation of an example of healthy reference relative abundance ranges
  • FIGURES 9A-9B are examples of target taxa
  • FIGURE 10 is an example of selecting probiotics for characterizations
  • FIGURES 11-12 are examples of probiotics and associated taxonomic groups
  • FIGURE 13A-13B are examples of relative abundances associated with taxonomic groups related to probiotics.
  • FIGURES 14-15 are examples of interfaces.
  • embodiments of a system 200 for characterizing a panel (e.g., plurality) of conditions (e.g., gut-related conditions) associated with a set of taxa related to microorganisms can include a taxonomic database 205 including reference microbiome features (e.g., microbiome composition diversity features; microbiome functional diversity features; microbiome pharmacogenomics features; etc.) for the set of taxa associated with the panel of conditions; a handling system 210 (e.g., a sample handling system, etc.) operable to collect a container including biological material (e.g., nucleic acid material, etc.) from a user (e.g., a human subject, patient, animal subject, environmental ecosystem, care provider, etc.), the handling system 210 including a sequencer system operable to determine a microorganism sequence dataset for the user from the biological material; a panel characterization system 220 operable to: determine user microbiome features (e.g., relative abundance
  • the method 100 and/or system 200 can function to characterize, for a user, microbiome composition and/or microbiome functional diversity across a plurality of taxa (e.g., microorganisms across a plurality of species and genera) based on a biological sample of the user, in order to characterize a plurality of conditions associated with the plurality of taxa.
  • the method 100 and/or system 200 can function to substantially concurrently generate characterizations in a multiplex manner for a plurality of users based on a plurality of biological samples derived for the plurality of users.
  • the method 100 and/or system 200 can function in any manner analogous to that described in U.S. App. No.
  • the method 100 and/or system 200 can additionally or alternatively function to promote (e.g., provide) therapies (e.g., treatments, etc.) such as therapeutic measures to users for treating conditions of a panel of conditions (e.g., based on a panel characterization) and/or perform any suitable function.
  • therapies e.g., treatments, etc.
  • Variations of the system 200 and/or method 100 can further facilitate monitoring and/or adjusting of such therapies provided to a subject, for instance, through reception, processing, and analysis of additional samples from a subject throughout the course of therapy (e.g., for evaluating and/or improving a plurality of conditions from a panel).
  • the method 100 and/or system 200 can generate and/or promote characterizations and/or therapies for a panel of conditions including one or more of: symptoms, causes, diseases, disorders, microbiome pharmacogenomics profiles (e.g., describing resistance and/or susceptibility to antibiotics) and/or any other suitable aspects associated with the panel of conditions.
  • characterizations and/or therapies for a panel of conditions including one or more of: symptoms, causes, diseases, disorders, microbiome pharmacogenomics profiles (e.g., describing resistance and/or susceptibility to antibiotics) and/or any other suitable aspects associated with the panel of conditions.
  • the panel of conditions preferably includes a panel of gut-related conditions including any one or more of: flatulence, bloating, diarrhea, gastroenteritis, indigestion, abdominal pain, abdominal tenderness, constipation, infection, cancer, dysbiosis, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), ulcerative colitis, Crohn's disease, Celiac disease, bowel control problems (e.g., fecal incontinence), lactose intolerance, diverticulosis, diverticulitis, acid reflux (e.g., GER, GERD, etc.), Hirschsprung disease, abdominal adhesions, appendicitis, colon polyps, foodborne illnesses, gallstones, gastritis, gastroparesis, gastrointestinal bleeding, hemorrhoids, pancreatitis, ulcers, Whipple disease, Zollinger- Ellison syndrome, related conditions, and/ or any other suitable gut -related conditions.
  • IBS irritable bowel syndrome
  • IBD
  • the panel of conditions can include one or more of: probiotics-related conditions (e.g., associated with microorganism taxonomic groups included in, affected by, and/or otherwise related to taxonomic groups included in probiotics; treatable with one or more probiotics; etc.); vaginal-related conditions (e.g., human Papillomavirus infection, syphilis, cervical cancer, squamous intraepithelial lesions for high- and low-grade, sexually transmitted infection, cervicitis, pelvic inflammatory disease, bacterial vaginosis, aerobic vaginitis, idiopathic infertility, etc.); psychiatric and behavioral conditions (e.g., a psychological disorder; depression; psychosis; anxiety; etc.); communication-related conditions (e.g., expressive language disorder; stuttering; phonological disorder; autism disorder; voice conditions; hearing conditions; eye conditions; etc.); sleep-related conditions (e.g., insomnia, sleep
  • Microbiome analysis can enable accurate and efficient characterization and/or therapy provision for a panel of conditions caused by and/ or otherwise associated with microorganisms.
  • the technology can overcome several challenges faced by conventional approaches in characterizing and/or promoting therapies for a condition.
  • conventional approaches can require patients to visit one or more care providers to receive a characterization and/or a therapy recommendation for a condition, which can amount to inefficiencies and health-risks associated with the amount of time elapsed before diagnosis and/or treatment.
  • Second, conventional approaches can require a number of different diagnostic tests to be performed to characterize a panel of conditions, which can additionally amount to inefficiencies and health-risks.
  • conventional genetic sequencing and analysis technologies for human genome sequencing can be incompatible and/or inefficient when applied to the microbiome (e.g., where the human microbiome can include over 10 times more microbial cells than human cells; where optimal sample processing techniques can differ; where scaling sample processing procedures for characterizing a panel of conditions can be different; where the types of conditions can differ; where sequence reference databases can differ; where the microbiome can vary across different body regions of the user; etc.).
  • sequencing technologies e.g., next-generation sequencing
  • technological issues e.g., data processing issues, issues with processing in a multiplex manner, information display issues, microbiome analysis issues, therapy prediction issues, therapy provision issues, etc.
  • system 200 and the method 100 can confer technologically-rooted solutions to at least the challenges described above.
  • the technology can confer improvements in computer-related technology (e.g., modeling associated with characterizing and/ or promoting therapies for a panel of conditions; improving computational efficiency in storing, retrieving, and/or processing microorganism-related data for a panel of conditions; computational processing associated with biological sample processing; etc.) by facilitating computer performance of functions not previously performable.
  • the technology can computationally generate panel characterizations and/or associated recommended therapies based on techniques (e.g., leveraging microorganism taxonomic databases, etc.) that are recently viable due to advances in sample processing techniques and sequencing technology.
  • the technology can confer improvements in processing speed, panel characterization accuracy, microbiome-related therapy determination and promotion, and/or other suitable aspects in relation to a panel of conditions.
  • the technology can generate and apply feature-selection rules (e.g., microbiome diversity feature-selection rules for composition, function, pharmacogenomics, etc.) to select an optimized subset of features (e.g., microbiome composition diversity features such as reference relative abundance features indicative of healthy ranges of taxonomic groups associated with a panel of conditions; user relative abundance features that can be compared to the reference relative abundance features; etc.) out of a vast potential pool of features (e.g., extractable from the plethora of microbiome data such as sequence data) for generating and/or applying characterization models and/or therapy models.
  • feature-selection rules e.g., microbiome diversity feature-selection rules for composition, function, pharmacogenomics, etc.
  • an optimized subset of features e.g., microbiome composition diversity features such
  • microbiomes e.g., human microbiomes, animal microbiomes, etc.
  • the potential size of microbiomes can translate into a plethora of data, giving rise to questions of how to process and analyze the vast array of data to generate actionable microbiome insights in relation to a panel of conditions.
  • the feature-selection rules and/or other suitable computer-implementable rules can enable shorter generation and execution times (e.g., for generating and/or applying taxonomic databases; for determining panel characterizations and/or associated therapies; etc.), model simplification facilitating efficient interpretation of results, reduction in overfitting, improvements in data sources (e.g., for generating taxonomic databases, etc.), improvements in identifying and presenting panel condition insights in relation to the microbiome (e.g., through collecting and processing an increasing amount of data associated with an increasing number of users to improve predictive power of the technology), improvements in data storage and retrieval (e.g., storing specific models, microorganism sequences, features, and/or other suitable data in association with a user and/or set of users to improve delivery of personalized characterizations and/or treatments for panels of conditions, etc.), and other suitable improvements to facilitate rapid determination of characterizations and/or therapies.
  • shorter generation and execution times e.g., for generating and/or applying taxonomic databases; for
  • the technology can transform entities (e.g., users, biological samples, treatment systems including medical devices, etc.) into different states or things.
  • the technology can transform a biological sample into a panel characterization for a plurality of conditions.
  • the system 200 and/or method 100 can identify therapies to promote to a patient to modify a microbiome composition, microbiome functional diversity, a microbiome pharmacogenomics profile and/or other microbiome-related aspects to prevent and/or ameliorate one or more conditions of a panel of conditions, thereby transforming the microbiome and/or health of the patient.
  • the technology can transform a biological sample (e.g., through fragmentation, multiplex amplification, sequencing, etc.) received by patients into microbiome datasets, which can subsequently be transformed into features correlated with a panel of conditions, in order to generate panel characterization models and/or therapy models.
  • the technology can control treatment systems to promote therapies (e.g., by generating control instructions for the treatment system to execute), thereby transforming the treatment system.
  • the improvements in computer-related technology can drive transformations in the biological sample processing approaches, such as selecting a subset of primers compatible with genetic targets associated with a panel of conditions.
  • the technology can amount to an inventive distribution of functionality across a network including a taxonomic database, a sample handling system, a panel characterization system, and a plurality of users, where the sample handling system can handle substantially concurrent processing of biological samples (e.g., in a multiplex manner) from the plurality of users, which can be leveraged, along with the taxonomic database, by the panel characterization system in generating personalized characterizations and/or therapies (e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographics, other behaviors, preferences, etc.) for a panel of conditions.
  • personalized characterizations and/or therapies e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographics, other behaviors, preferences, etc.
  • the technology can improve the technical fields of at least computational modeling of a panel of conditions in relation to microbiome digital medicine, digital medicine generally, genetic sequencing, and/or other relevant fields.
  • the technology can leverage specialized computing devices (e.g., devices associated with the sample handling system, such as sequencer systems; panel characterization systems; treatment systems; etc.) in determining and processing microbiome datasets for characterizing and/or determining therapies for a panel of conditions.
  • the technology can, however, provide any other suitable benefit(s) in the context of using non- generalized computer systems for panel characterization and/or microbiome modulation.
  • the taxonomic database 205 of the system 200 can function to provide marker information associated with a panel of conditions and suitable for comparison to user microbiome features in generating one or more panel characterizations.
  • the taxonomic database 205 can store microorganism genetic sequences in association with a corresponding plurality of taxa, which can be stored in association with one or more corresponding conditions.
  • the taxonomic database 205 can store reference relative abundance ranges (e.g., associated with a healthy state for one or more conditions, associated with an unhealthy state, etc.) and/or other suitable microbiome features for microorganism taxonomic groups associated with the panel of conditions, where the reference microbiome features can be extracted based on a set of biological samples from a population of users (e.g., exhibiting one or more conditions of the panel of conditions; not exhibiting the conditions; etc.).
  • the taxonomic database 205 can store user relative abundance ranges (e.g., for a user with an unknown microbiome profile in relation to the panel of conditions; etc.) and/or other suitable user microbiome features.
  • the taxonomic database 205 preferably stores markers including any one or more of: genetic sequences (e.g., sequences identifying a taxonomic group; microorganism sequences; human sequences; sequences indicative of conditions from a panel of conditions; sequences that are invariant across a set of microorganism taxonomic groups and/or users; conserved sequences; sequences including mutations; sequences including polymorphisms; etc.); peptide sequences; targets; features (e.g., microbiome composition diversity features, microbiome functional diversity features, microbiome pharmacogenomics features, etc.); protein types (e.g., serum proteins, antibodies, etc.); carbohydrate types; lipid types; whole cell markers; metabolite markers; natural product markers; genetic predisposition biomarkers; diagnostic biomarkers; prognostic biomarkers; predictive biomarkers; other molecular biomarkers; gene expression markers; imaging biomarkers; markers corresponding to functional, structural, evolutionary, and/or other
  • Genetic sequences stored by the taxonomic database 205 preferably include one or more gene sequences for rRNA (e.g., a variable region of an rRNA gene sequence), which can include any one or more of: 16S, 18S, 30S, 40S, 50S, 60S, 5S, 23S, 5.8S, 28S, 70S, 80S, and/or any other suitable rRNA. Additionally or alternatively, genetic sequences can include and/or otherwise be associated with other RNA genes, protein genes, other RNA sequences, DNA sequences and/or any other suitable genetic aspects.
  • rRNA e.g., a variable region of an rRNA gene sequence
  • Different markers stored by the taxonomic database 205 preferably share a marker characteristic, which can include one or more of: conserved genetic sequences across the plurality of taxa (e.g., semi-conserved genetic sequences including a variable region; conserved sequences that can be targeted by primers for targeting a plurality of taxonomic groups associated with a panel of conditions; etc.), conserved peptide sequences, shared biomarkers, and/or any other suitable marker- associated information.
  • conserved genetic sequences across the plurality of taxa e.g., semi-conserved genetic sequences including a variable region; conserved sequences that can be targeted by primers for targeting a plurality of taxonomic groups associated with a panel of conditions; etc.
  • conserved peptide sequences shared biomarkers, and/or any other suitable marker- associated information.
  • Stored markers are preferably associated with a plurality of taxa, in order to enable mapping of user microorganism sequences (e.g., derived from a collected biological sample of a user, etc.) to particular taxa based on a comparison with stored markers (e.g., comparing user microorganism sequences to stored markers to find matches satisfying predetermined conditions; identifying taxa associated with the matched markers; and associating the taxa to the user microorganism sequences; etc.).
  • user microorganism sequences e.g., derived from a collected biological sample of a user, etc.
  • stored markers e.g., comparing user microorganism sequences to stored markers to find matches satisfying predetermined conditions; identifying taxa associated with the matched markers; and associating the taxa to the user microorganism sequences; etc.
  • Taxonomic groups in relation to the taxonomic database 205, a panel of conditions (e.g., gut-related conditions), other system components, and/or any portion of the system 200 and method 100 can include one or more of: Clostridium (genus), Clostridium difficile (species), Alistipes (genus), Alloprevotella (genus), Anaerofilum (genus), Bacteroides (genus), Barnesiella (genus), Bifidobacterium (genus), Blautia (genus), Butyricimonas (genus), Campylobacter (genus), Catenibacterium (genus), Christensenella (genus), Collinsella (genus), Coprococcus (genus), Dialister (genus), Eggerthella (genus), Escherichia-Shigella (genus), Faecalibacterium (genus), Flavonifr actor (genus), Fusobacterium (genus), Gelria (genus), Haemophil
  • taxonomic groups can include any described in U.S. App. No. 14/919,614, filed 21-OCT-2015.
  • markers stored in association with one or more of the plurality of taxa described above can include 16S rRNA genetic sequences associated with the plurality of taxa.
  • the markers and/or the plurality of taxa can be associated (e.g., positively associated, negatively associated, etc.) with one or more: conditions, pathogens, commensal bacteria, probiotic bacteria, and/ or any other marker-associated information.
  • the taxonomic database 205 can store markers (e.g., microorganism sequences, abundance features such as relative abundance ranges, microbiome composition diversity features, microbiome functional diversity features, other features, etc.), associated taxonomic groups, and/or other suitable data related to probiotics (and/or other suitable microorganism-related therapies).
  • markers e.g., microorganism sequences, abundance features such as relative abundance ranges, microbiome composition diversity features, microbiome functional diversity features, other features, etc.
  • associated taxonomic groups e.g., a panel of gut-related conditions and/or other suitable conditions, etc.
  • Food sources of probiotics can include: milk (e.g., raw cow milk), kefir, cheese (e.g., ovine cheese), cocoa, kimchi, yogurt, kombucha, sauerkraut, bee products, pickles, natto, pickles, fermented foods (e.g., fermented sausages), other probiotic foods, probiotic supplements (e.g., probiotic pills, commercial probiotics, etc.), and/or other suitable types of probiotics.
  • milk e.g., raw cow milk
  • cheese e.g., ovine cheese
  • cocoa e.g., ovine cheese
  • cocoa kimchi
  • yogurt e.g., kombucha
  • sauerkraut sauerkraut
  • bee products pickles, natto, pickles
  • fermented foods e.g., fermented sausages
  • probiotic supplements e.g., probiotic pills, commercial probiotics, etc.
  • taxonomic groups associated with probiotics, conditions, other system components, and/or any portion of the system 200 and method 100 can include one or more of: Bacillus coagulans (species), Bifidobacterium animalis (species), Clostridium butyricum (species), Lactobacillus brevis (species), Lactobacillus coryniformis (species), Lactobacillus fermentum (species), Lactobacillus helveticus (species), Lactobacillus rhamnosus (species), Streptococcus salivarius (species), Acetobacter nitrogenifigens (species), Azospirillum brasilense (species), Bacillus licheniformis (species), Bifidobacterium bifidum (species), Brevibacillus laterosporus (species), Clavibacter michiganensis (species), Enterococcus italic
  • the taxonomic database 205 can include markers for a specific set of taxonomic groups including Bacillus coagulans (species), Bifidobacterium animalis (species), Clostridium butyricum (species), Lactobacillus brevis (species), Lactobacillus coryniformis (species), Lactobacillus fermentum (species), Lactobacillus helveticus (species), Lactobacillus rhamnosus (species), and Streptococcus salivarius (species), where the markers (e.g., for the specific set of taxonomic groups, for any suitable set of taxonomic groups, etc.) can be leveraged in generating a panel characterization of probiotics-related microorganisms (e.g., composition characteristics, functional diversity characteristics) in relation to corresponding probiotics (e.g., as shown in FIGURES 14-15).
  • markers e.g., for the specific set of taxonomic groups, for any suitable
  • taxonomic group characterization associated with probiotics can include, for the taxonomic group of Pediococcus pentosaceus (species): found in raw cow milk, kimchi, sauerkraut, pickles; spherical shape; 0.5-1.0 micrometer size; non-spore forming; non- motile; non-flagellate; G+; lactic acid producer; used as start culture in different fermentations; and/or other suitable characteristics.
  • Pediococcus pentosaceus species
  • spherical shape 0.5-1.0 micrometer size
  • non-spore forming non-spore forming
  • non- motile non- motile
  • non-flagellate G+
  • lactic acid producer used as start culture in different fermentations
  • the taxonomic database can be leveraged for characterizing the specific set of taxonomic groups and/ or other suitable set of taxonomic groups in relation to a set of conditions, such as based on an inverse association with IBS, an inverse association with type 2 diabetes, an inverse association with obesity, an inverse association with IBD, an inverse association respiratory infection duration, an association with weight loss, and/or any suitable association (e.g., inverse association, positive association, etc.) with any suitable condition.
  • the taxonomic database 205 can be applied in relation to probiotics in any suitable manner.
  • the taxonomic database 205 can be generated, used for storage, retrieved from, determined, and/ or otherwise applied through performing portions of the method 100 (e.g., Block S110).
  • the taxonomic database 205 can include a set of reference relative abundance ranges (and/or other suitable reference microbiome features) derived from: determining a target set of taxa associated with a panel of conditions (e.g., gut-related conditions, etc.), determining a set of reference markers; and determining the set of reference relative abundance ranges for a set of taxa selected based on a comparison between the set of reference markers and the target set of taxa.
  • Determining the set of reference markers (and/or other reference microbiome features) can include determining the set of reference markers based on predicted reads derived from a set of primers selected based on a marker characteristic shared across a plurality of taxonomic groups (e.g., which can improve efficiency in sample processing for facilitating panel characterizations, where same or similar type of primers can be used to target markers across a plurality of taxonomic groups associated with a panel of conditions, etc.), where the comparison between the set of reference markers and the target set of taxa can include a sequence similarity between the predicted reads and reference microorganism sequences associated with the target set of taxa.
  • the handling system 210 of the system 200 can function to receive and process (e.g., fragment, amplify, sequence, etc.) biological samples.
  • the handling system 210 can additionally or alternatively function to provide and/or collect sample kits 250 (e.g., including containers configured for receiving biological material, instructions for users to guide a self-sampling process, etc.) for a plurality of users (e.g., in response to a purchase order for a sample kit 250), such as through a mail delivery system and/or other suitable process.
  • the sample kits 250 can include materials and associated instructions for a user to collect a sample (e.g., through cotton tip swabs; aspiration of fluids; biopsy; etc.) from one or more collection sites.
  • Collection sites can be associated with one or more of: the female genitals, the male genitals, the rectum, the gut, the skin, the mouth, the nose, any mucous membrane, and/or any other suitable sample providing site (e.g., blood, sweat, urine, feces, semen, vaginal discharges, tears, tissue samples, interstitial fluid, other body fluid, etc.), where any individual site or combination of sites can be correlated with any suitable taxonomic groups and/or associated conditions described herein.
  • suitable sample providing site e.g., blood, sweat, urine, feces, semen, vaginal discharges, tears, tissue samples, interstitial fluid, other body fluid, etc.
  • the handling system 210 can additionally or alternatively include a library preparation system operable to automatically prepare biological samples (e.g., fragment and/or amplify using primers compatible with nucleic acid sequences associated with the antibiotics-associated condition, such as in a multiplex manner, etc.) to be sequenced by a sequencer system (e.g., a next generation sequencing platform); and/or any suitable components.
  • a library preparation system operable to automatically prepare biological samples (e.g., fragment and/or amplify using primers compatible with nucleic acid sequences associated with the antibiotics-associated condition, such as in a multiplex manner, etc.) to be sequenced by a sequencer system (e.g., a next generation sequencing platform); and/or any suitable components.
  • the handling system 210 can be operable to determine a microorganism sequence dataset based on amplification of nucleic acids from biological material using a primer of a set of primers (e.g., selected through performing Block S110 and/or other suitable portions of the method 100, etc.), where the primer targets a microorganism sequence corresponding to a taxonomic group associated with one or more conditions of a panel of conditions (and/or one or more probiotics).
  • the handling system 210 can be configured in any manner and/or include components (e.g., sequencer systems) described in any manner analogous to U.S. App. No. 14/919,614, filed 21-OCT-2015. However, the handling system 210 and associated components can be configured in any suitable manner.
  • the panel characterization system 220 of the system 200 can function to determine and/or analyze microbiome datasets and/or supplementary datasets for characterizing and/or determining therapies for a panel of conditions (e.g., through performing portions of the method 100, etc.).
  • the panel characterization system 220 can obtain and/or apply computer-implemented rules (e.g., taxonomic database 205 generation rules; feature selection rules; model generation rules; user preference rules; data storage, retrieval, and/or display rules; microorganism sequence generation rules; sequence alignment rules; and/or any other suitable rules).
  • the panel characterization system 220 can be configured in any suitable manner.
  • the treatment system 230 of the system 200 functions to promote one or more treatments to a user (e.g., a human subject; a care provider facilitating provision of the treatment; etc.) for treating one or more conditions of the panel of conditions (e.g., reducing the risk of the conditions; improving states of the conditions; improving symptoms and/or other suitable aspects of the conditions; modifying a microbiome pharmacogenomics profile of a user towards a state susceptible to treatments for the conditions, etc.).
  • a user e.g., a human subject; a care provider facilitating provision of the treatment; etc.
  • conditions of the panel of conditions e.g., reducing the risk of the conditions; improving states of the conditions; improving symptoms and/or other suitable aspects of the conditions; modifying a microbiome pharmacogenomics profile of a user towards a state susceptible to treatments for the conditions, etc.
  • the treatment system 230 can include any one or more of: a communications system (e.g., to communicate treatment recommendations, such as through an interface 240, through notifying a care provider to recommend and/or provide the treatment; to enable telemedicine; etc.), an application executable on a user device (e.g., a gut -panel condition application for promoting treatments for gut-related conditions; a medication reminder application; an application operable to communicate with an automatic medication dispenser; etc.), consumable therapies such as supplemental probiotics (e.g., type, dosage, treatment schedule, amounts and types of taxonomic groups included, etc.), probiotic foods, antibiotics (e.g., type, dosage, medication schedule etc.), supplementary medical devices (e.g., medication dispensers; medication devices associated with antibiotic provision, etc.), user devices (e.g., including biometric sensors), and/or any other suitable component.
  • a communications system e.g., to communicate treatment recommendations, such as through an interface 240, through notifying a care provider to recommend and/
  • the treatment system 230 can be operable to facilitate provision of a consumable therapy based on the panel characterization, where the consumable therapy is operable to affect the user for at least one of a microbiome composition and a microbiome function associated with the condition (e.g., gut-related condition, etc.), in promoting improvement of a state of the condition.
  • the therapy can include a probiotics-related therapy for the condition, where the probiotics-related therapy is associated with a set of taxa (e.g., including taxonomic groups described herein, etc.), and where the treatment system 230 includes an interface 240 for promoting the probiotics-related therapy in association with a taxonomic group from the set of taxa.
  • One or more treatment systems 230 are preferably controllable by the panel characterization system 220.
  • the panel characterization system 220 can generate control instructions and/or notifications to transmit to the treatment system 230 for activating and/or otherwise operating the treatment system 230 in promoting therapies.
  • the treatment system 230 can be configured in any other manner. 3.5 System - Interface
  • the system 200 can additionally or alternatively include an interface 240 that can function to improve presentation of panel characterization information, probiotic-related information, and/or other suitable microbiome-related information in relation to, for example, panel characterizations, associated therapy recommendations, comparisons to other users, comparisons based on demographics and/or other user characteristics, microbiome composition diversity, microbiome functional diversity, microbiome pharmacogenomics, and/or other suitable aspects.
  • an interface 240 can function to improve presentation of panel characterization information, probiotic-related information, and/or other suitable microbiome-related information in relation to, for example, panel characterizations, associated therapy recommendations, comparisons to other users, comparisons based on demographics and/or other user characteristics, microbiome composition diversity, microbiome functional diversity, microbiome pharmacogenomics, and/or other suitable aspects.
  • the interface 240 can present panel characterization information including a microbiome composition (e.g., relative abundances of taxonomic groups), functional diversity (e.g., relative abundance of genes and/or other functional-related characteristics, etc.), and/or other suitable information for a panel of conditions (e.g., composition in relation to conditions of the panel, etc.).
  • panel characterization information, probiotic-related information, and/or other suitable information can be presented relative to a user subgroups sharing a characteristic (e.g., similar dietary behaviors, similar demographic characteristics, patients sharing conditions, smokers, exercisers, users on different dietary regimens, consumers of probiotics, antibiotic users, groups undergoing particular therapies, etc.).
  • the interface 240 can be operable to present antibiotics- related information including a change in the microbiome pharmacogenomics profile (and/or microbiome composition, microbiome functional diversity, etc.) over time in relation to the treatment and the antibiotics-associated condition.
  • the interface 240 can be operable to improve display of antibiotics-related information associated with the antibiotics-treatable condition and derived based on a comparison between a user microbiome pharmacogenomics profile for the user relative a user group sharing a demographic characteristic.
  • the interface 240 can promote (e.g., present, provide a notification, etc.) a therapy (e.g., a probiotics-related therapy) in association with a taxonomic group from the set of taxa (e.g., recommending a probiotic including microorganisms of a taxonomic group associated with a condition of the panel of conditions, etc.).
  • a therapy e.g., a probiotics-related therapy
  • the interface's display of microbiome-related information can be improved through selection (e.g., based on components of the panel characterization satisfying a threshold condition; a user microbiome profile matching a reference profile beyond a threshold similarity; a risk of a condition of a panel exceeding a threshold; other trigger events; etc.) and presentation of a subset of the microbiome-related information (e.g., highlighting and/or otherwise emphasizing a subset of the information).
  • the interface 240 can display any suitable information and can be configured in any suitable manner.
  • the system 200 and/or components of the system 200 can entirely or partially be executed by, hosted on, communicate with, and/or otherwise include: a remote computing system (e.g., a server, at least one networked computing system, stateless, stateful), a local computing system, databases (e.g., taxonomic database 205, user database, microbiome dataset database, panel of conditions database, treatment database, etc.), a user device (e.g., a user smart phone, computer, laptop, supplementary medical device, wearable medical device, care provider device, etc.), and/or any suitable component.
  • a remote computing system e.g., a server, at least one networked computing system, stateless, stateful
  • databases e.g., taxonomic database 205, user database, microbiome dataset database, panel of conditions database, treatment database, etc.
  • a user device e.g., a user smart phone, computer, laptop, supplementary medical device, wearable medical device, care provider device, etc.
  • the system 200 can include a computing system operable to communicate with the handling system 210 (e.g., a next generation sequencing platform of the handling system 210) to perform suitable portions of the method 100, such as determining microbiome pharmacogenomics data. While the components of the system 200 are generally described as distinct components, they can be physically and/or logically integrated in any manner.
  • a smartphone application can partially or fully implement the panel characterization system 220 (e.g., apply a panel characterization model to generate a panel characterization for a panel of conditions, such as in real-time; sequence biological samples; process microorganism sequences; extract features from microbiome datasets; etc.) and the treatment system 230 (e.g., communicate with a calendar application of the smartphone to notify the user to take probiotics according to the parameters determined by a probiotic therapy model, etc.).
  • the functionality of the system 200 can be distributed in any suitable manner amongst any suitable system components. However, the components of the system 200 can be configured in any suitable manner.
  • embodiments of a method 100 for characterizing a panel of conditions based on processing a biological sample can include: generating a taxonomic database associated with markers for a plurality of taxa S110; generating a microbiome dataset (e.g., a microorganism sequence dataset including microorganism sequences, etc.) for a user based on a biological sample collected from the user S120; and/or performing a characterization process for at least one of microbiome composition, microbiome functional diversity, and/ or associated conditions (e.g., determining a panel characterization for a panel of conditions), based on the taxonomic database and the microbiome datasets (and/or supplementary datasets and/or other suitable data) S130.
  • a taxonomic database associated with markers for a plurality of taxa S110
  • generating a microbiome dataset e.g., a microorganism sequence dataset including microorganism sequences, etc.
  • a characterization process for at least one of microbio
  • the method 100 can additionally or alternatively include: collecting a supplementary dataset informative of the panel of conditions S125; promoting a therapy for the user based on the characterization process S140; determining a probiotics-related characterization S145; validating the characterization process S150; and/or any other suitable processes.
  • Blocks of the method 100 can be repeatedly performed in any suitable order to enable refining of the taxonomic database (e.g., through identifying new markers associated with different taxa and/or conditions, etc.), refining of the characterization process (e.g., through updating reference abundances used to compare against user relative abundances of targets for identifying clinically relevant results; through generation and updating of characterization models; through increasing the number of conditions that can be characterized using a single biological sample; etc.), the therapy process (e.g., through monitoring and modulating microbiome composition with therapies over time such as through iteratively performing Blocks S120 and S130 over time, where the therapies can be selected based on characterization results possessing sensitivity, specificity, precision, and negative predictive value; etc.), and/or other suitable processes.
  • the therapy process e.g., through monitoring and modulating microbiome composition with therapies over time such as through iteratively performing Blocks S120 and S130 over time, where the therapies can be selected based on characterization results possessing sensitivity, specific
  • One or more instances of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., in parallel; multiplexing to enable processing of multiple biological samples in parallel; computationally characterizing different conditions concurrently on different threads for parallel computing to improve system processing ability; etc.), in temporal relation to a trigger event, and/ or in any other suitable order at any suitable time and frequency by and/or using one or more instances of the system (e.g., including a sample handling network, a panel characterization system, a therapy system, sample kits, etc.), elements, and/ or entities described herein.
  • the system e.g., including a sample handling network, a panel characterization system, a therapy system, sample kits, etc.
  • data described herein can be associated with any suitable temporal indicators (e.g., seconds, minutes, hours, days, weeks, etc.; temporal indicators indicating when the data was collected, determined and/or otherwise processed; temporal indicators providing context to content described by the data, such as temporal indicators indicating a state of a panel of conditions at the time at which the biological sample was collected; etc.) and/or change in temporal indicators (e.g., microbiome features over time; microbiome composition diversity, functional diversity, and/or other suitable aspects over time; change in data; data patterns; data trends; data extrapolation and/or other prediction; etc.).
  • the method can be performed in any suitable manner. 4-1 Method - Generating a taxonomic database.
  • Block Sno recites: generating one or more taxonomic databases associated with markers for a plurality of taxa, which can function to create a database including marker information suitable for comparison to user microorganism sequences in generating one or more characterizations.
  • Generating a taxonomic database Sno preferably includes determining a set of reference markers for the taxonomic database (e.g., based on predicted reads derived from primers selected based on a shared marker characteristic across a plurality of taxa; etc.); determining a target list of taxa (e.g., associated with gut-related conditions); filtering the target list of taxa based on a comparison (e.g., sequence comparison) against the reference markers (e.g., while using optimization parameters); and storing, at the taxonomic database, the filtered taxa (e.g., as shown in FIGURES 9A-9B) in association with corresponding reference markers.
  • a set of reference markers for the taxonomic database e.g., based on predicted reads derived from primers selected based on a shared marker characteristic across a plurality of taxa; etc.
  • determining a target list of taxa e.g., associated with gut-related conditions
  • filtering the target list of taxa
  • Block S110 determining the set of reference markers is preferably based on one or more primers (e.g., primers to be used in amplification of genetic material from biological samples, as in Block S120, etc.).
  • Block S110 can include: predicting amplicons based on primers (e.g., V4 primers GTGCCAGCMGCCGCGGTAA for forward, and GGACTACHVGGGTWTCTAAT for reverse, etc.) allowing annealing satisfying a threshold condition (e.g., up to 2 mismatches over the entire sequence) for comparison to sequences from a reference database (e.g., SILVA database); filtering the amplicons based on degeneracy (e.g., filtering out degenerate amplicons that expand to more than 20 possible non- degenerate sequences); modifying the filtered amplicons to represent a forward read (e.g., including the forward primer and I25bp to the 3' end of the forward primer, etc.
  • a threshold condition
  • determining a target list of taxa preferably includes processing condition-related information sources (e.g., third-party information sources such as scientific literature, clinical tests, etc.; sources including information regarding conditions, associated microorganisms, and/or associated markers, etc.).
  • condition-related information sources e.g., third-party information sources such as scientific literature, clinical tests, etc.; sources including information regarding conditions, associated microorganisms, and/or associated markers, etc.
  • Block S110 can include manually processing condition-related information sources (e.g., with human curation of markers and/or associated information, etc.) to generate the target list of taxa.
  • Block S110 can include automatically processing condition-related information sources.
  • Block S110 can include: generating a list of online information sources; obtaining the online information sources based on the list; processing the online information sources to extract a set of taxa, associated conditions, and/or other associated data (e.g., through applying natural language processing techniques, etc.) for generating the target list of taxa.
  • Determining the target list of taxa preferably includes filtering the target list of taxa based on a comparison with the set of reference markers.
  • Block S110 can include associating reference markers from the set of reference markers to taxa from the target list of taxa, such as based on a performing a sequence similarity search using 100% identity over 100% of the length of a genetic sequence associated with one or more taxa from the plurality of taxa (e.g., a 16S rRNA gene V4 region for a taxa), against the set of reference markers.
  • any suitable identity parameter, length parameter, and/or other suitable parameters can be applied to a sequence similarity search, and associating reference markers with taxa can be performed in any suitable manner.
  • Reference markers for different taxa of a preliminary target list are preferably filtered according to optimization parameters (e.g., optimizing for sensitivity, specificity, precision, negative predicting value, and/or other metrics, such as through using confusion matrices, etc.).
  • optimization parameters e.g., optimizing for sensitivity, specificity, precision, negative predicting value, and/or other metrics, such as through using confusion matrices, etc.
  • taxa from the preliminary target list can be filtered based on an optimization parameter threshold (e.g., requiring each of the optimization parameters to exceed 90%; requiring precision of over 95%; etc.).
  • Block S120 can include: generating a plurality of sub-databases associating a given taxa to different numbers of reference markers (e.g., sequences), resulting in different optimization parameter profiles.
  • Block S110 can include: accepting a first subset of reference markers unambiguously corresponding to a taxa; ranking reference markers from a second subset of reference markers based on a quotient of dt/ti, where "ti" represents an annotation of the sequence to a taxa of interest, and "dt" represents an annotation of the sequence to a different taxa; generating a set of sub-databases for a taxa based on different quotient conditions (e.g., a sub-database optimized for specificity based on a quotient condition of o; a sub-database optimized for identifying true positives based on a quotient condition of 100); determining sets of optimization parameters for the set of sub- databases; filtering the preliminary target list of taxa based on sub-databases for the taxa corresponding to optimization parameters satisfying the optimization parameter thresholds; and storing the filtered taxa in association with the corresponding reference markers at the taxonomic database.
  • determining the target list of taxa can be performed in any suitable manner.
  • generating the taxonomic database can include identifying reference markers and associated taxa based on processing biological samples received from a population of users in relation to supplementary datasets received from the population of users (e.g., determining correlations with self-reported conditions for the users based on microbiome composition features and/or microbiome functional diversity features derived from biological samples collected from the users), but determining reference markers corresponding to target taxa can be performed in any suitable manner.
  • generating a taxonomic database can be performed in any suitable manner.
  • Block Si20 recites: generating one or more microbiome datasets (e.g., a microorganism sequence dataset including microorganism sequences, etc.) for one or more users (e.g., a current subject for determining a panel characterization; a population of subjects for generating the taxonomic database; etc.) based on biological samples collected from the plurality of users.
  • Block S120 functions to process biological samples collected from users in order to determine microorganism sequences that can be subsequently processed based on the taxonomic database (e.g., performing a sequence comparison between the microorganism sequences and genetic sequences stored at the taxonomic database) to determine characterizations for the users.
  • Block S120 can include any one or more of: lysing a biological sample (e.g., in conjunction with using stabilization buffer, etc.), disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample (e.g., extracting microorganism DNA with a column-based approach using a liquid-handling robot, etc.), purification of nucleic acids (e.g., DNA) in a biological sample, amplification (e.g., with a library preparation system) of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, sequencing of amplified nucleic acids of the biological sample (e.g., in a pair-end modality on a NextSeq platform to generate 2 x lsobp pair-end sequences; etc.), and/or any other suitable sample processing operations, such as those described in relation to U.S. App. No. 15/374,890
  • amplification of purified nucleic acids can include one or more of: polymerase chain reaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and/or any other suitable amplification technique.
  • PCR polymerase chain reaction
  • HDA helicase-dependent amplification
  • LAMP loop mediated isothermal amplification
  • NASBA nucleic acid sequence based amplification
  • SDA strand displacement amplification
  • RCA rolling circle amplification
  • LCR ligase chain reaction
  • the primers used are preferably selected to prevent or minimize amplification bias, and/or configured to amplify nucleic acid regions/sequences (e.g., of the 16S region, the 18S region, the ITS region, etc.) associated with markers stored in the taxonomic database (e.g., amplifying genetic sequences that can be compared to markers in the taxonomic database, in Block S130; amplifying genetic sequences corresponding to marker characteristics; amplifying genetic sequences informative taxonomically, phylogenetically, for diagnostics, for formulations such as for probiotic formulations; etc.), and/or configured for any other suitable purpose.
  • nucleic acid regions/sequences e.g., of the 16S region, the 18S region, the ITS region, etc.
  • markers stored in the taxonomic database e.g., amplifying genetic sequences that can be compared to markers in the taxonomic database, in Block S130; amplifying genetic sequences corresponding to marker
  • Block S120 can include amplifying 16S genes (e.g., genes coding for 16S rRNA) with universal V4 primers (e.g., 515F: GTGCCAGCMGCCGCGGTAA and 806R: GGACTACH VGGGTWTCTAAT) , other suitable primers associated with variable (e.g., semi-conserved hypervariable regions, etc.) regions (e.g., V1-V8 regions), and/or any other suitable portions of RNA genes.
  • Block S120 can include selecting primers associated with protein genes (e.g., coding for conserved protein gene sequences across a plurality of taxa, etc.).
  • primers used in variations of Block S120 can additionally or alternatively include incorporated barcode sequences specific to each biological sample, which can facilitate identification of biological samples post-amplification.
  • Selected primers can additionally or alternatively be associated with conditions, microbiome composition features (e.g., identified primers compatible with a genetic target corresponding to microbiome composition features associated with a group of taxa correlated with flatulence; genetic sequences from which relative abundance features are derived etc.), functional diversity features, supplementary features, and/or other suitable features.
  • Primers can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., Illumina Sequencing).
  • Primers can possess any suitable size (e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.). Additionally or alternatively, any suitable number of primers can be used in sample processing for performing characterizations (e.g., panel characterizations, probiotic-related characterizations, etc.), where the primers can be associated with any suitable number of targets, sequences, taxa, conditions, and/or other suitable aspects. Primers used in Block S120 and/or other suitable portions of the method 100 can be selected through processes described in Block S120 (e.g., primer selection based on parameters used in generating the taxonomic database) and/or any other suitable portions of the method 100.
  • Block S120 can include, in relation to sequence reads, one or more of: filtering, trimming, appending, clustering, labeling (e.g., as the actual genetic sequence; as an error; etc.).
  • Block S120 can include generating a set of reads based on amplification of the 16S gene; filtering the reads using an average Q-score > 30; trimming primers and leading bases from the reads; appending forward and reverse reads; clustering using a distance of 1 nucleotide (e.g., with the Swarm algorithm); labeling the most abundant read sequence per cluster as the actual genetic sequence; for each cluster, assigning the most abundant read sequence with a count corresponding to the number of reads in the cluster; and, for each cluster, performing chimera removal on the most abundant read sequence (e.g., using a VSEARCH algorithm, etc.).
  • sequencing can be performed in any suitable manner.
  • Block S120 can include barcoding a plurality of samples with forward and reverse indexes (e.g., unique combinations), sequencing the plurality of samples in a multiplex manner; and, after sequencing, demultiplexing the samples corresponding to different users (e.g., with a BCL2FASTQ algorithm, etc.). Additionally or alternatively, any number of instances of portions of Block S120 can be performed at any suitable time and frequency. However, Block S120 can be performed in any suitable manner analogous to U.S. Application No. 15/374,890 filed 09-DEC-2016, which is herein incorporated in its entirety by this reference, and/ or can be performed in any suitable manner.
  • Block S125 recites: receiving a supplementary dataset informative of a panel of conditions and/or probiotics-related information.
  • Block S125 can function to acquire additional data associated with one or more users of a set of users, which can be used to train and/or validate the characterization process (e.g., characterization models) generated in Block S130, the therapy process (e.g., therapy models) in Block S140, and/or any other suitable processes.
  • the characterization process e.g., characterization models
  • the therapy process e.g., therapy models
  • the supplementary dataset preferably includes survey-derived data, but can additionally or alternatively include any one or more of: diagnostic-related data (e.g., celiac disease testing, colonoscopy, sigmoidoscopy, lower GI series, upper GI endoscopy, upper GI series, virtual colonoscopy, etc.), contextual data derived from sensors and/or any other suitable components (e.g., components of the system 200, which can include treatment devices, user devices such as smartphones, wearable medical devices, etc.), medical data (e.g., current and historical medical data, such as antibiotics medical history), data informative of one or more conditions of a panel (e.g., indications of presence or absence of the conditions, associated diagnoses, associated treatments, progress over time, etc.), and/or any other suitable type of data.
  • diagnostic-related data e.g., celiac disease testing, colonoscopy, sigmoidoscopy, lower GI series, upper GI endoscopy, upper GI series, virtual colonoscopy, etc
  • Block S125 the survey-derived data can provide physiological, demographic, and behavioral information in association with a subject. Additionally or alternatively, Block S125, can be performed in any manner analogous to U.S. Application No. 14/919,614, filed 21-OCT-2015, which is incorporated in its entirety by this reference. However processing supplementary datasets Block S125 can be performed in any suitable manner.
  • Block S130 recites: performing a characterization process for at least one of microbiome composition, microbiome functional diversity, and/or associated conditions, based on the taxonomic database and the microbiome datasets.
  • Block S130 can function to process microbiome datasets (e.g., generated in Block S120) in relation to the taxonomic database (e.g., generated in Block S110) to generate one or more characterizations for the users. Characterizations for the user can include any characterizations analogous to those described in U.S. App. No.
  • Block S130 can include one or more of: determining a reference microbiome parameter range (e.g., a healthy reference relative abundance range such as shown in FIGURE 8, where the range can be associated with the absence of one or more conditions; a risky reference relative abundance range associated with the presence of and/ or risk of one or more conditions; microorganism composition range for abundance of one or more taxa; microorganism functional diversity range for functional features associated with one or more taxa; etc.); determining a user microbiome parameter for a user; generating a characterization for the user based on a comparison between the user microbiome parameter and the reference microbiome parameter range (e.g., characterizing a user as possessing an unhealthy microbiome composition in relation to Prevotella based on the user microbiome parameter indicating a Prevotella abundance outside of the healthy reference range for Prevotella; etc.) and/or any other suitable operations.
  • a reference microbiome parameter range e.g., a healthy reference relative abundance range such as shown in FIG
  • Reference microbiome parameter ranges can have any suitable lower- and upper-limits (e.g., a lower-limit above 0% for a relative abundance of Ruminococcus).
  • Reference microbiome parameter ranges can include ranges representing any suitable confidence intervals (e.g., 99% confidence intervals across a population of users).
  • reference relative abundance ranges can be calculated for any suitable taxa (e.g., from the target list of taxa), such as based on dividing the count of reads corresponding to that taxa by the total number of reads (e.g., total number of clustered and filtered reads); however, reference relative abundance ranges can be calculated in any suitable manner.
  • Block S130 preferably includes determining one or more panel characterizations for one or more panels of conditions (e.g., a panel of gut-related condition, etc.).
  • Panel characterizations can include, for one or more conditions of the panel, one or more of: presence of conditions, absence of conditions, risk of conditions, severity of conditions, recommendations associated with the conditions, microbiome composition associated with the conditions (e.g., microbiome composition diversity including relative abundances of taxonomic groups associated with the conditions), microbiome functional diversity associated with the conditions, microbiome pharmacogenomics (e.g., pharmacogenomics profile of the user for potential efficacy of different antibiotics for the conditions) associated with the conditions, probiotics (e.g., sources, associated taxonomic groups, correlations, etc.) associated with the conditions, and/or any other suitable aspects related to panels of conditions.
  • probiotics e.g., sources, associated taxonomic groups, correlations, etc.
  • Block S130 can include collecting biological samples and supplementary datasets from a population of users.
  • the population of users can include users associated with any suitable state of microbiome composition, microbiome functional diversity, conditions, and/or other suitable characteristics, where the supplementary datasets (e.g., digitally administered surveys at an application executing on mobile devices associated with the users) can be informative of the characteristics.
  • the supplementary dataset can inform conditions including one or more of: cancer, infection, obesity, chronic health issues, mental health disorders, and/or any other suitable condition.
  • the method 100 can include: processing biological samples from a population of healthy users (e.g., users never diagnosed with high blood sugar and/or diabetes, gut-related symptoms, and/or other conditions, etc.); processing the biological samples (e.g., as in Block S120) to determine microorganism sequences; determining relative abundance of each taxa (e.g., from the target list of taxa) for each user; and generating healthy ranges for each of the taxa based on the relative abundances across the population of healthy users.
  • a population of healthy users e.g., users never diagnosed with high blood sugar and/or diabetes, gut-related symptoms, and/or other conditions, etc.
  • processing the biological samples e.g., as in Block S120
  • determining relative abundance of each taxa e.g., from the target list of taxa
  • healthy ranges for each of the taxa based on the relative abundances across the population of healthy users.
  • the method 100 can include: determining the set of reference relative abundance ranges for the set of taxa includes: collecting a set of supplementary biological samples and a set of supplementary datasets for a population of users; processing the set of supplementary biological samples to generate a supplementary microorganism sequence dataset using a set of primers associated with the panel of microorganism-related conditions; and determining the set of reference relative abundance ranges based on the supplementary microorganism sequence dataset and the set of supplementary datasets.
  • empirically determining reference microbiome parameter ranges can be performed in any suitable manner.
  • determining reference microbiome parameter ranges can be performed non-empirically, such as based on manually and/or automatically processing condition- related information sources.
  • determining reference microbiome parameter ranges can be performed in any suitable manner.
  • determining a user microbiome parameter for a user is preferably based on generated microorganism sequences derived from biological samples of the user (e.g., as in Block S120; clustered and filtered reads; etc.). For example, determining a user microbiome parameter can include determining a relative abundance for different taxa (e.g., identified in the target list of taxa).
  • determining user microbiome parameters can include extracting panel- associated features (e.g., as shown in FIGURE 4), which can include one or more of: microbiome composition features, microbiome functional features, microbiome pharmacogenomics features, and/or other suitable features associated with one or more conditions of the panel, such as in a manner analogous to U.S. Application No. 15/374,890 filed 09-DEC-2016, which is herein incorporated in its entirety by this reference.
  • the method 100 can include: extracting a set of panel- associated features for the user based on the microorganism sequence dataset; determining a comparison between the reference features and the set of panel- associated features for the user; determining a panel characterization for the user for the panel of microorganism-related conditions based on the comparison.
  • the method 100 can include: extracting a set of panel-associated features including extracting microbiome composition diversity features and microbiome functional diversity features of the set of panel-associated features based on the microorganism sequence dataset, and where determining the comparison includes determining the comparison of the reference features with the microbiome composition diversity features and the microbiome functional diversity features.
  • the method 100 can include: determining reference microbiome parameter ranges from values of microbiome composition features and/or microbiome functional diversity features (e.g., derived from biological samples of healthy users, etc.); and comparing the user microbiome composition feature values and/or user microbiome functional diversity feature values to the reference microbiome parameter ranges to determine characterizations for the user (e.g., for conditions positively and/or negatively associated with the reference microbiome parameter ranges).
  • reference microbiome parameter ranges from values of microbiome composition features and/or microbiome functional diversity features (e.g., derived from biological samples of healthy users, etc.); and comparing the user microbiome composition feature values and/or user microbiome functional diversity feature values to the reference microbiome parameter ranges to determine characterizations for the user (e.g., for conditions positively and/or negatively associated with the reference microbiome parameter ranges).
  • comparing one or more user microbiome parameters to one or more reference microbiome parameter ranges associated with one or more characteristics can include characterizing the user as possessing or not possessing the characteristic based on whether the user microbiome parameter values fall inside or outside the reference microbiome parameter ranges.
  • Block S130 can include deriving a healthy reference relative abundance range for a Methanobrevibacter smithii; and characterizing the user as at risk of irritable bowel syndrome in response to the user having a relative abundance of Methanobrevibacter smithii exceeding the healthy reference relative abundance range.
  • determining a comparison between the reference features and a set of panel-associated features can include determining the set of panel-associated features as associated with at least one of: presence of microbiome composition features, absence of the microbiome composition features, relative abundance for taxonomic groups of the set of taxa, a ratio between at least two features associated with the set of taxa, interactions between the taxonomic groups, and phylogenetic distance between the taxonomic groups.
  • generating the taxonomic database can include determining a set of reference relative abundance ranges for the set of taxa, where the set of reference relative abundance ranges is associated with the panel of microorganism-related conditions; extracting a set of user relative abundance ranges for the set of taxa based on a microorganism sequence dataset for the user; and determining a comparison between the set of reference relative abundance ranges and the set of user relative abundance ranges.
  • determining a comparison between the reference features and the set of panel-associated features can include performing at least one of: a prediction analysis, multi hypothesis testing, a random forest test, and principal component analysis. However, comparing one or more user microbiome parameters can be performed in any suitable manner.
  • performing the characterization process can be based on thresholds (e.g., determining risk of a panel of conditions based on relative abundances of a set of taxa in relation to a set of thresholds associated with the condition, etc.), weights (e.g., weighting relative abundance of a first taxa more heavily than relative abundance of a second taxa, such as when the first taxa has a greater correlation with the condition of interest, etc.), machine learning models (e.g., a classification model trained on microbiome features and corresponding labels for taxa stored in the taxonomic database; etc.), computer-implemented rules (e.g., feature- engineering rules for extracting microbiome features; model generation rules; user preference rules; microorganism sequence generation rules; sequence alignment rules; etc.), and/or any other suitable aspects.
  • thresholds e.g., determining risk of a panel of conditions based on relative abundances of a set of taxa in relation to a set of thresholds associated with the condition, etc.
  • weights
  • performing the characterization process can be configured as measuring at least one of the following: a risk score, and/or a significance index to associate a taxon or a set of taxa with a condition (or group of conditions) of interest in any manner analogous to that described in U.S. Provisional Application serial number 62/558,489 filed 14-SEP-2017, which is herein incorporated in its entirety by this reference.
  • Block S130 can be performed in any suitable manner.
  • Block S130 and/or other suitable portions of the method 100 can include applying one or more models (e.g., panel characterization models; probiotics characterization models; therapy models; etc.) including one or more of: probabilistic properties, heuristic properties, deterministic properties, and/or any other suitable properties.
  • Each model can be run or updated: once; at a predetermined frequency; every time an instance of an embodiment of the method and/or subprocess is performed; every time a trigger condition is satisfied (e.g., detection of audio activity in an audio dataset; detection of voice activity; detection of an unanticipated measurement; etc.), and/or at any other suitable time frequency.
  • the module(s) can be run or updated concurrently with one or more other models, serially, at varying frequencies, and/ or at any other suitable time.
  • Each model can be validated, verified, reinforced, calibrated, or otherwise updated based on newly received, up-to-date data; historical data or be updated based on any other suitable data.
  • models and/or associated aspects e.g., approaches, algorithms, etc.
  • Block S130 can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S140, which recites: promoting a therapy based on the characterization process (e.g., based on panel characterizations, based on probiotics-related characterizations, based on features; etc.).
  • Block S140 can function to determine, recommend, and/or provide a personalized therapy to the user, in order to modulate the microbiome composition and/or functional features of the user toward a desired equilibrium state, and/or to improve one or more conditions.
  • Block S140 can include promoting a probiotic consumable to the user based on the panel characterization (and/or probiotics-related characterization), where the probiotic consumable is operable to improve a plurality of the microorganism-related conditions of the panel of microorganism-related conditions.
  • the method 100 can include collecting a diet-associated supplementary dataset associated with a dietary behavior of the user, where promoting the probiotic consumable includes promoting the probiotic consumable to the user based on the diet-associated supplementary dataset and the panel characterization (and/ or probiotic characterization.
  • Therapies can include any one or more of: probiotics, consumables (e.g., food items, beverage items, etc.), topical therapies (e.g., lotions, ointments, antiseptics, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, etc.), medications, antibiotics, bacteriophages, and any other suitable therapeutic measure.
  • Characterizations generated in Block S130 can be used to determine and/or promote a customized therapy, such as including formulation and regimen (e.g., dosage, usage instructions), to the user.
  • the method 100 can include: determining a user relative abundance for a taxa outside a health reference relative abundance range for the taxa; and promoting probiotics and/or other suitable therapies for modulating the microbiome composition of the user to achieve a user relative abundance within the health reference relative abundance range.
  • Block S140 can include determining and/or providing therapies configured to correct dysbiosis characteristics (e.g., identified based on characterizations determined in Block S130, etc.).
  • Block S140 can include determining and/or providing therapies with one or more therapy systems, which can include any one or more of: a communications system (e.g., to communicate therapy recommendations; to enable telemedicine; etc.; etc.), an application executable on a user device (e.g., gut-related condition application for promoting proper care of the gut, etc.), supplementary medical devices (e.g., treatment devices and/or diagnostic devices for gut-related conditions, medication dispensers, probiotic dispensers, etc.), user devices (e.g., including biometric sensors), and/or any other suitable component.
  • Block S140 can additionally or alternatively include generate control instructions and/or notifications for the therapy system for activating and/or otherwise operating the therapy system in association with promoting the therapy.
  • using therapy systems for performing Block S140 can be performed in any suitable manner.
  • Block S140 can include generating and/or providing notifications (e.g., a microbiome report for a patient, as shown in FIGURE 5) to a user regarding the therapies, the characterizations generated in Block S130, and/or any other suitable information.
  • notifications e.g., a microbiome report for a patient, as shown in FIGURE 5
  • Block S140 can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S145: determining a probiotics-related characterization.
  • Block S145 can function to process microbiome datasets (e.g., generated in Block S120) in relation to the taxonomic database (e.g., probiotics-related information included in the taxonomic database, etc.) to generate one or more probiotics-related characterizations for users. Additionally or alternatively, Block S145 can function to facilitate determination of panel characterizations upon which probiotic-related therapies can be based (e.g., determined and/or promoted).
  • Block S145 can include any one or more of: determining probiotic sources, determining taxonomic groups associated with probiotics, determining conditions (e.g., of a panel) associated with probiotics, generating characterizations describing probiotics-related information described herein and/or other suitable information, determining probiotics-related features (e.g., upon which characterizations and/ or therapies can be based; etc.), and/ or any other suitable processes.
  • Block S145 can include: identifying potential probiotics; filtering the potential probiotics based on comparing characteristics of the probiotics to performance metrics associated with the probiotics; identifying probiotic- related conditions (e.g., health benefits, sources of probiotics, taxonomic groups associated with the probiotics); and performing a second filtering of the probiotics based on a comparison with the probiotic-related conditions.
  • probiotic-related conditions e.g., health benefits, sources of probiotics, taxonomic groups associated with the probiotics
  • the method 100 can include: determining ranges (e.g., relative abundance ranges; healthy ranges; etc.) for probiotic strains (e.g., that can be identified reliably with analytical performance metrics, such as through performing one or more processes described herein); correlating the ranges (e.g., reference ranges) to one or more conditions; determining user ranges for a user; comparing the user ranges to the reference ranges; and/or determining therapies based on the comparisons.
  • Taxonomic groups associated with probiotics can include any suitable taxonomic groups described herein (e.g., in relation to the taxonomic database, etc.). However, Block S145 can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S150, which recites: validating the characterization process.
  • Block S150 can function to validate the process used in generating one or more characterizations (e.g., as in Block S130) for a user based on microbiome datasets and the taxonomic database, in order to facilitate accurate determination of user microbiome parameters and/or reference microbiome parameter ranges (e.g., for relative abundances of a target taxa).
  • Validating the characterization process preferably includes performing one or more of Blocks S110- S140 in relation to reference samples (e.g., with known microbiome composition and/or microbiome functional diversity, such as in relation to the target list of taxa, etc.).
  • Block S150 can include generating reference samples based on diluting genetic material (e.g., to any suitable ratio) associated with target taxa (e.g., synthetic genetic material such as synthetic double-stranded DNA representative of the V4 region of the i6S rRNA gene for different target taxa, as shown as "sDNA" in FIGURE 7, etc.); and processing the reference samples by performing one or more of Blocks S110-S140 to verify detection of target taxa associated with the reference samples.
  • diluting genetic material e.g., to any suitable ratio
  • target taxa e.g., synthetic genetic material such as synthetic double-stranded DNA representative of the V4 region of the i6S rRNA gene for different target taxa, as shown as "sDNA" in FIGURE 7, etc.
  • Block S150 can include processing reference samples derived from real or synthetic biological samples (e.g., stool samples with live or recombinant material of known composition, as shown as "Verification Samples" in FIGURE 7; etc.) to verify detection of target taxa associated with the reference samples. Additionally or alternatively, Block S150 can include modifying one or more parameters of associated with one or more of Blocks S110-S140 based on the results of validating the characterization process. However, Block S150 can be performed in any suitable manner.
  • reference samples derived from real or synthetic biological samples e.g., stool samples with live or recombinant material of known composition, as shown as "Verification Samples" in FIGURE 7; etc.
  • Block S150 can include modifying one or more parameters of associated with one or more of Blocks S110-S140 based on the results of validating the characterization process.
  • Block S150 can be performed in any suitable manner.
  • the method 100 and/or system of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof.
  • Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer-executable components integrated by computer- executable components integrated with apparatuses and networks of the type described above.
  • the computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.
  • the computer-executable component can be a processor, though any suitable dedicated hardware device can (alternatively or additionally) execute the instructions.
  • the FIGURES illustrate the architecture, functionality and operation of possible implementations of compositions, methods, and systems according to preferred embodiments, example configurations, and variations thereof. It should also be noted that, in some alternative implementations, the functions noted can occur out of the order noted in the FIGURES. For example, aspects shown in succession may, in fact, be executed substantially concurrently, or the aspects may sometimes be executed in the reverse order, depending upon the functionality involved.

Abstract

Certains modes de réalisation d'un système et d'une méthode de caractérisation d'un panel de conditions associées à un ensemble de taxa relatifs à des micro-organismes peuvent faire appel à une base de données taxinomique comprenant des caractéristiques de microbiome de référence concernant l'ensemble de taxa associé au panel de conditions ; un système de manipulation pouvant servir à collecter un contenant comprenant un matériel biologique provenant d'un utilisateur, le système de manipulation comprenant un système séquenceur pouvant servir à déterminer un ensemble de données de séquences de micro-organismes ; et un système de caractérisation de panel pouvant servir à : déterminer des caractéristiques de microbiome d'utilisateur de l'ensemble de taxa pour l'utilisateur sur la base de l'ensemble de données de séquences de micro-organismes, générer une comparaison entre les caractéristiques de microbiome d'utilisateur et les caractéristiques de microbiome de référence, et déterminer une caractérisation de panel du panel de conditions pour l'utilisateur sur la base de la comparaison ; et un système de traitement pouvant servir à promouvoir une thérapie pour une condition du panel de conditions sur la base de la caractérisation de panel.
PCT/US2017/052098 2016-09-16 2017-09-18 Méthode et système de caractérisations de panel WO2018053443A1 (fr)

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