KR20220157773A - Bacteria identification apparatus and bacteria identification method - Google Patents

Abstract

An apparatus for identifying bacteria and a method for identifying bacteria are provided. The method for identifying bacteria comprises: (a) a step of converting raw sequence file fragments from sequences generated by next-generation sequencing equipment into one file; (b) a step of removing a specific primer sequence from the converted file using a bioinformatics analysis tool; (c) a step of removing sequence duplication and errors using an analysis tool in the file from which the specific primer sequence has been removed; (d) a step of identifying bacteria by comparing the sequences of the file from which duplicates and errors in the sequences have been removed with sequences stored in a database using a bioinformatics analysis tool, and (e) a step of analyzing the identified bacteria using a statistical tool.

Description

Bacterial identification device and method for identifying bacteria {BACTERIA IDENTIFICATION APPARATUS AND BACTERIA IDENTIFICATION METHOD}

The present invention relates to an apparatus for identifying bacteria and a method for identifying bacteria.

Predicting the taxonomic composition of metagenome samples over the past decade has been difficult. Being able to determine the microbial taxa contained in a given sample can provide many insights into the role of microbes in the environment. A more accurate and detailed classification is possible if new genomes released every year are added to the database and analyzed. However, this process requires a very large amount of complex calculations, and typically requires a large CPU cluster as it requires millions of reads from samples of thousands of reference genomes.

Because the number of publicly available genomes has increased in recent years, the reliability of the "k-mer perfect match" approach has become sufficiently high, and computer speeds to implement it have increased, making it a useful method. On the other hand, homology search methods are slow because of the large number of comparisons to be performed, and are imprecise because related genomes have a similar level of sequence organization. To avoid these inaccuracies and reduce computational time, some homology search methods use genetic markers (sequences that occur only once in several species or genera) to reduce the number of comparisons.

A disadvantage of this method using genetic markers is that the bacterial genome is highly irregular in size and gene frequency (some species or genera contain more markers than others), and that markers are recalculated when other species or genera are added to the reference database. is that you have to If an existing marker is found in a newly discovered, completely different taxon, that marker can no longer be used for the existing taxon.

Republic of Korea Patent Publication No. 10-2020-0027900 (published on March 13, 2020)

One object of the present invention is to provide a bacteria identification device and a method for identifying bacteria capable of identifying bacteria in an optimized method.

Another object of the present invention is to provide a bacterial identification device and a method for identifying bacteria capable of automatically identifying bacterial phylum or lower in intestinal microorganisms.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A method for identifying bacteria according to an embodiment of the present invention for solving the above problems includes the steps of (a) converting raw sequence file fragments from sequences generated by next-generation sequencing equipment into one file; (b) removing a specific primer sequence from the converted file using a bioinformatics analysis tool, (c) removing sequence duplication and errors using an analysis tool from the file from which the specific primer sequence was removed Step (d) identifying bacteria by comparing the sequences of the file from which duplicates and errors in the sequences have been removed with sequences stored in a database using a bioinformatics analysis tool; and (e) using a statistical tool to identify the bacteria. It includes the step of using and interpreting.

In an embodiment, the step (a) is characterized in that the nucleotide sequence fragments existing for each sample are produced or compressed into a single file and converted into a single file.

In an embodiment, the step (b) is characterized in that a specific primer sequence is removed by removing a primer sequence corresponding to a specific region of bacteria from one file converted in the step (a).

In an embodiment, in the step (c), it is characterized in that the position of the erroneous sequence is removed from the sequence error and duplication in the forward sequence and the reverse sequence.

In an embodiment, the step (c) is characterized in that sequence errors are removed through an Amplicon Sequence Variant (ASV) method.

In an embodiment, the sequence generated by the next-generation sequencing equipment is characterized in that it is a sequence file created by sequencing equipment.

In an embodiment, the sequences generated by the next-generation sequencing equipment are bacteria present in the human intestine.

In an embodiment, the sequence generated by the next-generation sequencing equipment is characterized in that it is a sequence generated through next-generation sequencing (NGS) analysis of bacteria extracted from feces excreted from the human intestine.

Bacterial identification device according to another embodiment of the present invention, the next-generation sequencing device for generating sequences through next-generation sequencing analysis and the sequence generated by the next-generation sequencing device are unprocessed base sequence file fragments into one. file, remove a specific primer sequence from the converted file using a bioinformatics analysis tool, remove duplicates and errors in the sequence using an analysis tool from the file from which the specific primer sequence has been removed, and a control unit for identifying bacteria by comparing the sequences of the file from which duplicates and errors have been removed with sequences stored in the database using a bioinformatics analysis tool, and analyzing the identified bacteria using a statistical tool.

The bacterial identification program according to another embodiment of the present invention for solving the above problems is combined with a computer as hardware and stored in a medium to perform any one of the above methods.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method may be further provided.

According to the present invention as described above, the present invention can provide an apparatus and method for identifying bacteria capable of identifying bacteria in an optimized way.

In addition, the present invention can provide a novel bacterial identification device and method capable of automatically identifying bacterial phylum or lower in intestinal microorganisms.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a conceptual diagram illustrating an apparatus for identifying bacteria according to an embodiment of the present invention.
2 is a flowchart illustrating a method for identifying bacteria according to an embodiment of the present invention.
3, 4, 5a and 5b, 6a and 6b, 7, 8, 9 and 10 are conceptual diagrams for explaining the bacterial identification method described in FIG. 2 .

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of the terms used in this specification will be briefly described. However, it should be noted that the description of terms is intended to help the understanding of the present specification, and is not used in the sense of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

In this specification, the 'bacteria identification device' includes all of various devices that can perform calculation processing and provide results to the user.

For example, the bacteria identification device is not only a computer, a terminal, a desktop PC, and a notebook (Note Book), but also a smart phone, a tablet PC, a cellular phone, and a PCS phone (Personal Communication). Service phone), synchronous/asynchronous IMT-2000 (International Mobile Telecommunication-2000) mobile terminal, Palm PC (Palm Personal Computer), Personal Digital Assistant (PDA), etc. may also be applicable.

In addition, the bacteria identification device may receive a request from a client and communicate with a server that performs information processing.

An apparatus for identifying bacteria according to an embodiment of the present invention may be implemented to include at least one of the components described in FIG. 1 .

1 is a conceptual diagram illustrating an apparatus for identifying bacteria according to an embodiment of the present invention.

The bacterial identification device according to an embodiment of the present invention may include a next-generation sequencing device 110 and a control unit 130.

The next generation sequencing device 110 may generate a sequence through next generation sequencing (NGS) analysis.

Here, the sequence generated by the next-generation sequencing equipment 110 may be a sequence file created by sequencing equipment.

In addition, sequences generated by the next-generation sequencing equipment 110 may be sequences of bacteria or bacteria present in the human intestine.

In addition, the sequence generated by the next generation sequencing equipment 110 may be a sequence generated through next generation sequencing (NGS) analysis of bacteria extracted from feces excreted from the human intestine.

2 is a flowchart illustrating a method for identifying bacteria according to an embodiment of the present invention.

Referring to FIG. 2 , the control unit 130 may convert unprocessed base sequence file fragments from sequences generated by next-generation sequencing equipment into one file (S210).

The control unit 130 may create or compress nucleotide sequence fragments existing for each sample into a single file and convert them into a single file.

The control unit 130 may remove a specific primer sequence from the converted file using a bioinformatics analysis tool (S220).

The controller 130 may remove a specific primer sequence by removing a primer sequence corresponding to a bacterial specific region from the converted file.

The control unit 130 may remove sequence duplication and errors using an analysis tool in the file from which the specific primer sequence has been removed (S230).

The control unit 130 may find the location of the erroneous sequence in the forward sequence and the reverse sequence and remove the sequence error and duplication.

Here, the control unit 130 may remove sequence errors through an Amplicon Sequence Variant (ASV) method. The ASV method may be one of the bioinformatics error correction methods.

ASV may refer to a single DNA sequence recovered from high-throughput marker gene analysis.

ASV can be used to group species based on DNA sequences, detect biological and environmental variation, and determine ecological patterns.

The ASV method is broadly applicable through algorithms that create error models tailored to individual sequencing runs, and use the models to discriminate between true biological sequences and those generated by errors.

The control unit 130 may identify bacteria by comparing the sequences of the file from which duplicates and errors in the sequences have been removed with the sequences stored in the database using a bioinformatics analysis tool (S240).

Here, the bioinformatics analysis tool may include a bioinformatics algorithm. The bioinformatics algorithm may be, for example, a Needleman-Wunch algorithm.

Contents of data as a result of identifying the bacteria may include read count, read percentage, and the like.

In addition, the bacteria identification result data is 7 data according to the taxa of species, genus, family, order, class, phylum, and kingdom. can be distinguished by

In addition, the control unit 130 may output bacteria identification result data in the form of a .tsv or .csv file.

The controller 130 may analyze the identified bacteria using a statistical tool (S250).

The statistical tool may include a phylogenetic tree, PCA, heatmap, and the like.

3, FIG. 4, FIG. 5, FIG. 6, FIG. 7, FIG. 8, FIG. 9 and FIG. 10 are conceptual diagrams for explaining the bacterial identification method described in FIG. 2.

Hereinafter, by applying the bacterial identification method according to FIG. 2, the contents of the bacterial identification experiment will be reviewed.

1) Production of experimental samples

In the present specification, a set of 35 microorganisms and fungi was prepared using 12 kinds of microorganisms and 2 kinds of molds generally distributed in the human intestine, and sequenced was performed. The type of strain is derived.

2) Criteria and Distinction between Forward and Reverse in ATP

Figure 3 is a graph of fastqc composed of bi-directional graphs of samples subjected to two amplicon sequencing. 3 is an example of a general Fastq, and the left side shows Forward and the right side shows reverse.

Referring to FIG. 4, comparing No_Filter (0-0), ATP (method of the present invention), F210-R170, Figaro (277-246) and VL-QIIME2 (283-237), the number of leads passing through the filter The number differs by more than two times depending on options and positions.

4 shows the number of leads passing through the filter, and the left side shows the number of leads passing through the filter based on the top of FIG. 3 and the right side shows the bottom graph of FIG. 3 .

5A and 5B show mapped areas and distributions. FIG. 5A shows the mapped area and distribution based on the upper graph of FIG. 3 and FIG. 5B shows the lower graph of FIG. 3 .

Even if a lot of data passes through the filter, the length of forward and reverse leads forming one common sequence is limited to a specific length, that is, 400 to 430 bp, as shown in FIG. 5 .

In the graph of FIG. 3 , the number of leads that passed the filter for F210-R170 is far greater, but as shown in the graph on the left of FIG.

6A and 6B show the number of leads that can determine the final genus. FIG. 6A shows the number of leads that can determine the final genus corresponding to the upper graph of FIG. 3 and FIG. 6B to the lower graph of FIG. 3 .

Common sequences of the rear part sequence of Forward and the front part of Reverse are created, and these common parts overlap each other to create a new long lead. Even if the maximum number of short leads for forward and reverse is secured, if these common parts do not overlap each other and create a new long lead, the ability to distinguish between species and genus is weakened.

For this reason, the present invention uses an ATP (Auto Truncation Position) method that can generate an appropriate common sequence between leads and has excellent discriminative power.

When this method is used, as shown in the left and right graphs of FIG. 3, the number of passing through the filter is not the maximum, but as shown in the result of FIG. 6, it can be seen that the number of species mapped is significantly higher.

The control unit 130 adjusts the overlapping size to 10 to 60, secures the maximum number of high-quality forward leads, and selects the length of reverse. It can be seen that the number of leads is large.

In addition, in the present invention, when the ATP method is used for the lower sample of FIG. 100 with low quality, it can be confirmed that 7.74% of the mapping reads are confirmed and the accuracy is increased as shown in FIG. 6.

3) Algorithm of ATP

The ATP algorithm is an algorithm that finds an optimal overlapping region by adjusting k-mer, quality score, and mismatch fraction.

Basically, it is an algorithm principle that finds an optimal region while performing windows shifting from the right side of the forward to the left side using a 17 bp K-mer.

The quality score selects areas with a basic score of 19 or higher, and the mismatch fraction selects only high-quality areas with a score of 0.3 or less to find an assembly, that is, an overlapping area.

7 shows an ATP algorithm for finding an optimal region by window shifting.

8 shows a flowchart for finding common reads using an overlap region.

Referring to FIG. 8 , the control unit 130 sets the r2 sequence as a reverse complementary sequence and generates a K-mer count table. The controller 130 may detect a sequence overlapping region and process an inconsistent basic region. In addition, the control unit 130 generates a consensus lead from the r1 and r2 leads.

4) Species and genus comparison experiments using ATP

In this specification, it can be confirmed that the ability to distinguish species and genus is far better than existing companies or programs by using the function of ATP (Auto Truncation Position) that automatically finds a truncation position according to the quality of the lead.

The results of the genus can be distinguished with an accuracy of 20% or more as shown in FIG. 9, and in the comparison of species in FIG.

According to the present invention as described above, the present invention can provide an apparatus and method for identifying bacteria capable of identifying bacteria in an optimized way.

In addition, the present invention can provide a novel bacterial identification device and method capable of automatically identifying bacterial phylum or lower in intestinal microorganisms.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

The operation and function of the bacteria identification device described above may be analogously applied in the same/similar manner to the method for identifying bacteria.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. Such codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related code for determining where (address address) of the computer's internal or external memory the additional information or media required for the computer's processor to execute the functions should be referenced. have. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the communication module of the computer to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (10)

In the method for identifying bacteria, performed by the device,
(a) converting raw sequence file fragments from sequences generated by next-generation sequencing equipment into a single file;
(b) removing a specific primer sequence from the converted file using a bioinformatics analysis tool;
(c) removing sequence duplication and errors using an analysis tool in the file from which the specific primer sequence has been removed;
(d) identifying bacteria by comparing the sequences of the file from which duplicates and errors have been removed with sequences stored in a database using a bioinformatics analysis tool; and
(e) a method for identifying bacteria comprising the step of analyzing the identified bacteria using a statistical tool. According to claim 1,
In step (a),
A method for identifying bacteria, characterized in that the nucleotide sequence fragments present in each sample are produced or compressed into a single file and converted into a single file. According to claim 2,
In step (b),
Bacterial identification method, characterized in that by removing a specific primer sequence by removing the primer sequence corresponding to the bacterial specific region from the one file converted in step (a). According to claim 3,
In step (c),
A method for identifying bacteria, characterized by eliminating sequence errors and redundancies by locating erroneous sequences in forward and reverse sequences. According to claim 4,
Wherein step (c) is characterized by removing sequence errors through an Amplicon Sequence Variant (ASV) method. According to claim 1,
The bacterial identification method, characterized in that the sequence generated by the next-generation sequencing equipment is a sequence file created by the sequencing equipment. According to claim 1,
The bacterial identification method, characterized in that the sequence generated by the next-generation sequencing equipment is a bacterium present in the human intestine. According to claim 1,
Characterized in that the sequence generated by the next generation sequencing equipment is a sequence generated by next generation sequencing (NGS) analysis of bacteria extracted from feces excreted from the human intestine. Next-generation sequencing devices that generate sequences through next-generation sequencing; and
From the sequences generated by next-generation sequencing equipment, unprocessed sequencing file pieces are converted into one file, and a specific primer sequence is removed from the converted one file using a bioinformatics analysis tool, and the specific primer sequence is removed. In the file from which the sequences have been removed, duplicates and errors in the sequence are removed using an analysis tool, and the sequences of the file from which the duplicates and errors have been removed are compared with the sequences stored in the database using a bioinformatics analysis tool to identify bacteria. Bacterial identification apparatus comprising a control unit for identifying and analyzing the identified bacteria using a statistical tool. A program that is combined with a computer, which is hardware, and stored in a recording medium to execute the method of any one of claims 1 to 8.

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