A novel method for integrating single-cell genome and metagenomic sequences

Abstract

To provide a new treatment method for integrating a single-cell genome sequence and a metagenome sequence.SOLUTION: A method for analyzing a sequence of an organism unit population including a plurality of unit organism units includes the steps of: A) performing metagenome analysis to the organism unit population to obtain a metagenome fragment sequence; B) performing single-cell genome analysis to the organism unit population to obtain a single-cell genome fragment sequence derived from the plurality of unit organism units; C) severally performing reconstruction, separation and / or integration of an original genome sequence longer than a lead and / or chimera removal regarding A) and B); and D) performing a combined analysis using sequences which at least include metagenome fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstruction genome sequence.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present disclosure provides a novel processing method, system, and related technology for integrating single-cell genome sequences and metagenomic sequences, specifically, an analysis system for constructing high-quality genome sequences by combining and analyzing single-cell genome sequences and metagenomic sequences. [Background technology]

[0002] Progress is being made in compiling microbial genome data, but much of the current data is based on metagenomic information, which is insufficient in both quality and quantity when analyzing complex bacterial flora.

[0003] Although it has become possible to obtain some genetic information (such as genome information) for single biological units, there is a growing need for higher quality genetic information. Summary of the Invention [Means for solving the problem]

[0004] The present disclosure provides a method for determining a high-quality genome sequence of a single organism unit (e.g., a bacterial strain) included in an organism unit population from an organism unit population including multiple unit organism units such as a microbial community. In metagenomics analysis, there is a problem with accurate separation of mixed genome sequences, and in single-cell genome analysis, there is a problem with amplification bias. The present disclosure is characterized in that both single-cell genome analysis and metagenomics analysis are performed on an organism unit population including the same multiple unit organism units, and the quality of the genome sequence obtained is improved by mutually utilizing the analysis results obtained therefrom. It is possible to provide a genome sequence of higher quality than the genome sequence obtained by each method alone.

[0005] Examples of embodiments of the present disclosure include the following: (Item 1) A method for analyzing a sequence of a population of organism units comprising a plurality of unit organism units, comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, sorting and / or integrating and / or removing chimeras from original genome sequences longer than the reads for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; A method comprising: (Item 2) The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, The method according to item 1. (Item 3) 3. The method according to item 1 or 2, wherein the combination analysis comprises fractionating a metagenomic fragment sequence or a sequence derived therefrom with a single-cell genome fragment sequence or a sequence derived therefrom. (Item 4) The method according to any one of items 1 to 3, wherein the combination analysis comprises integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom. (Item 5) The method according to any one of Items 1 to 4, wherein the combination analysis comprises mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing a metagenomic fragment sequence, thereby dividing the single-cell genome fragment sequence into a sequence obtained by reconstructing a metagenomic fragment sequence, identifying the aligned single-cell genome fragment sequence as a chimeric sequence, and removing the chimeric sequence as necessary. (Item 6) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) a step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, respectively, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The method according to any one of items 1 to 5, comprising: (Item 7) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The method according to any one of items 1 to 6, comprising: (Item 8) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) A step of reconstructing the original genome sequence longer than the read for B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The method according to any one of items 1 to 7, comprising: (Item 9) A program for causing a computer to implement a method for analyzing a sequence of a population of organism units including a plurality of unit organism units, the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, separating, integrating and / or removing chimeras from original genome sequences longer than the fragment sequences for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, the program. (Item 10) The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, Item 9. The program according to item 9. (Item 11) 11. The program according to item 9 or 10, wherein the combination analysis includes fractionating a metagenomic fragment sequence or a sequence derived therefrom using a single-cell genome fragment sequence or a sequence derived therefrom. (Item 12) 12. The program according to any one of items 9 to 11, wherein the combination analysis includes integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom. (Item 13) 13. The program according to any one of Items 9 to 12, wherein the combination analysis comprises mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing the metagenomic fragment sequence, thereby dividing the single-cell genome fragment sequence into a sequence obtained by reconstructing the metagenomic fragment sequence, and identifying the aligned single-cell genome fragment sequence as a chimeric sequence, and removing the chimeric sequence as necessary. (Item 14) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) a step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, respectively, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; 14. The program according to any one of items 9 to 13, comprising: (Item 15) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; 15. The program according to any one of items 9 to 14, comprising: (Item 16) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) A step of reconstructing an original genome sequence longer than the fragment sequence for B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; 16. The program according to any one of items 9 to 15, comprising: (Item 17) A recording medium storing a program for causing a computer to implement a method for analyzing a sequence of a population of organism units including a plurality of unit organism units, the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, separating, integrating and / or removing chimeras from original genome sequences longer than the fragment sequences for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; A recording medium including: (Item 18) The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, Item 18. The recording medium according to item 17. (Item 19) 19. The recording medium according to item 17 or 18, wherein the combination analysis comprises fractionating a metagenomic fragment sequence or a sequence derived therefrom using a single-cell genome fragment sequence or a sequence derived therefrom. (Item 20) 20. The recording medium according to any one of items 17 to 19, wherein the combination analysis comprises integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom. (Item 21) 21. The recording medium according to any one of Items 17 to 20, wherein the combination analysis comprises mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing the metagenomic fragment sequence, thereby dividing the single-cell genome fragment sequence into a sequence obtained by reconstructing the metagenomic fragment sequence, and identifying the aligned single-cell genome fragment sequence as a chimeric sequence, and removing the chimeric sequence as necessary. (Item 22) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) a step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, respectively, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; 22. The recording medium according to any one of items 17 to 21, comprising: (Item 23) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; 23. The recording medium according to any one of items 17 to 22, comprising: (Item 24) The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) A step of reconstructing an original genome sequence longer than the fragment sequence for B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; 24. The recording medium according to any one of items 17 to 23, comprising: (Item 25) A system for analyzing a sequence of a population of organism units including a plurality of organism units, the system comprising: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) a sequence processing unit that performs reconstruction, fractionation, integration and / or chimera removal of original genome sequences longer than the metagenome fragment sequences and single-cell genome fragment sequences, respectively; D) An improved genome sequence generation unit that performs a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, the system. (Item 26) The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, 26. The system according to item 25. (Item 27) 27. The system of claim 25, wherein the combined analysis comprises fractionating a metagenomic fragment sequence or a sequence derived therefrom with a single-cell genome fragment sequence or a sequence derived therefrom. (Item 28) 28. The system according to any one of items 25 to 27, wherein the combination analysis comprises integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom. (Item 29) 29. The system according to any one of Items 25 to 28, wherein the combination analysis comprises mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing the metagenomic fragment sequence, thereby dividing the single-cell genome fragment sequence into a sequence obtained by reconstructing the metagenomic fragment sequence, identifying the aligned single-cell genome fragment sequence as a chimeric sequence, and removing the chimeric sequence as necessary. (Item 30) The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) a reconstruction unit that reconstructs an original genome sequence longer than the fragment sequence for each of A) and B); C-2) A step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, respectively, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; and C-3) a generation unit that generates a fractionated fragment sequence by referring to the metagenomic fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence; D) an improved genome sequence generating unit that combines the separated fragment sequence with a sequence separated from a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; 30. The system according to any one of items 24 to 29, comprising: (Item 31) The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) a reconstruction unit that reconstructs an original genome sequence longer than the fragment sequence for each of A) and B); C-2) an integration unit that integrates the sequences obtained by reconstructing the single-cell genome fragment sequences to generate an integrated sequence of the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) A generating unit that generates a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) an improved genome sequence generation unit that combines a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The system according to any one of items 25 to 30, comprising: (Item 32) The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) a reconstruction unit that reconstructs an original genome sequence longer than the fragment sequence in B); C-2) an integration unit that integrates the sequences obtained in C-1) to generate an integrated sequence of sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A generator that reconstructs an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) an improved genome sequence generation unit that combines a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The system according to any one of items 25 to 31, comprising: Effect of the Invention

[0006] This disclosure makes it possible to obtain high-quality genome sequences that are highly complete and have low levels of contamination. Compared to general binning methods, the number of mismatches to the reference genome is smaller, and the F value, which is an index of measurement accuracy of the reference genome, is also high. It also shows a high rate of obtaining ribosomal RNA, which is difficult to obtain by simple binning in metagenomic analysis. It is considered to be effective in detecting differences in gene function due to mutations such as single nucleotide polymorphisms. [Brief description of the drawings]

[0007] [Figure 1] Figure 1 shows an overview of the single-cell genome / metagenome cross-reference. Data is obtained from the microbiome using metagenomics and single-cell genome analysis methods. By mutually utilizing the data obtained from each method, such as reads and assembled sequences, it is possible to obtain genome sequences of higher quality than those obtained by each method alone. [Diagram 2]Figure 2 is a schematic diagram of how high-quality genomes are obtained by reassembling metagenomic reads that have been sorted by single-cell genome reads, using the integrated sequence as a guide. Data is obtained from the microbiome using metagenomic analysis and single-cell genome analysis methods. Assembled sequences and integrated sequences are obtained from the single-cell genome reads. Assembled sequences are also obtained from the metagenomic reads. The metagenomic assembled sequence is associated with the single cell by mapping the single-cell genome read to the metagenomic assembled sequence, and a fractionated sequence sorted by the single cell is obtained. Furthermore, fractionated metagenomic reads (fractionated fragment sequences) are obtained by mapping the metagenomic read to the fractionated sequence. The fractionated metagenomic reads are reassembled using the single-cell integrated sequence as a guide to obtain a high-quality genome sequence. [Diagram 3] Figure 3 is a schematic diagram of how a high-quality genome is obtained by matching each assembled sequence. An assembled sequence is obtained from each metagenomic read and single-cell genome read. Each assembled sequence is mapped to obtain the correspondence (separation of metagenomic assembled sequences into fractionated sequences). A high-quality genome sequence is obtained by combining the single-cell genome integrated sequence with the corresponding metagenomic assembled sequence. [Figure 4] Figure 4 is a schematic diagram of how high-quality genomes are obtained by sorting metagenomic reads into single-cell genome assembled sequences and integrated sequences. Assembled sequences and integrated sequences are obtained from single-cell genome reads. Metagenomic reads are mapped to these sequences to obtain fractionated metagenomic reads. Each fractionated metagenomic read is assembled and combined with the corresponding single-cell genome assembled sequence and integrated sequence to obtain a high-quality genome sequence. [Diagram 5]Figure 5 shows the results obtained in Example 1, and plots the integrated index (average z-score, where the contamination level is multiplied by -1) of the completeness and contamination level of genome sequences analyzed by five metagenomic analysis methods (CONCOCT, MaxBin2, MetaBAT2, DAS_Tool, metaWARAP) and the method of the present disclosure (hybrid_contig). The method of the present disclosure showed higher values ​​for completeness and contamination level than existing methods. [Figure 6] Figure 6 shows the results obtained in Example 1, and shows the integrated index (average z-score of rRNA length and number of tRNA types) of 5S, 16S, 23S ribosomal RNA and transfer RNA extracted by five metagenomic analysis methods (CONCOCT, MaxBin2, MetaBAT2, DAS_Tool, metaWARAP) and the method disclosed herein (hybrid_contig). The method disclosed herein showed a tendency to obtain higher rRNA and tRNA than existing methods. [Figure 7] FIG. 7 shows the results obtained in Example 1, and is a plot of the F values ​​of genome sequences analyzed by five metagenomic analysis methods (CONCOCT, MaxBin2, MetaBAT2, DAS_Tool, metaWARAP) and the method of the present disclosure (hybrid_contig). The F value is the harmonic mean of the precision and recall of the acquired genome sequence against the reference genome sequence. The method of the present disclosure showed a higher F value than existing methods. [Figure 8] Figure 8 shows the results obtained in Example 1, which plots the number of mismatches per 100 kb when the genome sequences analyzed by five metagenomic analysis methods (CONCOCT, MaxBin2, MetaBAT2, DAS_Tool, metaWARAP) and the method of the present disclosure (hybrid_contig) were mapped to the reference genome. The method of the present disclosure showed a lower number of mismatches than existing methods. [Figure 9]Figure 9 shows the results obtained in Example 2, comparing the integrated sequence of the single-cell genome (csag) with the genome sequence (hybrid) combined with the metagenomic assembled sequence. While the total genome size of both is almost the same, the number of contigs is smaller in the hybrid genome sequence. Accordingly, the N50, which is one of the evaluation indices for assembled sequences, is higher in the hybrid. [Figure 10] Figure 10 shows an example of detecting and removing chimeric sequences from single-cell genome reads using a metagenomic assembled sequence. An assembled sequence is obtained from the metagenomic read. The single-cell genome read is mapped to this assembled sequence. If the single-cell genome read is a chimeric sequence, the entire length of the read is not mapped, so this is used to identify the chimeric sequence. Single-cell genome reads identified as chimeric sequences are split at the chimeric junctions and reused as multiple reads. [Figure 11] 11 shows the results obtained in Example 4, showing the accuracy of chimeric sequence detection by the conventional method and the method of the present disclosure. Compared to the conventional method, the number of chimeric sequences that can be recognized has increased, and the accuracy rate has also improved. [Figure 12] Figure 12 shows the results obtained in Example 2, comparing the integrated index of genome sequence completeness and contamination level, and the index of rRNA and tRNA acquisition, of the integrated sequence of the single-cell genome (csag) with the genome sequence (hybrid) combined with the metagenomic assembly sequence. Both indices were equivalent, without any decrease, in the genome sequence (hybrid) combined with the metagenomic assembly sequence. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The present disclosure will be described below while showing the best mode. Throughout this specification, the expression of the singular form should be understood to include the concept of the plural form, unless otherwise specified. Therefore, the singular article (for example, in the case of English, "a", "an", "the", etc.) should be understood to include the concept of the plural form, unless otherwise specified. In addition, it should be understood that the terms used in this specification are used in the sense commonly used in the field, unless otherwise specified. Therefore, unless otherwise defined, all technical terms and scientific and technical terms used in this specification have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. In case of conflict, the present specification (including definitions) will take precedence.

[0009] (definition, etc.) The following provides definitions of terms particularly used in this specification and / or explains basic technical content as appropriate.

[0010] In this specification, the term "unitary biological unit" refers to a unit having genetic information or other biomolecular information. The unitary biological unit may include, but is not limited to, a cell, a cell-like structure, a virus, etc., and may also include artificially produced ones (so-called artificial cells) and digital cells (provided as information). Specifically, an individual unitary biological unit may correspond to a single cell.

[0011] As used herein, the term "biological unit population" refers to a population composed of multiple unit biological units, and examples thereof include, but are not limited to, microbial communities, cell populations, and virus populations, as well as populations of artificially produced cells (so-called artificial cells) and digital cells (provided as information).

[0012] As used herein, a "cell" refers to any particle that contains molecules containing genetic information and is capable of replicating (whether independently or not). As used herein, a "cell" includes cells of unicellular organisms, bacteria, cells derived from multicellular organisms, fungi, and the like.

[0013] As used herein, the term "cell-like structure" refers to any particle that contains molecules that have genetic information. As used herein, the term "cell-like structure" includes intracellular organelles such as mitochondria, nuclei, and chloroplasts, as well as viruses.

[0014] As used herein, the term "collection" refers to an assembly containing two or more single biological units, cells, or cell-like structures. As used herein, the term "collection" may be used interchangeably with "population."

[0015] As used herein, a "subpopulation," when used in conjunction with a "population," refers to a portion of a population that has a smaller number of single biological units, cells, or cell-like structures than the population.

[0016] As used herein, "nucleic acid information" refers to the information of nucleic acids contained in a cell or cell-like structure, including the presence or absence of a particular gene sequence, the yield of a particular gene, or the total nucleic acid yield.

[0017] As used herein, "identity" refers to the similarity of structure or sequence between two biomolecules. When the subject is a sequence, identity can also be determined by comparing the positions in each sequence that can be aligned for comparison.

[0018] As used herein, the term "single organism level" refers to processing the genetic information or other biomolecular information contained in one single organism in a state in which it can be distinguished from the genetic information or other biomolecular information contained in other single organisms.

[0019] As used herein, the term "single cell level" refers to processing the genetic information or other biomolecular information contained in one cell or cell-like structure in a state where it is distinguished from the genetic information or other biomolecular information contained in other cells or cell-like structures. For example, when amplifying a polynucleotide at the "single organism unit level" or "single cell level", the amplification is performed in a state where the polynucleotide in a single organism unit, or a cell or cell-like structure, is distinguishable from the polynucleotide in another single organism unit, or another cell or cell-like structure. In one embodiment of the present disclosure, the step of contacting the polynucleotide with an amplification reagent to amplify the polynucleotide in a gel capsule can also amplify the polynucleotide while maintaining the gel state in the gel capsule.

[0020] As used herein, "unit-organism-based analysis" refers to the analysis of genetic information or other biomolecular information contained in one single biological unit (e.g., a cell or cell-like structure) in a manner that distinguishes it from genetic information or other biomolecular information contained in other single biological units (e.g., cells or cell-like structures).

[0021] In this specification, "single cell genome analysis" refers to the analysis of genetic information contained in a single cell or cell-like structure in a state where it is distinguished from genetic information contained in unit organisms such as other cells or cell-like structures, and the target is typically a genome sequence, but is not limited to genomes in the narrow sense, and any genetic information can be the target. In other words, when the term "genome" is used in this specification, it includes not only genomes in the narrow sense (a set of chromosomes containing a minimum group of genes necessary for an organism to carry out normal life activities), but also those that code for genetic information that does not constitute chromosomes.

[0022] As used herein, "metagenomic analysis" refers to the analysis of genetic information contained in a single biological unit, such as a plurality of cells or cell-like structures present in a specific environmental unit (which may be, but is not limited to, soil, animal intestines, etc.), without distinguishing it from genetic information contained in other cells or cell-like structures. Metagenome does not refer to the genome or genetic body of a single type of cell or cell-like structure, but refers to the genome or genetic body of all species in an environmental unit. The subject is typically a genome sequence, but is not limited to genomes in the narrow sense, and any genetic information may be the subject.

[0023] As used herein, the term "single-cell genome fragment sequence" refers to a polynucleotide sequence containing a portion of a genome sequence obtained by single-cell genome analysis, particularly by sequencing in a single cell. It may also be referred to as a "single-cell genome read" or a "single-cell genome read sequence."

[0024] As used herein, the term "metagenomic fragment sequence" refers to a polynucleotide sequence containing a part of a genome sequence obtained by metagenomic analysis, particularly sequencing. It may also be referred to as a "metagenomic read" or a "metagenomic read sequence."

[0025] As used herein, "reconstruction of an original genome sequence longer than a fragment sequence" refers to constructing a sequence longer than a fragment sequence by linking multiple fragment sequences using a common sequence between the fragment sequences. As used herein, "reconstruction" can also be referred to as "assembly" or "assembly," and these terms are synonymous and can be used interchangeably.

[0026] As used herein, the term "sequence obtained by reconstructing single-cell genome fragment sequences" refers to a sequence obtained by reconstructing a plurality of single genome fragment sequences. It may also be referred to as an assembled sequence of a single-cell genome.

[0027] As used herein, the term "sequence obtained by reconstructing metagenomic fragment sequences" refers to a sequence obtained by reconstructing a plurality of metagenomic fragment sequences. It may also be referred to as an assembled sequence of a metagenome.

[0028] As used herein, "integration" refers to grouping sequences by organism unit (phylogenetic classification: clustering) and reconstructing (reassembling) the sequences contained in each group. For example, it refers to reconstruction of a "sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences" from a "sequence obtained by reconstructing single-cell genome fragment sequences."

[0029] As used herein, the term "sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences" refers to a sequence obtained by grouping the sequences obtained by reconstructing single genome fragment sequences by unit organism and reconstructing the sequences contained in each group. It is also called an "integrated sequence."

[0030] As used herein, "sorting" refers to grouping sequences by organismal unit using a reference sequence and reconstructing the sequences contained in each group. This is also called "binning."

[0031] In this specification, the term "sequences obtained by fractionating sequences obtained by reconstructing metagenomic fragment sequences" refers to sequences obtained by grouping the sequences obtained by reconstructing metagenomic fragment sequences by unit organism using a reference sequence, and reconstructing the sequences contained in each group. It is also called "fractionated sequences."

[0032] As used herein, the term "single-cell genome fragment sequence or a sequence derived therefrom" refers to a "single-cell genome fragment sequence" and any sequence derived therefrom. For example, it includes, but is not limited to, a "sequence obtained by reconstructing a single-cell genome fragment sequence" and a "sequence obtained by integrating sequences obtained by reconstructing a single-cell genome fragment sequence."

[0033] As used herein, the term "metagenome fragment sequence or a sequence derived therefrom" refers to a "metagenome fragment sequence" and any sequence derived therefrom. For example, it includes, but is not limited to, a "sequence obtained by reconstructing a metagenome fragment sequence" and a "sequence obtained by fractionating a sequence obtained by reconstructing a metagenome fragment sequence."

[0034] As used herein, the term "chimeric sequence" refers to a sequence that contains both aligned and non-aligned portions when aligned with a reference sequence. For example, chimeric sequences may be generated by performing PCR using multiple templates.

[0035] As used herein, "mapping" refers to a process of aligning fragment sequences to regions that have a high degree of match between each fragment sequence and the base sequence in the reference sequence used.

[0036] As used herein, a "reference sequence" is a sequence to which a lead sequence is mapped in order to determine which region of a gene a fragment sequence corresponds to and which mutation of a gene the lead sequence corresponds to, etc. It is also sometimes called a "reference sequence." As used herein, the term "fractionated fragment sequences" refers to a set of sequences obtained by fractionating metagenomic fragment sequences on an organismal basis without assembling them.

[0037] In this specification, "combination analysis" refers to any analysis for creating high-quality genome sequences using single-cell genome fragment sequences or sequences derived therefrom and metagenomic fragment sequences or sequences derived therefrom. For example, it includes, but is not limited to, separation of metagenomic fragment sequences or sequences derived therefrom using single-cell genome fragment sequences or sequences derived therefrom; integration of single-cell genome fragment sequences or sequences derived therefrom using metagenomic fragment sequences or sequences derived therefrom; mapping single-cell genome fragment sequences to sequences reconstructed from metagenomic fragment sequences, thereby dividing single-cell genome fragment sequences into sequences reconstructed from metagenomic fragment sequences, and identifying aligned single-cell genome fragment sequences as chimeric sequences, and removing chimeric sequences as necessary.

[0038] (Description of the technology used) (Preparation of single-cell genomic DNA) Methods for extracting and preparing single-cell genomic DNA from microbial communities include the droplet method (Hosokawa, 2017) and the SAG-gel method (Chijiiwa, 2020).

[0039] (Preparation of metagenomic DNA) Methods for extracting single-cell genomic DNA from microbial communities include the IHMS DNA extraction protocol Q. Methods for preparing extracted DNA include the QIAseq FX DNA library kit.

[0040] (DNA sequencing) DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as the PacBio Sequel System and Oxford nanopre GridION.

[0041] (Single cell genome analysis) The analysis of the obtained single-cell genome reads is performed on a computer using appropriate analysis tools. It is preferable to perform the analysis on a high-performance computer that can perform parallel calculations. Analysis tools for evaluating the quality of the reads include bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Tools for removing contaminating reads from humans and other sources include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. Tools for assembling reads include SPAdes. Tools for predicting genes from genome sequences include Prodigal, Prokka, and DFAST. Tools for evaluating the completeness and degree of contamination of genome sequences include CheckM. Tools for evaluating the assembly accuracy of genome sequences include QUAST. Tools for identifying and grouping identical strains of single-cell genomes include ccSAG (Kogawa, 2018).

[0042] (Metagenomic analysis) The analysis of the obtained metagenomic reads is performed on a computer using an appropriate analysis tool. It is preferable to perform the analysis on a high-performance computer that can perform parallel calculations. Examples of analysis tools that evaluate the quality of the reads include bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Examples of tools that remove contaminating reads from humans and other sources include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. Examples of tools that assemble reads include MEGAHIT and SPAdes. Examples of tools that predict genes from genome sequences include Prodigal, Prokka, and DFAST. Examples of tools that perform binning on assembled sequences include CONCOCT, MaxBin2, and MetaBAT2. Examples of tools that reconstruct binning results include DAS_Tool and metaWARAP.

[0043] (Metagenomic data sorting guide) Guiding the separation of metagenomic assembled sequences by single-cell genome organism lineages is performed on a computer using appropriate analytical tools. It is preferable to run the analysis on a high-performance computer capable of parallel computing. One method to guide the separation of metagenomic reads and assembled sequences using single-cell genome reads, assembled sequences, and integrated assembled sequences is to map the sequences obtained by each method and establish correspondence between them. Examples of tools for mapping sequences include BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh. Converting the mapping results to a format such as bed makes it easier to calculate sequence coverage. An example of a tool for converting to bed format is BEDtools.

[0044] (High quality through integration) The quality improvement of the sequence is carried out on a computer using appropriate analytical tools, preferably on a high-performance computer capable of parallel computing.

[0045] (Obtaining single-cell genome and metagenome fragment sequences) Metagenomic analysis and single-cell genomic analysis are performed on the same microbial community to obtain fragment sequences (reads). In single-cell genomic analysis, reads derived from multiple cells are obtained.

[0046] (Single cell genome analysis) A quality check process is performed on each single-cell read obtained. Sequence assembly is performed using the quality-processed reads to reconstruct the original genome sequence that is longer than the read. A single-cell genome assembled sequence is obtained.

[0047] (Single-cell genome assembly sequences and systematics integration) The single-cell genome sequences are compared with each other, and single cells derived from the same strain are classified into biological lineages based on sequence homology, marker gene homology, etc. The classified single-cell genomes are integrated to obtain an integrated single-cell genome assembly sequence.

[0048] (Metagenomic analysis) The obtained metagenomic reads are subjected to a quality check process. The reads that have undergone the quality check process are used to assemble sequences, and the original genome sequence, which is longer than the reads, is reconstructed. The metagenomic assembled sequence is obtained.

[0049] (Separation of metagenomic assembled sequences by single-cell genome organism lineage) The metagenomic assembled sequences are sorted into single-cell biological lineages using the integrated single-cell genome assembled sequences (or the associated single-cell genome reads or single-cell genome assembled sequences). Here, the genome coverage rate of the metagenomic assembled sequences, the homology between the metagenomic assembled sequences and the integrated single-cell genome assembled sequences, the genome sequence composition, and the homology of marker genes can be used as indicators for sorting.

[0050] (Improving the quality of fractionated metagenomic assembled sequences) The metagenomic reads are mapped to the sorted metagenomic assembled sequence, and reassembly is performed using the associated metagenomic reads. At this time, an integrated single-cell genome assembled sequence (or a single-cell genome assembled sequence) belonging to the same biological phylogenetic group is used as a guide. Alternatively, a genome sequence is constructed by combining the sorted metagenomic assembled sequence or metagenomic reassembled sequence with the single-cell genome assembled sequence (or a single-cell genome assembled sequence).

[0051] (Improving the quality of single-cell genome assembly sequences) The single-cell genome reads are mapped to the integrated single-cell genome assembled sequence, and reassembled using the associated single-cell genome reads. In this case, a metagenomic assembled sequence belonging to the same organism lineage group is used as a guide. Alternatively, a genome sequence is constructed by combining the integrated single-cell genome assembled sequence and the metagenomic assembled sequence.

[0052] (Preferred embodiment) A description of a preferred embodiment is given below, but it should be understood that this embodiment is an example of the present disclosure, and the scope of the present disclosure is not limited to such a preferred embodiment. It should also be understood that those skilled in the art can easily make modifications, changes, etc. within the scope of the present invention by referring to the following preferred examples. Regarding these embodiments, those skilled in the art can combine one or more of any embodiments as appropriate.

[0053] In one aspect, the present disclosure provides a method for analyzing a sequence of a population of organisms (e.g., a microbial community) that includes a plurality of organisms (e.g., single cells), the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, separating and / or integrating and / or removing chimeras from the original genome sequences longer than the fragment sequences for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; The method disclosed herein includes the steps of: (1) providing a method for obtaining a sequence of a genome sequence of a single cell, comprising: (a) providing a method for obtaining a sequence of a genome sequence of a single cell; (b) providing a method for obtaining a sequence of a genome sequence of a single cell; and (c) providing a method for obtaining a sequence of a genome sequence of a single cell; and (d) providing a method for obtaining a sequence of a genome sequence of a single cell; and (e) providing a method for obtaining a sequence of a genome sequence of a single cell; and (f) providing a method for obtaining a sequence of a genome sequence of a single cell; and (g) providing a method for obtaining a sequence of a genome sequence of a single cell; and (h) providing a method for obtaining a sequence of a genome sequence of a single cell; and (i) providing a method for obtaining a sequence of a genome sequence of a single cell; and (i) providing a method for obtaining a sequence of a genome sequence of a single cell; and (ii) providing a method for obtaining a sequence of a genome sequence of a single cell; and (iii) providing a method for obtaining a sequence of a genome sequence of a single cell; and (iv) providing a method for obtaining a sequence of a genome sequence of a single cell; and (v) providing a method for obtaining a sequence of a genome sequence of a single cell; and (vi ...

[0054] In general, single-cell genome analysis and metagenomic analysis are not performed from the same community sample, and there has been no method of using one data as a database to refer to the other data. However, when analyzing a sequence derived from a single cell using the method of the present disclosure, not only a database containing a sequence derived from a single cell but also a database containing sequence information derived from a metagenomics may be used as a reference database, and sequence information with higher accuracy than that of the conventional technology can be obtained using either database. By using the method of the present disclosure, a sequence having a high level of genome sequence completeness and contamination degree can be obtained. Since it is difficult to achieve both genome sequence completeness and contamination degree in existing methods, the sequence obtained by the method of the present disclosure is useful for all genome sequence analysis. Generally, it is difficult to obtain 16S, 23S, and 5S ribosomal RNA in metagenomic analysis, but the present disclosure, which is combined with single-cell genome analysis, makes it possible to obtain these operon structures. In addition, a reduction in the number of mismatches due to reassembly can be expected.

[0055] In one embodiment, the organism unit population is any population that includes multiple types of cells or cell-like structures. In certain embodiments, the organism unit population includes, but is not limited to, microbial communities such as soil microflora and gut microbial algae, mixtures of somatic cells and diseased cells (e.g., cancer cells), mixtures of cells and viruses, etc.

[0056] In one embodiment, the metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing a metagenomic fragment sequence, and a sequence obtained by fractionating a sequence obtained by reconstructing a metagenomic fragment sequence.

[0057] In another embodiment, the single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing the single cell genome fragment sequence, and a sequence obtained by integrating the sequence obtained by reconstructing the single cell genome fragment sequence.

[0058] In one embodiment, the classification is performed by mapping any reference sequence to the metagenomic assembly sequence and classifying each metagenomic assembly sequence into the phylogenetic group with the highest coverage. In a specific embodiment, the single-cell genome integrated sequence obtained by the method of the present disclosure can be used as the reference sequence.

[0059] In one embodiment, the integration is performed by selecting the obtained single-cell genome assembled sequences based on a predetermined criterion (e.g., completion rate, contamination rate), calculating the value of an index for identifying biological lineage for the selected sequences, and regarding sequences with a value equal to or greater than a certain value as being in the same group. In a particular embodiment, the integration may be performed by selecting the obtained single-cell genome assembled sequences based on a predetermined criterion (e.g., completion rate, contamination rate), calculating the homology of single-copy marker genes for the selected sequences, and regarding sequences with a homology value equal to or greater than a certain value as being in the same group. Grouping may also be performed based on genome sequence composition such as GC content or tetranucleotide frequency.

[0060] In one embodiment, the combination analysis may be any analysis using a single cell genome fragment sequence or a sequence derived therefrom and a metagenomic fragment sequence or a sequence derived therefrom. In a particular embodiment, the combination analysis may include, but is not limited to, the following: separation of a metagenomic fragment sequence or a sequence derived therefrom using a single cell genome fragment sequence or a sequence derived therefrom; integration of a single cell genome fragment sequence or a sequence derived therefrom using a metagenomic fragment sequence or a sequence derived therefrom; mapping a single cell genome fragment sequence to a sequence reconstructed from a metagenomic fragment sequence, thereby identifying a single cell genome fragment sequence aligned by dividing the metagenomic fragment sequence into a sequence reconstructed from a metagenomic fragment sequence as a chimeric sequence, and removing the chimeric sequence as necessary. These combination analyses may be performed alone or in combination.

[0061] In one embodiment, the separation of a metagenomic fragment sequence or a sequence derived therefrom using a single-cell genome fragment sequence or a sequence derived therefrom may include mapping a single-cell genome read to a metagenomic assembly sequence to associate a metagenomic assembly sequence with a single cell, and obtaining a fractionated sequence separated by a single cell. Furthermore, a metagenomic read is mapped to a fractionated sequence to obtain a fractionated metagenomic read (fractionated fragment sequence). It is also possible to obtain a high-quality genome sequence by performing reassembly using a single-cell integrated sequence as a guide for the fractionated metagenomic read. It is also possible to combine a metagenomic fractionated sequence separated from a metagenomic fragment sequence with a sequence obtained by integrating a sequence reconstructed from a single-cell genome fragment sequence. Although reassembly is effective for reducing the number of mismatches and misassembled blocks, good results can also be obtained by combining a metagenomic fractionated sequence with a single-cell integrated sequence when simply increasing an assembly index such as N50 or total length. Those skilled in the art can use these methods appropriately according to the purpose.

[0062] In certain embodiments, the method of the present disclosure comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) a step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, respectively, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; Includes.

[0063] In the present disclosure, the step of A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence may be carried out as follows: Total DNA is extracted from a population of organisms that contains multiple organisms, and a sequence library is constructed. Methods for extracting single-cell genomic DNA from microbial communities include the IHMS DNA extraction protocol Q. Methods for preparing extracted DNA include the QIAseq FX DNA library kit. Metagenome fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may also be performed at this time. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, or methods using DNA sequencers such as the PacBio Sequel System and Oxford nanopre GridION.

[0064] In the present disclosure, the step of B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of organism units may be carried out as follows: For example, a single cell is isolated from a population of organisms that contains multiple organisms, and the isolated single cell is lysed and genomic DNA is amplified to construct a sequencing library. Methods for extracting and preparing single-cell genomic DNA from a microbial community include droplet methods and the SAG-gel method. Single-cell genome fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may also be performed at this time. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as PacBio Sequel System and Oxford nanopre GridION.

[0065] In the present disclosure, the steps of C-1) A) and B) for reconstructing the original genome sequence longer than the reads may be carried out as follows: Reads of a certain quality are extracted from metagenomic reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads such as human are removed from these reads. Tools for removing contaminating reads such as human include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. Assembled sequences are obtained by assembling the obtained high-quality reads. Tools for assembling reads include MEGAHIT and SPAdes. Reads of a certain quality are extracted from single-cell genome reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads such as human are removed from these reads. Tools for removing contaminating reads from humans and other sources include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. Assembled sequences are obtained by assembling the obtained high-quality reads. An example of a tool for assembling reads is SPAdes.

[0066] In the present disclosure, C-2) a step of separating the sequence obtained by reconstructing the metagenomic fragment sequences and integrating the sequence obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequences and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequences, respectively, in which the step of referring to the single-cell genome fragment sequences when separating the sequence obtained by reconstructing the metagenomic fragment sequences may be performed as follows: The single-cell genome assembled sequences obtained as described above are integrated using ccSAG or the like. Specifically, clustering is performed using the single copy marker gene homology and ANI of each single-cell genome assembled sequence. Gene homology can be determined using BLAST, and ANI can be calculated using fastANI or the like. The grouped single-cell genome reads are combined and reassembled using a tool such as SPAdes to obtain a single-cell genome integrated sequence. The grouped single-cell genome reads are mapped to the metagenomic assembled sequence using a mapping tool such as BWA, Bowtie 2, HISAT2, Minimap2, or bbmap.sh to separate the metagenomic assembled sequences. One criterion for separation is to associate the mapped reads with the single cell that occupies the widest area in the metagenomic assembled sequence.

[0067] In the present disclosure, C-3) the step of generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence, may be performed as follows: The metagenomic reads are mapped to the fractionated metagenomic sequence using a mapping tool such as BWA, Bowtie 2, HISAT2, Minimap2, or bbmap.sh, thereby obtaining fractionated metagenomic reads.

[0068] In the present disclosure, the step of D) combining the separated fragment sequence with a sequence obtained by separating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence may be carried out as follows: The fractionated metagenomic reads are reassembled using an assembly tool such as SPAdes. At this time, the corresponding single-cell assembled sequence or single-cell genome integrated sequence is referenced as an assembly guide. In the case of SPAdes, it is possible to reference them as guides by specifying options such as --pacbio or --nanopore.

[0069] The method disclosed herein makes it possible to reconstruct sequence information obtained from two analyses, thereby covering a wider range of genomic regions with fewer and longer pieces of sequence information.

[0070] In certain embodiments, the method of the present disclosure comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; Includes.

[0071] In the present disclosure, the step of A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence may be carried out as follows: Total DNA is extracted from a population of organism units that contains multiple organism units, and a sequence library is constructed. Metagenomic fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may be performed at this time. For example, a method for extracting single-cell genomic DNA from a microbial community is the IHMS DNA extraction protocol Q. A method for preparing the extracted DNA is the QIAseq FX DNA library kit. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as the PacBio Sequel System and Oxford nanopre GridION.

[0072] In the present disclosure, the step of B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of organism units may be carried out as follows: For example, a single cell is isolated from a population of organisms that contains multiple organisms, and the isolated single cell is lysed and genomic DNA is amplified to construct a sequencing library. Methods for extracting and preparing single-cell genomic DNA from a microbial community include droplet methods and the SAG-gel method. Single-cell genome fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may also be performed at this time. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as PacBio Sequel System and Oxford nanopre GridION.

[0073] In the present disclosure, the steps of C-1) A) and B) for reconstructing the original genome sequence longer than the reads may be carried out as follows: For example, low-quality fragment sequences are deleted from the fragment sequences obtained in A) and B), and multiple fragment sequences are linked using common sequences between the fragment sequences to construct a sequence longer than the fragment sequence. More specifically, reads of a specific quality are extracted from the metagenomic reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads such as human are removed from these reads. Examples of tools for removing contaminating reads such as human include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. An assembled sequence is obtained by assembling the obtained high-quality reads. Examples of tools for assembling reads include MEGAHIT and SPAdes. Reads of a specific quality are extracted from the single-cell genome reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads from humans and other sources are removed from these reads. Tools for removing contaminating reads from humans and other sources include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. Assembled sequences are obtained by assembling the obtained high-quality reads. An example of a tool for assembling reads is SPAdes.

[0074] In the present disclosure, the step of C-2) integrating the sequence obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequences may be performed as follows: For example, the reconstructed sequences of the single-cell genome fragment sequences obtained in C-1) are grouped by organism unit, and the sequences contained in each group are reconstructed. More specifically, the obtained single-cell genome assembled sequences are integrated using ccSAG or similar. Clustering is performed using the single-copy marker gene homology and ANI of each single-cell genome assembled sequence. BLAST can be used for gene homology, and fastANI can be used for ANI calculation. The grouped single-cell genome reads are combined and reassembled using a tool such as SPAdes to obtain a single-cell genome integrated sequence.

[0075] In the present disclosure, the step of C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence from the sequence obtained by reconstructing the metagenomic fragment sequence with reference to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence may be performed as follows: For example, the reconstructed sequence of the single-cell genome fragment sequence obtained in C-1) and / or the single-cell genome fragment sequence obtained in C-2) are mapped to the reconstructed sequence of the metagenomic fragment sequence, and the reconstructed sequence of the metagenomic fragment sequence is assigned to the phylogenetic group with the highest coverage rate, thereby obtaining a separated sequence of the reconstructed sequence of the metagenomic fragment sequence. More specifically, the obtained metagenomic assembled sequence and the single-cell genome integrated sequence are mapped by a tool such as BLAST, and the metagenomic assembled sequence is separated. One criterion for separation is to correspond to the single cell in which the area of ​​the mapped single-cell genome integrated sequence occupies the widest range in the metagenomic assembled sequence.

[0076] In the present disclosure, the step of D) combining a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequences and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence may be performed as follows: For example, an improved reconstructed genome sequence is obtained by reconstructing (combining) a sequence obtained by separating a sequence obtained by reconstructing a metagenome fragment sequence and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequence. More specifically, the separated metagenome assembled sequence and the corresponding single-cell genome integrated sequence are combined using a tool such as CSAR, LINKS, or HaploMerger2.

[0077] In certain embodiments, the method of the present disclosure comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) A step of reconstructing the original genome sequence longer than the read for B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; Includes.

[0078] In the present disclosure, the step of A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence may be carried out as follows: For example, total DNA is extracted from a population of organism units that includes multiple organism units, and a sequence library is constructed. Methods for extracting single-cell genomic DNA from microbial communities include the IHMS DNA extraction protocol Q. Methods for preparing extracted DNA include the QIAseq FX DNA library kit. Metagenome fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may also be performed at this time. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as the PacBio Sequel System and Oxford nanopre GridION.

[0079] In the present disclosure, the step of B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of organism units may be carried out as follows: For example, a single cell is isolated from a population of organisms that contains multiple organisms, and the isolated single cell is lysed and genomic DNA is amplified to construct a sequencing library. Methods for extracting and preparing single-cell genomic DNA from a microbial community include droplet methods and the SAG-gel method. Single-cell genome fragment sequences are obtained by single-end or paired-end sequencing. Long-read sequencing may also be performed at this time. DNA sequencing methods include methods using DNA sequencers such as Illumina MiSeq and Illumina HiSeq, and methods using DNA sequencers such as PacBio Sequel System and Oxford nanopre GridION.

[0080] In the present disclosure, the step of reconstructing an original genome sequence longer than the read for C-1)B) may be carried out as follows: For example, low-quality fragment sequences are deleted from the fragment sequences obtained in B), and multiple fragment sequences are linked using common sequences between the fragment sequences to construct a sequence longer than the fragment sequence. More specifically, reads of a specific quality are extracted from single-cell genome reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads such as human are removed from these reads. Examples of tools for removing contaminating reads such as human include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. An assembled sequence is obtained by assembling the obtained high-quality reads. An example of a tool for assembling reads is SPAdes.

[0081] In the present disclosure, C-2) the step of integrating the sequences obtained in C-1) to generate a sequence obtained by integrating the sequences obtained by reconstructing single-cell genome fragment sequences may be carried out as follows: For example, the reconstructed sequences of the single-cell genome fragment sequences obtained in C-1) are grouped by organism unit, and the sequences contained in each group are reconstructed. More specifically, the obtained single-cell genome assembled sequences are integrated using ccSAG or similar. Clustering is performed using the single-copy marker gene homology and ANI of each single-cell genome assembled sequence. BLAST can be used for gene homology, and fastANI can be used for ANI calculation. The grouped single-cell genome reads are combined and reassembled using a tool such as SPAdes to obtain a single-cell genome integrated sequence.

[0082] In the present disclosure, C-3) the step of generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence may be performed as follows: For example, the sequence obtained by reconstructing the single-cell genome fragment sequence obtained in C-1) and / or the single-cell genome fragment sequence obtained in C-2) is mapped to the metagenomic fragment sequence, and the metagenomic fragment sequence is sorted into the phylogenetic group with the highest coverage rate, thereby obtaining a fractionated fragment sequence of the metagenomic fragment sequence. More specifically, reads of a specific quality are extracted from the metagenomic reads using quality control tools such as bbduk.sh, fastx-toolkit, fastp, fastqc, and prinseq-lite.pl. Furthermore, contaminating reads such as human are removed from these reads. Examples of tools for removing contaminating reads such as human include mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and formatting tools such as reformat.sh. The obtained high-quality reads are mapped to the single-cell genome integrated sequence using mapping tools such as BWA, Bowtie 2, HISAT2, Minimap2, and bbmap.sh, and are fractionated.

[0083] In the present disclosure, C-4) the step of reconstructing an original genome sequence longer than the fractionated fragment sequence of the metagenomic fragment sequence may be carried out as follows: For example, low-quality fractionated fragment sequences are deleted from the fractionated fragment sequences of the metagenomic fragment sequences obtained in C-3), and a sequence longer than the fractionated fragment sequence is constructed by linking a plurality of the remaining fractionated fragment sequences using a common sequence between the fractionated fragment sequences. More specifically, the fractionated metagenomic reads are assembled using an assembly tool such as MEGAHIT or SPAdes.

[0084] In the present disclosure, the step of D) combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenomic fragment sequences with a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence may be carried out as follows: For example, an improved reconstructed genome sequence is obtained by reconstructing (combining) a sequence obtained by reconstructing the fractionated fragment sequences of the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence. More specifically, the fractionated metagenomic assembled sequence and the corresponding single-cell genome integrated sequence are combined using a tool such as CSAR, LINKS, or HaploMerger2.

[0085] In another aspect, the present disclosure provides a program for implementing on a computer a method for analyzing a sequence of a population of organism units (e.g., a microbial community) including a plurality of organism units (e.g., single cells), the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, sorting and / or integrating and / or removing chimeras from original genome sequences longer than the reads for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; The Company provides programs including:

[0086] Programming languages ​​to be implemented include various shells (bash, csh, etc.), perl, python, ruby, R, mathematica, matlab, julia, php, javascript, and other interpreter-type scripting languages, and c, c++, c#, java, go, and other compiled languages.

[0087] In another aspect, the present disclosure provides a recording medium storing a program for causing a computer to implement a method for analyzing a sequence of a population of organism units (e.g., a microbial community) including a plurality of organism units (e.g., single cells), the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, sorting and / or integrating and / or removing chimeras from original genome sequences longer than the reads for A) and B), respectively; D) performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; A recording medium including the above-mentioned is provided.

[0088] In each of the above embodiments, each component may be realized by a dedicated hardware or by executing a software program suitable for each component. Each component may be realized by a program execution unit configured by a CPU or a processor and a main storage device reading and executing a software program recorded on a recording medium such as a USB memory, a hard disk, or a semiconductor memory.

[0089] In yet another aspect, the present disclosure provides a system for analyzing a sequence of a population of organism units (e.g., a microbial community) that includes a plurality of organism units (e.g., single cells), the system comprising: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) A sequence processing unit that reconstructs, separates and / or integrates original genome sequences longer than the reads and / or removes chimeras from the metagenome fragment sequences and single-cell genome fragment sequences, respectively; D) A sequence reconstruction unit that performs a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; The present invention provides a system including:

[0090] In the present disclosure, A) the metagenomic fragment sequence generating unit that performs metagenomic analysis on the organism unit population and obtains a metagenomic fragment sequence may be implemented as follows: for example, a CPU that stores a program that performs metagenomic analysis on the organism unit population and obtains a metagenomic fragment sequence, or a CPU that functions as a metagenomic fragment sequence generating unit when a program that performs metagenomic analysis on the organism unit population and obtains a metagenomic fragment sequence is transmitted. When performing a large-scale analysis, it is desirable to use a parallel computer system. A plurality of computers constructed from a plurality of CPUs and main memory devices, and a storage that eliminates I / O bottlenecks by a distributed file system are prepared. It is possible to operate it as a web application by starting a web server. It is possible to operate it as a system that provides metagenomic reads as input and metagenomic assembled sequences as output.

[0091] In the present disclosure, B) the single cell genome fragment sequence generating unit that performs single cell genome analysis on the organism unit population and obtains single cell genome fragment sequences derived from the plurality of unit organism units may be implemented as follows: For example, a CPU that stores a program that performs single cell genome analysis on the organism unit population and obtains single cell genome fragment sequences derived from the plurality of unit organism units, or a CPU that functions as a single cell genome fragment sequence generating unit when a program that performs single cell genome analysis on the organism unit population and obtains single cell genome fragment sequences derived from the plurality of unit organism units is transmitted. When performing large-scale analysis, it is desirable to use a parallel computer system. A plurality of computers constructed from a plurality of CPUs and main memory devices, and a storage that eliminates I / O bottlenecks by a distributed file system are prepared. It is possible to operate it as a web application by starting a web server. It is possible to operate it as a system that provides single cell genome reads as input and single cell genome assembled sequences as output.

[0092] In the present disclosure, C) the sequence processing unit that reconstructs, separates, and / or integrates, and / or removes chimeras from the original genome sequence longer than the read for the metagenome fragment sequence and the single cell genome fragment sequence may be implemented as follows: For example, a CPU that stores a program that reconstructs, separates, and / or integrates, and / or removes chimeras from the original genome sequence longer than the read for the metagenome fragment sequence and the single cell genome fragment sequence, or a part that functions as a sequence processing unit when a program that reconstructs, separates, and / or integrates, and / or removes chimeras from the original genome sequence longer than the read for the metagenome fragment sequence and the single cell genome fragment sequence is transmitted to the CPU. When performing large-scale analysis, it is desirable to use a parallel computer system. A plurality of computers constructed from a plurality of CPUs and main storage devices, and a storage that eliminates I / O bottlenecks by a distributed file system are prepared. It is possible to operate it as a web application by starting a web server. It is possible to operate the system to provide metagenomic reads and single-cell genome reads as inputs, and chimera-removed reads, metagenomic assembled sequences, single-cell genome assembled sequences, fractionated metagenomic assembled sequences, and single-cell genome integrated sequences as outputs.

[0093] In the present disclosure, the sequence reconstruction unit D) performing combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence may be implemented as follows: For example, a CPU storing a program for performing combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence, or a CPU that functions as a sequence reconstruction unit when a program for performing combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence is transmitted. When performing a large-scale analysis, it is desirable to use a parallel computer system. A plurality of computers constructed from a plurality of CPUs and main storage devices, and a storage that eliminates I / O bottlenecks by a distributed file system are prepared. It is possible to operate it as a web application by starting a web server. It is possible to operate it as a system that provides metagenomic reads and single cell genome reads, or metagenomic assembled sequences and single cell genome integrated sequences as input, and a reconstructed genome sequence as output.

[0094] In one aspect, the present disclosure provides a method for identifying useful genes and / or useful gene clusters, comprising: Optionally, using a gene prediction tool on the genome sequence obtained by the method of the present disclosure; Using a gene function prediction tool on the genome sequence and / or gene data to identify useful genes and / or useful gene clusters; The present invention provides a method comprising:

[0095] In the present disclosure, the step of using a gene prediction tool on a genome sequence obtained by the disclosed method may be carried out as follows: Gene information can be obtained by running a gene prediction tool such as Prokka or DFAST on the obtained genome sequence.

[0096] In the present disclosure, the step of identifying useful genes and / or useful gene clusters using a gene function prediction tool on the genome sequence and / or gene data may be carried out as follows: By using a biosynthetic gene cluster detection tool such as antiSMASH, NaPDoS, PRISM / GNP on the obtained genome sequence or the obtained predicted genes, it is possible to detect useful gene clusters, etc.

[0097] In one embodiment, useful genes and / or useful gene clusters include drug resistance genes, genes encoding proteins involved in the production of useful biomolecules, and their gene clusters.

[0098] Although the system, program, recording medium, and method according to one or more aspects of the present disclosure have been described based on the embodiments, the present disclosure is not limited to these embodiments. As long as they do not deviate from the spirit of the present disclosure, various modifications conceived by a person skilled in the art to the present embodiments and forms constructed by combining components in different embodiments may also be included within the scope of one or more aspects of the present disclosure.

[0099] In addition, some or all of the components of the device in each of the above embodiments may be configured as one system LSI (Large Scale Integration). For example, the system for processing sequence information of the present disclosure may be combined with a database as necessary, or may include or be combined with a system for identifying sequences having functions such as biomarkers.

[0100] A system LSI is a super multi-function LSI manufactured by integrating multiple components on a single chip. Specifically, it is a computer system that includes a microprocessor, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. A computer program is stored in the ROM. The microprocessor operates according to the computer program, and the system LSI achieves its functions. Here, the system LSI is called a system LSI, but it may also be called an IC, an LSI, a super LSI, or an ultra LSI depending on the degree of integration. In addition, the method of integration is not limited to LSI, and it may be realized by a dedicated circuit or a general-purpose processor. After the LSI is manufactured, a field programmable gate array (FPGA) that can be programmed, or a reconfigurable processor that can reconfigure the connections and settings of the circuit cells inside the LSI may be used. If an integrated circuit technology that replaces LSI appears due to the progress of semiconductor technology or a different derived technology, it may naturally be used to integrate the functional blocks. The application of biotechnology, etc. is also possible.

[0101] Moreover, one aspect of the present disclosure may be not only such sequence information processing devices or systems, but also function-specialized systems (e.g., biomarker screening devices, drug efficacy assessment devices, diagnostic devices, etc.). An embodiment of the present disclosure may also be a computer program that causes a computer to execute each of the characteristic steps included in sequence information processing. Another aspect of the present disclosure may be a computer-readable non-transitory recording medium on which such a computer program is recorded.

[0102] In each of the above embodiments, each component may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory. Here, the software for realizing the pain estimation device of each of the above embodiments may be the program described above in this specification.

[0103] All references cited herein, including scientific literature, patents, patent applications, and the like, are hereby incorporated by reference in their entirety to the same extent as if each was specifically set forth.

[0104] The present disclosure has been described above by showing preferred embodiments for ease of understanding. The present disclosure will be described below based on examples, but the above description and the following examples are provided for illustrative purposes only and are not provided for the purpose of limiting the present disclosure. Therefore, the scope of the present disclosure is not limited to the embodiments or examples specifically described in this specification, but is limited only by the scope of the claims. EXAMPLES

[0105] In the following examples, products from Illumina were used, but products from Thermo Fisher Scientific, Oxford Nanopore Technologies, etc. can also be used instead.

[0106] Example 1 (Sample acquisition) We obtained a bacterial cell cocktail (Cell-Mock-001) provided by the NITE Biotechnology Center (NBRC). The bacterial cell cocktail was made by purely culturing 15 NBRC strains and mixing them so that the cell numbers were equal.

[0107] (Obtaining the reference genome) Based on the NBRC numbers of the strains contained in the standard cell cocktail (Cell-Mock-001), we obtained reference genome sequences for the 15 strains contained in the cell cocktail from NCBI. The details of the reference genomes are shown in the table below. [Table 1]

[0108] (Single-cell genomic DNA preparation and DNA sequencing) Microbial single-cell isolation from a standard bacterial cocktail, lysis of the isolated single cells, and genomic DNA amplification were performed using the SAG-gel method (Chijiiwa, 2020). DNA sequences were obtained by 150-cycle paired-end sequencing using an Illumina MiSeq (total number of reads: 52M, total base size: 3.9Gb).

[0109] (Single cell genome analysis) Low-quality reads were removed from 48 single-cell genome reads obtained from the standard bacterial cocktail using bbduk.sh 38.79 (options: threads=4, qtrim=r, trimq=10, minlength=40, maxns=1). Reads were de novo assembled using SPAdes 3.14.0 (options: --sc --careful --disable-rr --disable-gzip-output -t 4 -m 32), and contigs shorter than 1000 bp were excluded from further analysis.

[0110] (Single-cell genome phylogenetics and integration) Forty-seven single-cell genome assembly sequences were extracted that had a completeness of 10% or more by CheckM and a contamination level of less than 10%. For the 47 targets, ANI (Average Nucleotide Identity) was calculated by brute force using fastANI 1.3 with default options. In addition, the homology of common single-copy marker genes obtained by CheckM v1.1.2 taxonomy work flow (options: -nt --tab_table -t 16 domain Bacteria) was calculated using blastn 2.9.0+ with default options. Single cells with ANI of 95% or more and single-copy marker gene homology of 99.9% or more were considered to be the same strain and were grouped.

[0111] (Metagenomic DNA preparation and DNA sequencing) Total DNA was extracted from the standard bacterial cocktail according to IHMS protocol Q. A sequencing library was constructed using the QIAseq FX DNA library kit. DNA sequences were obtained by 150-cycle paired-end sequencing using an Illumina MiSeq (total number of reads: 35M, total base size: 2.6Gb).

[0112] (Metagenomic sequence analysis) One metagenomic read from the standard bacterial cocktail was purified using bbduk.sh 38.79 (options: threads=4, qtrim=r, trimq=10, minlength=40, maxns=1) to remove low-quality reads. Reads were de novo assembled using SPAdes 3.14.0 (options: --meta --k auto -t 24 -m 240).

[0113] (Metagenomic sequencing) The integrated single-cell genome reads were mapped to the metagenomic assembly sequences using minimap2 2.17 with default options. The mapping results were converted to bed format using the bamtobed ​​function of bedtools v2.29.2, and the bed formats were further integrated using the merge function. A fractionated metagenomic assembly sequence was constructed by sorting each metagenomic assembly sequence into the phylogenetic group with the highest coverage.

[0114] (Single-cell genome-guided reassembly) The metagenomic reads were mapped to the metagenomic assembled sequences sorted by the single-cell genome assembled sequences using minimap2 2.17 to obtain the corresponding metagenomic reads (fractionated fragment sequences). The sorted metagenomic reads were reassembled de novo using SPAdes 3.14.0 to obtain the final genome sequence (options: --careful -k auto -t 4 -m 32 --nanopore FILE). Here, by specifying the integrated single-cell genome assembled sequences of the corresponding phylogenetic classification for the --nanopore option, the integrated single-cell genome assembled sequences were presented to SPAdes as pseudo long reads, and a hybrid assembly of the sorted metagenomic reads and the integrated single-cell genome assembled sequences was performed (Figure 2).

[0115] (obtaining binned data for comparison) Commonly used methods for fractionating metagenomic assembled sequences were CONCOCT 1.0.0 (Concoct, 2014), MaxBin2 2.2.6 (Maxbin2, 2014), and MetaBAT2 2.12.1 (Metabat2, 2015), which were run with default options. To reconstruct the fractionation results obtained with these three different methods, DAS_Tool 1.1.2 (DAS_Tool, 2018) and MetaWRAP 1.2.3 (DAS_Tool, 2018) were used with default options.

[0116] (Identification of the corresponding reference genome) For the genome sequences separated by this method and the genome sequences separated by the five common binning methods, we calculated the ANI against the reference genome using fastANI 1.3. Reference genomes with 99.5% or more homology were matched to each separated genome sequence.

[0117] (Calculating sequence accuracy) Each assembled sequence was evaluated using QUAST v.5.0.2 (default options) and ChcekM v1.1.2 lineage work flow (options: -nt --tab_table -t 16). 5S, 16S, and 23S ribosomal RNA genes were identified using Prokka 1.14.6 (options: --rawproduct --cpus 8 --mincontiglen 200). To evaluate the measurement accuracy of the reference genome, each assembled sequence was mapped to the reference genome using minimap2 2.17 with default options. The mapping results were converted to bed format using bedtools v2.29.2, and the genome coverage rate L for the reference genome was calculated. When the reference genome size is G and the assembled sequence size is A, the precision (P), recall (R), and F score (F1score) were calculated using the following formula.

number

[0118] (Completeness and contamination level) Figure 5 shows the integrated index of completeness and contamination of genome sequences obtained by each method. The index was the average z-score (zx) of completeness and z-score (zy) of contamination for all genome sequences. Here, when the completeness vector is x (%), the z-score (zx) of completeness is: zx=(x-mx) / sx (mx is the average of x calculated from the data obtained by the existing method and the method of the present disclosure, and sx is the standard deviation of completeness). Similarly, when the contamination level vector is y (%), the z-score (zy) of the contamination level is zy=(y-my) / sy (my is the average of y calculated from the data obtained by the existing method and the method of the present disclosure, and sy is the standard deviation of the contamination level). However, the contamination level is multiplied by -1. In existing methods (conococt, maxbin2, metabat2, metawrap, dastool), the level of either completeness or contamination level is not high, so some of the integrated indices of completeness and contamination level of genome sequences are below the overall average value of 0. On the other hand, the genome sequences (hybrid-contig) disclosed herein are all above 0, i.e., they show a high level of value.

[0119] (Obtaining rRNA and tRNA) Figure 6 shows the degree of rRNA and tRNA acquisition. Here, for each type of rRNA (e.g., 5S, 16S), when the longest length that can be extracted is r, the z-score (zr), which indicates the degree of acquisition of each rRNA, is: zr=(r-mr) / sr (mr is the average of r calculated from data obtained by the existing method and the method of the present disclosure, and sr is the standard deviation of the degree of rRNA acquisition). Similarly, for tRNA, when the number of types obtained is t, the z-score (zt) of tRNA is zt=(t-mt) / st (mt is the average of t calculated from the data obtained by the existing method and the method of the present disclosure, and st is the standard deviation of the number of types that could be obtained).

[0120] In addition to the integrity and contamination of rRNA and tRNA, the acquisition of rRNA and tRNA is an important indicator of genome quality. For all genome sequences, the acquisition index of rRNA and tRNA was calculated using the average value of the z-score for the longest rRNA length and the type of tRNA. Compared to existing metagenomic analysis methods, the method disclosed herein shows a high value for the acquisition index of rRNA and tRNA.

[0121] (Reference genome reproducibility) FIG. 7 shows the measurement accuracy of the reference genome. The F1score, which is an accuracy index, is an index showing the harmonic mean of the precision and recall of the genome sequences obtained by the conventional method and the method of the present disclosure with respect to the reference genome sequence, and the genome sequence obtained by the method of the present disclosure shows the highest value. FIG. 8 shows the number of mismatches per 100 kb when the genome sequences obtained by the conventional method and the method of the present disclosure are mapped to the reference genome. The more accurately the genome sequence is reproduced, the fewer the number of mismatches. In this index, too, the genome sequence obtained by the method of the present disclosure shows the lowest value. These results show that the method of the present disclosure can obtain a genome sequence with higher accuracy than the conventional method.

[0122] Example 2 (Sample acquisition) Human fecal samples were used in Example 2. Samples were collected using Techno Suruga Lab's collection kit and collection container, and the subsequent analysis was performed.

[0123] (Single-cell genomic DNA preparation and DNA sequencing) Microbial single-cell isolation from fecal samples, lysis of isolated single cells, and genomic DNA amplification were performed using the SAG-gel method (Chijiiwa, 2020). DNA sequences were obtained by 300-cycle paired-end sequencing using an Illumina HiSeq (total number of reads: 49M, total base size: 6.5Gb).

[0124] (Single cell genome analysis) Low-quality reads were removed from 96 single-cell genomic reads from fecal samples using bbduk.sh 38.79 (options: threads=4, qtrim=r, trimq=10, minlength=40, maxns=1). Reads were de novo assembled using SPAdes 3.14.0 (options: --sc --careful --disable-rr --disable-gzip-output -t 4 -m 32), and contigs shorter than 1000 bp were excluded from further analysis.

[0125] (Single-cell genome phylogenetics and integration) From the obtained single-cell genome assembled sequences, 60 sequences with CheckM completeness of 20% or more and contamination level of less than 10% were extracted. For the 60 targets, ANI (Average Nucleotide Identity) was calculated by brute force using fastANI 1.3 with default options. In addition, the homology of common single-copy marker genes obtained by CheckM v1.1.2 taxonomy work flow (options: -nt --tab_table -t 16 domain Bacteria) was calculated using blastn 2.9.0+ with default options. Single cells with ANI of 95% or more and single-copy marker gene homology of 99.9% or more were regarded as the same strain and grouped. As a result, genome sequences of 18 different strains were obtained.

[0126] (Metagenomic DNA preparation and DNA sequencing) Total DNA was extracted from fecal samples according to IHMS protocol Q. A sequencing library was constructed using the QIAseq FX DNA library kit. DNA sequences were obtained by 300-cycle paired-end sequencing using an Illumina HiSeq (total number of reads: 40M, total base size: 6.0Gb).

[0127] (Metagenomic sequence analysis) One metagenomic read from a fecal sample was purified using bbduk.sh 38.79 (options: threads=4, qtrim=r, trimq=10, minlength=40, maxns=1) to remove low-quality reads. De novo assembly of the reads was performed using MEGAHIT v1.1.13 (options: -t 12).

[0128] (Assembled sequence mapping) The integrated sequences of the 18 single-cell genomes were mapped to the metagenomic assembled sequence using BWA 0.7.17 with default options. Each contig of the metagenomic assembled sequence was matched to the integrated sequence with the most contig coverage. 8,178 metagenomic contigs (total base size 34.9 Mb) were identified based on their correspondence to the integrated sequence.

[0129] (Assembled sequence merging) The integrated sequence and metagenomic assembled sequence were combined using CSAR v1.1.1. The single-cell genome integrated sequence was specified as the target, and the metagenomic assembled sequence was specified as the reference (Figure 3). The -nuc option was used.

[0130] (Comparison of the integrated sequence with the combined high-quality genome sequence) Figure 9 shows a comparison of the accuracy of the integrated sequence of the single-cell genome and the genome sequence that was improved by combining the metagenomic assembled sequence. csag represents the integrated sequence, and hybrid represents the combined high-quality genome sequence. While the genome size was almost the same for both, the number of contigs decreased in the hybrid and the N50 increased in the hybrid. In other words, it shows that the contigs of the divided single-cell genome integrated sequence were combined by the metagenomic assembled sequence to form a genome sequence of higher quality. In addition, the integrated index of the completeness and contamination level of the genome sequence and the index of rRNA and tRNA acquisition were also evaluated, and the results showed that both indices were equivalent to the combined genome sequence (hybrid) and did not decrease from the metagenomic assembled sequence (Figure 12).

[0131] Example 3 Here, we show an example of a method for sorting metagenomic reads by single-cell genome assembled or integrated sequences. Single-cell genome reads and metagenomic reads are obtained for the same sample, and assembled and integrated sequences are obtained from the single-cell genome reads. The metagenomic reads are mapped to the assembled and integrated sequences obtained here and sorted. The sorted metagenomic reads are assembled to obtain a sorted assembled sequence. These are then combined with the corresponding single-cell genome assembled and integrated sequences to obtain a final high-quality genome sequence (Figure 4).

[0132] Example 4 (Usage Data) Here, analysis was performed using the data of the standard bacterial cocktail obtained in Example 1. Sample acquisition, preparation and DNA sequencing of single-cell genomic DNA, preparation and DNA sequencing of metagenomic DNA, single-cell genome analysis, single-cell genome phylogenetic classification and integration, and metagenomic sequence analysis were the same as in Example 1. In this example, analysis was performed using data identified as Pseudomonas putida (accession number: GCF_000412675.1).

[0133] (Identification of chimeric sequences using reference genome sequences) Single-cell genome reads were mapped against the reference genome sequence of Pseudomonas putida, a standard bacterial cocktail. The mapping tool used was minimap2, and analysis options were set to default. Correct chimeric sequences were identified.

[0134] (Identification of chimeric sequences by conventional methods) Chimeric sequences of single-cell genome reads were identified using the ccSAG method (Kogawa, 2018). When creating single-cell genome integrated sequences, identical strains of single-cell genome assembled sequences are identified based on marker genes, ANI, etc. Corresponding single-cell genome assembled sequences identified as identical strains were obtained. Results of mapping single-cell genome reads to single-cell genome assembled sequences other than themselves were obtained. Minimap2 was used as the mapping tool, and analysis options were set to default.

[0135] Chimeric Sequence Identification by the Methods of the Disclosure We obtained the results of mapping single-cell genome reads against metagenomic assembled sequences using the minimap2 mapping tool and the default analysis options.

[0136] (Identification of chimeric sequences) In this example, chimeric sequences were identified by analyzing the CIGAR string described in the SAM format, which is the mapping result. Reads containing soft clip, hard clip, and N characters in the CIGAR string were identified as chimeric sequences. In addition, soft clip means that some reads are not mapped to the genome, and the unmapped parts remain as information in the read, while hard clip means that some reads are not mapped to the genome, and the unmapped parts are removed from the read. In the case of soft clipped reads, the mapped and unmapped parts are divided into two reads and reused, and in the case of hard clip, the unmapped parts are already missing from the read, so they are reused as one read as they are. The N character indicates how far apart the mapped locations are when a particular read is mapped to a separate location. For example, if it appears in the form of 25M5000N25M, this means that a 50bp read is mapped by 25bp, 25bp, but is mapped 5000bp apart. In addition, if the CIGAR string is 45M23S, the left 45 bases of the read are mapped and the right 23 bases are not mapped. This is considered to be a chimeric sequence that combines independent sequences of 45 and 23 bases.

[0137] (Compared to conventional methods) 11 is a plot of chimeric sequences that could be identified in the same way as chimeric sequence identification using a reference genome sequence, chimeric sequences that were detected only by chimeric sequence identification using a reference genome sequence, and chimeric sequences that were not detected by chimeric sequence identification using a reference genome sequence, in the conventional method and the method disclosed herein. The number of chimeric sequences that could be correctly identified by the method disclosed herein was 164,019, which was 18.3% more than the 138,682 detected by the conventional method. In addition, the accuracy rate was greatly improved from 76.7% by the conventional method to 94.4%.

[0138] Example 5 The following is an example of the use of the present invention in a sample analysis contract.

[0139] (Receipt of specimen) Receive fecal, saliva, skin, soil, or other specimens from the user in an appropriate manner. For example, for fecal specimens, a stool collection kit provided by Techno Suruga Lab may be used. For saliva specimens, an OMNIgene oral kit may be used. For skin specimens, a Metaflo Keeper provided by Techno Suruga Lab may be used. For soil specimens, the collected soil may be received in a refrigerated state.

[0140] (DNA extraction, preparation, and sequencing) The received samples will be divided into those for metagenomic analysis and those for single-cell genomic analysis, and DNA extraction, preparation, and DNA sequencing will be performed for each.

[0141] (Preparing the data analysis environment) Data analysis can be performed on Windows or Mac, but here we assume that it will be performed on a computer running the Linux (registered trademark) OS. When performing large-scale data analysis, it is desirable to use a high-performance computer that can perform parallel analysis. QC tools (bbduck.sh, fastqp, etc.), assembly tools (MEGAHIT, SPAdes, etc.), mapping tools (BEWA, HISAT2, MiniMap2, etc.), assembly evaluation tools (CheckM, QUAST, etc.), gene prediction tools (Prokka, DFAST, etc.), function estimation tools (antiSMASH, ABRicate), and other utilities (BLAST, SAMtools, BEDtools, etc.) are installed.

[0142] (Obtaining high-quality genomes through cross-referencing) The obtained metagenomic reads are assembled to obtain a metagenomic assembled sequence. The single-cell genome reads are mapped to the metagenomic assembled sequence to remove chimeras. The chimera-removed single-cell genome reads are assembled to obtain a single-cell genome assembled sequence. The single-cell genome assembled sequence is integrated using ccSAG or other methods to create a single-cell genome integrated sequence. The metagenomic assembled sequence and single-cell genome integrated sequence are matched by mapping or other methods, and the metagenomic assembled sequence is sorted. A high-quality genome at the strain level is obtained by combining the metagenomic sorted sequence and the single-cell genome integrated sequence.

[0143] (Gene prediction and function estimation) Run gene prediction tools on the high-quality genomes to obtain gene data. Run function prediction tools on the high-quality genome sequences or gene data to identify genes that confer drug resistance, identify biosynthetic gene clusters, etc.

[0144] (Providing analytical data) The data obtained from the analysis (high-quality genome sequences and associated genetic data, etc.) will be provided to users by downloading them from the cloud, or by mail using a portable hard drive or other device.

[0145] Example 6 (Web Services Overview) This example refers to a web application that integrates and analyzes metagenomic data and single-cell genome data measured independently by the user to return a high-quality genome sequence. The web server can be Apache or nginx. Since the actual calculations are a heavy load, a calculation server is prepared in addition to the web server. The calculation status is managed by the calculation server, and the web server is queried to inform the user of the calculation status. After the calculation is completed, the user can obtain the output via the web.

[0146] (Input data) The data the service will accept could include metagenomic reads and single-cell genome reads, or metagenomic assembled sequences and single-cell genome assembled sequences.

[0147] (Output data) Users will be able to download high-quality genome sequences, with optional predicted gene data and gene function information.

[0148] (Note) Although the present disclosure has been illustrated using the preferred embodiments thereof, it is understood that the present disclosure should be interpreted in scope only by the claims. It is understood that the patents, patent applications, and other documents cited in this specification are incorporated by reference into this specification in the same manner as if the contents themselves were specifically set forth herein. [Industrial Applicability]

[0149] The present disclosure finds applicability in the field of genetic technology.

Claims

1. A method for analyzing a sequence of a population of organism units comprising a plurality of unit organism units, comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, sorting and / or merging and / or removing chimeras from original genome sequences longer than the reads for A) and B), respectively; D) A step of performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, The combination analysis includes: aligning the single-cell genome fragment sequence with the metagenomic fragment sequence by mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing the metagenomic fragment sequence, dividing the single-cell genome fragment sequence into an aligned portion and a non-aligned portion, and identifying the single-cell genome fragment sequence including the aligned portion and the non-aligned portion as a chimeric sequence; and removing the chimeric sequence or reusing the sequence of the aligned portion of the chimeric sequence to obtain an improved reconstructed genome sequence. method.

2. The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, The method of claim 1.

3. The method of claim 1 or 2, wherein the combined analysis comprises fractionating metagenomic fragment sequences or sequences derived therefrom with single-cell genome fragment sequences or sequences derived therefrom.

4. The method of any one of claims 1 to 3, wherein the combination analysis comprises integration of single cell genome fragment sequences or sequences derived therefrom with metagenomic fragment sequences or sequences derived therefrom.

5. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) A step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, and integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to generate a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence separated from a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The method according to any one of claims 1 to 4, comprising:

6. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The method according to any one of claims 1 to 5, comprising:

7. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing the original genome sequence longer than the read for B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The method according to any one of claims 1 to 6, comprising:

8. A program for causing a computer to implement a method for analyzing a sequence of a population of organism units including a plurality of unit organism units, the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, separating, integrating and / or removing chimeras from original genome sequences longer than the fragment sequences for A) and B), respectively; D) A step of performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, the combination analysis includes aligning the single-cell genome fragment sequence with the metagenomic fragment sequence by mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing a metagenomic fragment sequence, dividing the single-cell genome fragment sequence into an aligned portion and a non-aligned portion, and identifying the single-cell genome fragment sequence including an aligned portion and a non-aligned portion as a chimeric sequence, and removing the chimeric sequence or reusing the sequence of the aligned portion of the chimeric sequence to obtain an improved reconstructed genome sequence.

9. The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, The program according to claim 8.

10. The program according to claim 8 or 9, wherein the combination analysis comprises separating a metagenomic fragment sequence or a sequence derived therefrom using a single-cell genome fragment sequence or a sequence derived therefrom.

11. The program according to any one of claims 8 to 10, wherein the combination analysis comprises integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom.

12. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) A step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, and integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to generate a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence separated from a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The program according to any one of claims 8 to 11, comprising:

13. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The program according to any one of claims 8 to 12, comprising:

14. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing an original genome sequence longer than the fragment sequence of B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The program according to any one of claims 8 to 13, comprising:

15. A recording medium storing a program for causing a computer to implement a method for analyzing a sequence of a population of organism units including a plurality of unit organism units, the method comprising: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) Reconstructing, separating, integrating and / or removing chimeras from original genome sequences longer than the fragment sequences for A) and B), respectively; D) A step of performing a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, the combination analysis comprises: aligning the single-cell genome fragment sequence with the metagenomic fragment sequence by mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing a metagenomic fragment sequence, dividing the single-cell genome fragment sequence into an aligned portion and a non-aligned portion, and identifying the single-cell genome fragment sequence including the aligned portion and the non-aligned portion as a chimeric sequence; and removing the chimeric sequence or reusing the sequence of the aligned portion of the chimeric sequence to obtain an improved reconstructed genome sequence.

16. The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, The recording medium according to claim 15.

17. The recording medium according to claim 15 or 16, wherein the combination analysis comprises fractionating a metagenomic fragment sequence or a sequence derived therefrom using a single-cell genome fragment sequence or a sequence derived therefrom.

18. The recording medium according to any one of claims 15 to 17, wherein the combination analysis comprises integration of a single-cell genome fragment sequence or a sequence derived therefrom with a metagenomic fragment sequence or a sequence derived therefrom.

19. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) A step of separating the sequence obtained by reconstructing the metagenomic fragment sequence, and integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to generate a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, wherein the single-cell genome fragment sequence is referred to when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) generating a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) combining the separated fragment sequence with a sequence separated from a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The recording medium according to any one of claims 15 to 18, comprising:

20. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing original genome sequences longer than the fragment sequences for each of A) and B); C-2) integrating the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) generating a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence from the sequence obtained by reconstructing the metagenome fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) A step of combining a sequence obtained by classifying the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The recording medium according to any one of claims 15 to 19, comprising:

21. The method comprises: A) performing metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) performing single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) Reconstructing an original genome sequence longer than the fragment sequence of B); C-2) integrating the sequences obtained in C-1) to generate a sequence obtained by integrating sequences obtained by reconstructing single-cell genome fragment sequences; C-3) generating a fractionated fragment sequence of the metagenomic fragment sequence from the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A step of reconstructing an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) A step of combining a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The recording medium according to any one of claims 15 to 20, comprising:

22. A system for analyzing a sequence of a population of organism units including a plurality of organism units, the system comprising: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C) a sequence processing unit that performs reconstruction, sorting, integration and / or chimera removal of original genome sequences longer than the metagenome fragment sequences and single-cell genome fragment sequences, respectively; D) An improved genome sequence generation unit that performs a combination analysis using a sequence including at least a metagenomic fragment sequence or a sequence derived therefrom and a single-cell genome fragment sequence or a sequence derived therefrom to obtain an improved reconstructed genome sequence; Including, The system includes the steps of: aligning the single-cell genome fragment sequence with the metagenomic fragment sequence by mapping the single-cell genome fragment sequence to a sequence obtained by reconstructing the metagenomic fragment sequence, dividing the single-cell genome fragment sequence into an aligned portion and a non-aligned portion, and identifying the single-cell genome fragment sequence including the aligned portion and the non-aligned portion as a chimeric sequence; and removing the chimeric sequence or reusing the sequence of the aligned portion of the chimeric sequence to obtain an improved reconstructed genome sequence.

23. The metagenomic fragment sequence or a sequence derived therefrom includes a metagenomic fragment sequence, a sequence obtained by reconstructing the metagenomic fragment sequence, and a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence, The single cell genome fragment sequence or a sequence derived therefrom includes a single cell genome fragment sequence, a sequence obtained by reconstructing a single cell genome fragment sequence, and a sequence obtained by integrating a sequence obtained by reconstructing a single cell genome fragment sequence, 23. The system of claim 22.

24. The system of claim 22 or 23, wherein the combined analysis comprises fractionating metagenomic fragment sequences or sequences derived therefrom with single-cell genome fragment sequences or sequences derived therefrom.

25. The system of any one of claims 22 to 24, wherein the combination analysis comprises integration of single-cell genome fragment sequences or sequences derived therefrom with metagenomic fragment sequences or sequences derived therefrom.

26. The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) a reconstruction unit that reconstructs original genome sequences longer than the fragment sequences for A) and B), respectively; C-2) A separation and integration unit that separates the sequence obtained by reconstructing the metagenomic fragment sequence, integrates the sequence obtained by reconstructing the single-cell genome fragment sequence, and generates a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence, and refers to the single-cell genome fragment sequence when separating the sequence obtained by reconstructing the metagenomic fragment sequence; C-3) a generation unit that generates a fractionated fragment sequence from a sequence obtained by fractionating the sequence obtained by reconstructing the metagenomic fragment sequence, by referring to the metagenomic fragment sequence; D) an improved genome sequence generating unit that combines the separated fragment sequence with a sequence separated from a sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The system according to any one of claims 22 to 25, comprising:

27. The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) a reconstruction unit that reconstructs original genome sequences longer than the fragment sequences for A) and B), respectively; C-2) an integration unit that integrates the sequences obtained by reconstructing the single-cell genome fragment sequences to generate a sequence obtained by integrating the sequences obtained by reconstructing the single-cell genome fragment sequences; C-3) A generation unit that generates a sequence obtained by separating the sequence obtained by reconstructing the metagenomic fragment sequence from the sequence obtained by reconstructing the metagenomic fragment sequence by referring to the sequence obtained by reconstructing the single-cell genome fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; D) an improved genome sequence generation unit that combines a sequence obtained by separating the sequence obtained by reconstructing the metagenome fragment sequence and a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence to obtain an improved reconstructed genome sequence; The system according to any one of claims 22 to 26, comprising:

28. The system comprises: A) a metagenomic fragment sequence generator that performs metagenomic analysis on the organism unit population to obtain a metagenomic fragment sequence; B) a single-cell genome fragment sequence generation unit that performs single-cell genome analysis on the organism unit population to obtain single-cell genome fragment sequences derived from the plurality of unit organism units; C-1) A reconstruction unit that reconstructs an original genome sequence longer than the fragment sequence of B); C-2) an integration unit that integrates the sequences obtained in C-1) to generate an integrated sequence of sequences obtained by reconstructing single-cell genome fragment sequences; C-3) a fractionated fragment sequence generation unit that generates a fractionated fragment sequence of the metagenomic fragment sequence by referring to a sequence obtained by reconstructing the single-cell genome fragment sequence from the metagenomic fragment sequence and / or a sequence obtained by integrating the sequence obtained by reconstructing the single-cell genome fragment sequence; C-4) A generator that reconstructs an original genome sequence longer than the read for the fractionated fragment sequence of the metagenomic fragment sequence; D) an improved genome sequence generation unit that combines a sequence obtained by reconstructing the fractionated fragment sequences of the metagenome fragment sequences and a sequence obtained by integrating a sequence obtained by reconstructing the single-cell genome fragment sequences to obtain an improved reconstructed genome sequence; The system according to any one of claims 22 to 27, comprising:

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