Analyse de données métagénomiques shotgun

Module 24

Olivier Rué

MaIAGE

Valentin Loux

MaIAGE

Cédric Midoux

PROSE & MaIAGE

March 18, 2024

Introduction

Practical informations

  • 9h00 - 17h00
  • 2 breaks morning and afternoon
  • Lunch at INRAE restaurant (not mandatory)
  • Questions allowed
  • Everyone has something to learn from each other

Diffusion of documents

All documents presented during this training course are intended for distribution, and are available on https://documents.migale.inrae.fr

Better know you

Who are you?

  • Institution / Laboratory / position

What is your scientific topic?

  • Studied ecosystem
  • Scientific question
  • Experimental design

What is your background?

  • Already treated shotgun data?
  • Background in bioinformatics?

Better know us

  • Open infrastructure dedicated to life sciences
    • Computing resources, tools, databanks…
  • Dissemination of expertise in bioinformatics
  • Design and development of applications
  • Data analysis

Data analysis service

  • We are specialized in genomics/metagenomics
  • Bioinformaticians and Statisticians
  • More than 120 projects since 2016
  • Computational documents used and distributed for reproducibility
  • 2 types of services

Collaboration

We take care of the analyses

Accompaniment

We help you to perform the analyses

Training objectives

  • Know the advantages and limits of shotgun metagatenomics data analysis
  • Identify tools to analyze your data
  • Run tools with migale resources

Program

Day 1

  • Introduction
  • Reminders
  • QC
  • Taxonomic classification

Day 2

  • Cleaning
  • Assembly
  • Annotation

The truth about bioinformatics

Introduction to metagenomics analyses

Introduction

Introduction

Introduction

Challenges

  • Complexity of the ecosystem
  • Completeness of databases
  • Sequencing depth
  • Computational resources required
  • …

Classical metagenomics approach

Toy datastet presentation

Home-made dataset

  • 5 samples
  • 1M or 2M reads per sample
  • 50 Bacteria, 10 Archaea, 4 Viruses
  • Illumina Hiseq 2x125 bp
  • Uniform or log-normal abundance
conda activate insilicoseq-1.5.4
# après un premier tirage aléatoire permettant de récupérer 50 génomes bactériens, 4 génomes viraux et 10 génomes d'archées
iss generate --seed 13062022 -k bacteria viruses archaea -U 50 4 10  --model hiseq --output s1 -n 2000000 --abundance_file hiseq_ncbi_abundance.txt
iss generate --seed 14062022 --genomes s1_genomes.fasta  --model hiseq --output s1 -n 2000000 --abundance_file s1_abundance.txt -z
iss generate --seed 14062022 --genomes s2_genomes.fasta  --model hiseq --output s2 -n 2000000 --abundance_file s2_abundance.txt -z
iss generate --seed 14062022 --genomes s1_genomes.fasta  --model hiseq --abundance uniform --output s3 -n 2000000 -z
iss generate --seed 14062022 --genomes s2_genomes.fasta  --model hiseq --abundance uniform --output s4 -n 1000000 -z
iss generate --seed 14062022 --genomes s1_genomes.fasta  --model hiseq --output s5 -n 2000000 --abundance_file s1_abundance.txt -z

Dataset composition

Reminders about command line

The migale facility

  • working directories (save/work/home)
  • SGE submission system (qsub/qstat)
  • conda environments (conda activate)

All usefull information

Main commands

cd
ls
rm
cp
scp
ln -s
mv


..
.
../../
qstat
qsub
qarray
qacct


conda activate
conda info

Switch to TP 1

Reminders about sequencing

Generations

Note

We will only deal with short reads during this training

Reminders about Illumina sequencing

Sequencing - Vocabulary

  • Read: piece of sequenced DNA
  • DNA fragment: 1 or more reads depending on whether the sequencing is single- or paired-end
  • Insert: Fragment size
  • Depth: \(\frac{N * L}{G}\)
    \(N =\) number of reads
    \(L =\) reads size
    \(G =\) genome size
  • Coverage: % of genome covered

FASTQ format

@ST-E00114:1342:HHMGVCCX2:1:1101:3123:2012 1:N:0:TCCGGAGA+TCAGAGCC
CTTGGTCATTTAGAG
+
***<<*AEF???***
@ST-E00114:1342:HHMGVCCX2:1:1101:11556:2030 1:N:0:TCCGGAGA+TCAGAGCC
CATTGGCCATATCAT
+
AAAE??<<*???***

Meaning

@Identifier1 (comment)
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
@Identifier2 (comment)
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+
QQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ

Quality score encoding

Quality score

Measure of the quality of the identification of the nucleobases generated by automated DNA sequencing

FASTQ compression

  • Compression is essential to deal with FASTQ files (reduce disk storage)
  • extension: file.fastq.gz
  • Tools are (almost all) able to deal with compressed files

Quality control

Quality control

  • One of the most easy step in bioinformatics …
  • … but one of the most important
  • check if everything is ok
  • Indicates if/how to clean reads
  • Shows possible sequencing problems
  • The results must be interpreted in relation to what has been sequenced

Reads are not perfect

Why QC’ing your reads?

Try to answer to (not always) simple questions:

  • Are data conform to the expected level of performance?
    • Size / Number of reads / Quality
  • Residual presence of adapters or indexes?
  • (Un)expected techincal biases?
  • (Un)expected biological biases?

Warning

Using callouts is an effective way to highlight content that your reader give special consideration or attention.

Switch to TP 2

Taxonomic affiliation

Taxonomic affiliation

Definition

Taxonomic assignment in the context of bioinformatics involves the computational identification and classification of organisms into their taxonomic groups using various data sources, such as DNA sequences, protein sequences, or other molecular markers. This process typically utilizes algorithms and computational tools to compare sequences against reference databases or phylogenetic trees, allowing for accurate identification and classification of organisms at different taxonomic levels.

Kraken

Kraken [5] is a very popular taxonomic affiliation tool. It is very fast and accurate. Kraken examines the k-mers (~35 bp) within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps k-mers to the lowest common ancestor (LCA) of all genomes known to contain the given k-mer.

Method:

  1. Chop genomes into k-mers and link to a taxonomic id.
  2. Chop reads into k-mers and search for exact hits in database
  3. Search for highest-wighted root-to-leaf paths and assign the taxonomic id of the lowest node to read

Kraken

Kaiju

Kaiju [6] is an equivalent of Kraken, but with some particularities:

  • Database of proteic sequences
  • Supposed to be more sensitive
  • Translate reads in all six reading frames, split at stop codons
  • Use BWT and FM-index table

Kaiju databanks

Taxonomic classification caveats

  • Databanks
  • K-mer choice (sensitivity / specificity)
  • Allow a “fast” overview of your data
    • Contaminants?
    • Host reads?
    • Classification rate

Some taxonomic affiliation tools

tool migale comments
Kraken2 ✔️ the reference, fast and efficient
Kaiju ✔️ protein level
Bracken ✔️ Bayesian Reestimation of Abundance with Kraken
Centrifuge ✔️ indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem
MetaPhlAn3 ✔️ MetaPhlAn relies on unique clade-specific marker genes identified from ~17,000 reference genomes (~13,500 bacterial and archaeal, ~3,500 viral, and ~110 eukaryotic)

Switch to TP 3

Reads cleaning

Quality/Length/Adapters

  • Detect and remove sequencing adapters present in the FASTQ files
  • Filter / trim reads according to quality (seen with FastQC)

rRNA removal

  • Mandatory step if you want to skip rRNA reads

Contamination

Definition

Contamination corresponds to the presence of DNA that does not come from the sample studied.

  • Recognizing contamination, followed by appropriate decontamination, should be a critical first step for all microbiome analyses.
  • Skipping this step can easily result in confounded results and false conclusions.

Contamination origins

External contaminants

  • negative controls (i.e., blank reagent controls) during sample collection, preparation, and/or sequencing
  • in silico detection (taxonomic affiliation, mapping…)

Well-to-well contamination

  • Use blanks as negative controls [8]
  • Study the sharing of strains between samples [9]
  • Compare abundance profiles (CroCoDeEL, in dev.)

Cleaning tools

Reads cleaning

tool migale comments
fastp [10] ✔️ all in one
pear [11] ✔️ for merging reads
sickle [12] ✔️ adaptative trimming

Decontamination tools

tool migale comments
kneaddata [13] ✔️ remove rRNA reads
sortmerna [14] ✔️ remove rRNA reads, slow…
HoCoRT [15] ✔️ choice between several aligners, easy to use
DeconSeq ❌
GenCoF ❌

Switch to TP 4

Metagenomics assembly

Objectives

  • Reconstruct genes and organisms from complex mixtures
  • Dealing with the ecosystem’s heterogeneity, multiple genomes at varying levels of abundance
  • Limiting the reconstruction of chimeras

Assembly strategies

Co-assembly?

  • Usefull in some cases:
    • differences in coverage between samples
Pros of co-assembly Cons of co-assembly
More data Higher computational overhead
Better/longer assemblies Risk of shattering the assembly
Access to lower abundant organisms Risk of increased contamination

Co-assembly

In these cases, co-assembly is reasonable if:

  • Same samples
  • Same sampling event
  • Longitudinal sampling of the same site
  • Related samples

If it is not the case, individual assembly should be prefered. In this case, an extra step of de-replication should be used

Tools

  • Generic tool with a meta option : SPAdes and metaSPAdes [16]
  • Tools requiring less memory : MEGAHIT [17]
  • The historical tool allowing many parameters : Velvet (and MetaVelvet)
  • A (not so) recent benchmark of short reads metagenome assemblers. [18]
  • Long read / Hybrid assemblies use different algorithms and strategies and are still a research question.

Assessment of assembly quality

After assembly, we use MetaQUAST [19] to evaluate and compare metagenome assemblies.

What MetaQUAST does :

  • De novo metagenomic assembly evaluation
  • [Optionally] identify reference genomes from the content of the assembly
  • Reference-based evaluation
  • Filtering so-called misassemblies based on read mapping
  • Report and visulaisation

De novo metrics

Evaluation of the assembly based on:

  • Number of contigs greater than a given threshold (0, 1kb, …)
  • Total / thresholded assembly size
  • Largest contig size
  • N50 : the sequence length of the shortest contig at 50% of the total assembly length, equivalent to a median of contig lengths. (N75 idem, for 75%)
  • L50 : the number of contigs at 50% of the total assembly length. (L75 idem, for 75%)

Reference-based metrics

  • Metrics based on the comparison with reference genomes.
  • Reference genomes are given by the user or automatically constitued by MetaQuast based on comparison of rRNA genes content of the assembly and a reference database (Silva).
  • Complete genomes are then automatically downloaded.

Reference-based metrics

For each given reference genome, based on an alignement of all contigs on it :

  • Duplication rate
  • Percent genome complete
  • NGA50 : equivalent of N50 but with the aligned block of the contigs
  • “Misassemblies” : breakpoint of alignement in a contigs.
  • An individual Quast report and an alignement visualisation.

Counts on assembly

  • We used only a portion of the reads for assembly.
  • For further analyses it is necessary to map all the reads on the contigs to obtain counts per features (genes/contigs…)
  • We will use BWA [20]
    • Firstly, we build an index.
    • Secondly, reads are aligned.
    • We can use samtools [21] and bedtools [22] to manipulate SAM/BAM files.

Switch to TP 5

Binning

Objectives

Binning is a good compromise when the assembly of whole genomes is not feasible.

Similar contigs are grouped together.

Approach

  • MetaBAT [23] is a tool for reconstructing genomes from complex microbial communities.

Bins evaluation

For the evaluation of bins, we will use completeness and contamination estimated by CheckM [24]

  • Use of collocated sets of genes that are ubiquitous and single-copy within a phylogenetic lineage.
  • Among a set of tools in CheckM we will use the checkm2 predict workflow which only mandatory requires a directory of genome bins.

Switch to TP 6

Annotation

Objective

  • Syntaxic annotation (gene prediction)
  • Functionnal annotation (function prediction for protein coding genes)
  • Prokka [25] is a is a software tool to annotate bacterial, archaeal and viral genomes (quite) quickly and produce standards-compliant output files.
  • Prokka automatically annotate a complete bacterial genome in ~15mn.
  • Prokka will not replace expert annotation but gives you an homogenoeus procedure for annotation of conserved genes familly

Genes prediction (1)

  • Gene prediction in complete prokaryotic genomes isn’t as such a problem.
    • Most errors / difference in start position
  • Efficient gene predictors are available (bactgeneSHOW, prodigal, glimmerHMM,…).
  • Most of them uses HMM (Hidden Markov Models) models to predict the gene structure

Genes prediction (2)

  • Gene prediction on metagenomes is difficult due to :
    • assembly fragmentation
    • assembly errors, frameshift, chimeras,…
    • different species in the same sample that could/should lead to use different models
    • a mix of viral, bacterial and eukaryotic sequences.
  • Prodigal (with the -meta parameter) and fraggenescan have good enough results on metagenomic contigs.
  • Caution to partial genes in the following analysis !

Prokka pipeline

  • Coding gene prediction with Prodigal [26]
  • tRna; rRna gene prediction with Aragorn, Barnap, RNAmmer (optionnal)
  • Functionnal annotation based on similarity search with a threshold (1e-6) and hierarchically against :
    • [Optionnal] a given proteome ( --proteins parameter)
    • ISFinder for transposases, not entire IS
    • NCBI Bacterial Antimicrobial Resistance Reference Gene Database for Antimicrobial Resistance Genes.
    • UniprotKB/Swissprot curated proteins with evidence that (i) from Bacteria (or Archaea or Viruses); (ii) not be “Fragment” entries; and (iii) have an evidence level (“PE”) of 2 or lower, which corresponds to experimental mRNA or proteomics evidence.
    • Domain and motifs (hmmsearch) :

Prokka pipeline

EggNogg Mapper

  • eggNOG-mapper [27] is a tool for fast functional annotation of novel sequences. It uses precomputed orthologous groups and phylogenies from the eggNOG database to transfer functional information from fine-grained orthologs only.
  • eggNog database :
    • 5090 organisms, 2502 viruses.
    • 4.4 M orthologous groups annotated with COG category, Gene Ontolgy, EC number, Kegg orthologs and pathways, CAZy, PFAMs

EggNogg Mapper workflow

  • eggNOG uses hmmsearch to search against HMM eggNOG database OR Diamond / MMSeqs2 to search against eggNOG protein database
  • Refine first hit using a list of precomputed orthologs.
  • Assign one ortholog
  • Functionnal annotation transfer using this ortholog

Other options for functionnal annotation

  • Diamond [28] is a sequence aligner for protein (equivalent blastp) and translated DNA (equivalent tblastx) searches, designed for high performance analysis of big sequence data. Diamond is 100x to 20,000x the speed of BLAST.
    • Diamond could be used to query against any given databank.
  • ghostKOALA [29, Online]
  • KOALA (KEGG Orthology And Links Annotation) is KEGG’s internal annotation tool for K number assignment.

Other options for functionnal annotation (2)

  • cd-hit [30] is a software for clustering protein sequences. It can be used to downsize the number of lines in the in the gene count matrix.

Switch to TP 7

Biostatistics

What to do after bioinformatics

Automatization

Snakemake workflow

We have developed a workflow that allows us to automate all these analyses.

  • developed with snakemake
  • executable on MIGALE
{
    "SAMPLES": ["mock"],
    "NORMALIZATION": true,
    "SORTMERNA": true,
    "ASSEMBLER": "metaspades",
    "CONTIGS_LEN": 1000,
    "PROTEINS-PREDICTOR": "prodigal"
}

Other tools

  • Pear : merge paired-end
  • SimkaMin [32] : fast and resource frugal de novo comparative metagenomics
  • Linclus (MMseqs2) [33] : Clustering huge protein sequence sets in linear time.
  • PLASS[34] : Protein-Level ASSembler
  • …

GraftM: rapid community profiles from metagenomes

Anvi’o: integrated multi-omics at scale

  • Anvi’o [36] is an open-source, community-driven analysis and visualization platform for microbial ’omics.
  • With this tutorial, starting from a metagenomic assembly, you will :
    • Process your contigs,
    • Profile your metagenomic samples and merge them,
    • Visualize your data, identify and/or refine genome bins interactively, and create summaries of your results.

Metagenome atlas

  • Metagenome-atlas [37] is an easy-to-use metagenomic pipeline based on snakemake.
  • It handles all steps of analysis :
    mamba install -y -c bioconda -c conda-forge metagenome-atlas
    atlas init --db-dir databases path/to/fastq/files
    atlas run all

Metagenome atlas

Long reads metagenomics

Long reads metagenomics

Long reads metagenomics

Taxonomic classifiers

Long reads metagenomics

Functional annotation

Long reads metagenomics

  • Long read data can be used to improve assembly
  • Bottlenecks :
    • DNA extraction (?)
    • cost of data generation
    • sequencing errors
  • State of the art pipeline for assembly :
    • standalone long read assembly (ex: MetaFLYE [39])
    • optional error correction with short reads

Metatranscriptomics

Take home message

  • Shotgun metagenomics is still an ongoing active bioinformatics research field
  • Numerous software dedicated to assembly, binning, functional annotation are actively developed
  • Depending on the ecosystem , one can have different approaches :
    • mapping on a reference database
    • assembly and mapping
  • The biological question must determine the analysis

Need help?

References

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2. Breitwieser FP, Lu J, Salzberg SL. A review of methods and databases for metagenomic classification and assembly. Briefings in bioinformatics. 2019;20:1125–36.
3. Yang C, Chowdhury D, Zhang Z, Cheung WK, Lu A, Bian Z, et al. A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data. Computational and Structural Biotechnology Journal. 2021;19:6301–14. doi:https://doi.org/10.1016/j.csbj.2021.11.028.
4. Stoler N, Nekrutenko A. Sequencing error profiles of Illumina sequencing instruments. NAR Genomics and Bioinformatics. 2021;3. doi:10.1093/nargab/lqab019.
5. Wood DE, Salzberg SL. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome biology. 2014;15:1–12.
6. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with kaiju. Nature communications. 2016;7:11257.
7. Jurasz H, Pawlowski T, Perlejewski K. Contamination issue in viral metagenomics: Problems, solutions, and clinical perspectives. Frontiers in Microbiology. 2021;12:745076.
8. Minich JJ, Sanders JG, Amir A, Humphrey G, Gilbert JA, Knight R. Quantifying and understanding well-to-well contamination in microbiome research. mSystems. 2019;4:10.1128/msystems.00186–19. doi:10.1128/msystems.00186-19.
9. Lou YC, Hoff J, Olm MR, West-Roberts J, Diamond S, Firek BA, et al. Using strain-resolved analysis to identify contamination in metagenomics data. Microbiome. 2023;11:36.
10. Zhou Y, Chen Y, Chen S, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90. doi:10.1093/bioinformatics/bty560.
11. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate illumina paired-end reAd mergeR. Bioinformatics. 2013;30:614–20.
12. Joshi N, Fass J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011.
13. Lab H. KneadData is a tool designed to perform quality control on metagenomic and metatranscriptomic sequencing data, especially data from microbiome experiments. 2022. https://github.com/biobakery/kneaddata.
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15. Rumbavicius I, Rounge TB, Rognes T. HoCoRT: Host contamination removal tool. BMC bioinformatics. 2023;24:371.
16. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology. 2012;19:455–77. doi:10.1089/cmb.2012.0021.
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18. Vollmers J, Wiegand S, Kaster A-K. Comparing and evaluating metagenome assembly tools from a microbiologist’s perspective-not only size matters! PloS one. 2017;12:e0169662.
19. Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2015;32:1088–90. doi:10.1093/bioinformatics/btv697.
20. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:13033997. 2013.
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22. Quinlan AR, Hall IM. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.
23. Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
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25. Seemann T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9. doi:10.1093/bioinformatics/btu153.
26. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics. 2010;11:1–11.
27. Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Mering C von, et al. Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper. Molecular Biology and Evolution. 2017;34:2115–22. doi:10.1093/molbev/msx148.
28. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nature Methods. 2014;12:59–60. doi:10.1038/nmeth.3176.
29. Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. Journal of molecular biology. 2016;428:726–31.
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32. Benoit G, Mariadassou M, Robin S, Schbath S, Peterlongo P, Lemaitre C. SimkaMin: Fast and resource frugal de novo comparative metagenomics. Bioinformatics. 2019. doi:10.1093/bioinformatics/btz685.
33. Steinegger M, Söding J. Clustering huge protein sequence sets in linear time. Nature Communications. 2018;9. doi:10.1038/s41467-018-04964-5.
34. Steinegger M, Mirdita M, Söding J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nature Methods. 2019;16:603–6. doi:10.1038/s41592-019-0437-4.
35. Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Research. 2018;46:e59–9. doi:10.1093/nar/gky174.
36. Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: An advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319. doi:10.7717/peerj.1319.
37. Kieser S, Brown J, Zdobnov EM, Trajkovski M, McCue LA. ATLAS: A snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformatics. 2020;21. doi:10.1186/s12859-020-03585-4.
38. Kim C, Pongpanich M, Porntaveetus T. Unraveling metagenomics through long-read sequencing: A comprehensive review. Journal of Translational Medicine. 2024;22:111.
39. Kolmogorov M, Rayko M, Yuan J, Polevikov E, Pevzner P. metaFlye: Scalable long-read metagenome assembly using repeat graphs. 2019. doi:10.1101/637637.
40. Shakya M, Lo C-C, Chain PS. Advances and challenges in metatranscriptomic analysis. Frontiers in genetics. 2019;10:904.
41. Ojala T, Häkkinen A-E, Kankuri E, Kankainen M. Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics. Trends in Genetics. 2023;39:686–702.

Module 24 - Métagénomique shotgun