Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs.
Journal
Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147
Informations de publication
Date de publication:
01 Oct 2024
01 Oct 2024
Historique:
received:
29
04
2024
accepted:
07
08
2024
medline:
2
10
2024
pubmed:
2
10
2024
entrez:
1
10
2024
Statut:
epublish
Résumé
Metagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read. Here, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3-40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes. PenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities. Video Abstract.
Sections du résumé
BACKGROUND
BACKGROUND
Metagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read.
RESULTS
RESULTS
Here, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3-40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes.
CONCLUSION
CONCLUSIONS
PenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities. Video Abstract.
Identifiants
pubmed: 39354646
doi: 10.1186/s40168-024-01904-y
pii: 10.1186/s40168-024-01904-y
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
187Subventions
Organisme : National Research Foundation of Korea
ID : 2019R1A6A1A10073437
Organisme : Bundesministerium für Bildung und Forschung
ID : 031L0185
Organisme : European Research Council
ID : 685778
Pays : International
Informations de copyright
© 2024. The Author(s).
Références
Sunagawa S, Acinas SG, Bork P, Bowler C, Eveillard D, Gorsky G, et al. Tara Oceans: towards global ocean ecosystems biology. Nat Rev Microbiol. 2020;18(8):428–45.
pubmed: 32398798
doi: 10.1038/s41579-020-0364-5
Morais LH, Schreiber HL IV, Mazmanian SK. The gut microbiota-brain axis in behaviour and brain disorders. Nat Rev Microbiol. 2021;19(4):241–55.
pubmed: 33093662
doi: 10.1038/s41579-020-00460-0
Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol. 2009;9(5):313–23.
pubmed: 19343057
pmcid: 4095778
doi: 10.1038/nri2515
Amabebe E, Robert FO, Agbalalah T, Orubu ES. Microbial dysbiosis-induced obesity: role of gut microbiota in homoeostasis of energy metabolism. Br J Nutr. 2020;123(10):1127–37.
pubmed: 32008579
doi: 10.1017/S0007114520000380
Roux S, Matthijnssens J, Dutilh BE. Metagenomics in virology. Encyc Virol. 2021;1:133–40.
Adriaenssens EM, Van Zyl L, De Maayer P, Rubagotti E, Rybicki E, Tuffin M, et al. Metagenomic analysis of the viral community in Namib Desert hypoliths. Env Microbiol. 2015;17(2):480–95.
doi: 10.1111/1462-2920.12528
Schulz F, Alteio L, Goudeau D, Ryan EM, Yu FB, Malmstrom RR, et al. Hidden diversity of soil giant viruses. Nat Commun. 2018;9(1):1–9.
doi: 10.1038/s41467-018-07335-2
Santos-Medellin C, Zinke LA, Ter Horst AM, Gelardi DL, Parikh SJ, Emerson JB. Viromes outperform total metagenomes in revealing the spatiotemporal patterns of agricultural soil viral communities. ISME J. 2021;15(7):1956–70.
pubmed: 33612831
pmcid: 8245658
doi: 10.1038/s41396-021-00897-y
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537(7622):689–93.
pubmed: 27654921
doi: 10.1038/nature19366
Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA, Brussaard CP, et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun. 2017;8(1):1–12.
doi: 10.1038/ncomms15955
Hwang J, Park SY, Park M, Lee S, Lee TK. Seasonal dynamics and metagenomic characterization of marine viruses in Goseong Bay, Korea. PLoS ONE. 2017;12(1):e0169841.
pubmed: 28122030
pmcid: 5266330
doi: 10.1371/journal.pone.0169841
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell. 2019;177(5):1109–23.
pubmed: 31031001
pmcid: 6525058
doi: 10.1016/j.cell.2019.03.040
Wolf YI, Silas S, Wang Y, Wu S, Bocek M, Kazlauskas D, et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat Microbiol. 2020;5(10):1262–70.
pubmed: 32690954
pmcid: 7508674
doi: 10.1038/s41564-020-0755-4
Zayed AA, Wainaina JM, Dominguez-Huerta G, Pelletier E, Guo J, Mohssen M, et al. Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome. Science. 2022;376(6589):156–62.
pubmed: 35389782
pmcid: 10990476
doi: 10.1126/science.abm5847
Shkoporov AN, Clooney AG, Sutton TD, Ryan FJ, Daly KM, Nolan JA, et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe. 2019;26(4):527–41.
pubmed: 31600503
doi: 10.1016/j.chom.2019.09.009
Gregory AC, Zablocki O, Zayed AA, Howell A, Bolduc B, Sullivan MB. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe. 2020;28(5):724–40.
pubmed: 32841606
pmcid: 7443397
doi: 10.1016/j.chom.2020.08.003
Gulyaeva A, Garmaeva S, Ruigrok RA, Wang D, Riksen NP, Netea MG, et al. Discovery, diversity, and functional associations of crAss-like phages in human gut metagenomes from four Dutch cohorts. Cell Rep. 2022;38(2):110204.
pubmed: 35021085
doi: 10.1016/j.celrep.2021.110204
Li R, Wang Y, Hu H, Tan Y, Ma Y. Metagenomic analysis reveals unexplored diversity of archaeal virome in the human gut. Nat Commun. 2022;13(1):7978.
pubmed: 36581612
pmcid: 9800368
doi: 10.1038/s41467-022-35735-y
Sutton TD, Hill C. Gut bacteriophage: current understanding and challenges. Front Endocrinol. 2019;10:490764.
doi: 10.3389/fendo.2019.00784
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3(7):754–66.
pubmed: 29867096
doi: 10.1038/s41564-018-0166-y
Koonin EV, Krupovic M, Dolja VV. The global virome: How much diversity and how many independent origins? Wiley Online Library; 2023.
Nayfach S, Páez-Espino D, Call L, Low SJ, Sberro H, Ivanova NN, et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat Microbiol. 2021;6(7):960–70.
pubmed: 34168315
pmcid: 8241571
doi: 10.1038/s41564-021-00928-6
Mirzaei MK, Xue J, Costa R, Ru J, Schulz S, Taranu ZE, et al. Challenges of studying the human virome-relevant emerging technologies. Trends Microbiol. 2021;29(2):171–81.
doi: 10.1016/j.tim.2020.05.021
Kleiner M, Hooper LV, Duerkop BA. Evaluation of methods to purify virus-like particles for metagenomic sequencing of intestinal viromes. BMC Genomics. 2015;16:1–15.
doi: 10.1186/s12864-014-1207-4
Sanjuán R, Domingo-Calap P. Mechanisms of viral mutation. Cell Mol Life Sci. 2016;73:4433–48.
pubmed: 27392606
pmcid: 5075021
doi: 10.1007/s00018-016-2299-6
Kupczok A, Bailey ZM, Refardt D, Wendling CC. Co-transfer of functionally interdependent genes contributes to genome mosaicism in lambdoid phages. Microb Genomics. 2022;8(11):000915.
doi: 10.1099/mgen.0.000915
Schatz MC, Delcher AL, Salzberg SL. Assembly of large genomes using second-generation sequencing. Genome Res. 2010;20(9):1165–73.
pubmed: 20508146
pmcid: 2928494
doi: 10.1101/gr.101360.109
Steinegger M, Mirdita M, Söding J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat Methods. 2019;16(7):603–6.
pubmed: 31235882
doi: 10.1038/s41592-019-0437-4
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6.
pubmed: 25609793
doi: 10.1093/bioinformatics/btv033
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27(5):824–34.
pubmed: 28298430
pmcid: 5411777
doi: 10.1101/gr.213959.116
Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. GigaScience. 2019;8(9):giz100.
Sutton TD, Clooney AG, Ryan FJ, Ross RP, Hill C. Choice of assembly software has a critical impact on virome characterisation. Microbiome. 2019;7(1):1–15.
doi: 10.1186/s40168-019-0626-5
Antipov D, Raiko M, Lapidus A, Pevzner PA. Metaviral SPAdes: assembly of viruses from metagenomic data. Bioinformatics. 2020;36(14):4126–9.
pubmed: 32413137
doi: 10.1093/bioinformatics/btaa490
Meleshko D, Hajirasouliha I, Korobeynikov A. coronaSPAdes: from biosynthetic gene clusters to RNA viral assemblies. Bioinformatics. 2022;38(1):1–8.
doi: 10.1093/bioinformatics/btab597
Mallawaarachchi V, Roach MJ, Decewicz P, Papudeshi B, Giles SK, Grigson SR, et al. Phables: from fragmented assemblies to high-quality bacteriophage genomes. Bioinformatics. 2023;39(10):btad586.
Fritz A, Bremges A, Deng ZL, Lesker TR, Götting J, Ganzenmueller T, et al. Haploflow: Strain-resolved de novo assembly of viral genomes. Genome Biol. 2021;22(1):1–19.
doi: 10.1186/s13059-021-02426-8
Fitzpatrick AH, Rupnik A, O’Shea H, Cotter P. High throughput sequencing for the detection and characterization of RNA viruses. Front Microbiol. 2021;12:621719.
pubmed: 33692767
pmcid: 7938315
doi: 10.3389/fmicb.2021.621719
Baaijens JA, El Aabidine AZ, Rivals E, Schönhuth A. De novo assembly of viral quasispecies using overlap graphs. Genome Res. 2017;27(5):835–48.
pubmed: 28396522
pmcid: 5411778
doi: 10.1101/gr.215038.116
Li W, Malhotra R, Wu S, Jha M, Rodrigo A, Poss M, et al. ViPRA-Haplo: de novo reconstruction of viral populations using paired end sequencing data. IEEE/ACM Trans Comput Biol Bioinforma. 2024;21:492–500.
Hunt M, Gall A, Ong SH, Brener J, Ferns B, Goulder P, et al. IVA: accurate de novo assembly of RNA virus genomes. Bioinformatics. 2015;31(14):2374–6.
pubmed: 25725497
pmcid: 4495290
doi: 10.1093/bioinformatics/btv120
Yang X, Charlebois P, Gnerre S, Coole MG, Lennon NJ, Levin JZ, et al. De novo assembly of highly diverse viral populations. BMC Genomics. 2012;13(1):1–13.
doi: 10.1186/1471-2164-13-475
Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 2015;31(12):i35–43.
pubmed: 26072503
pmcid: 4765874
doi: 10.1093/bioinformatics/btv231
Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 2011;12(5):1–14.
doi: 10.1186/gb-2011-12-5-r44
Vollmers J, Wiegand S, Kaster AK. Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters! PLoS One. 2017;12(1):e0169662.
Steinegger M, Söding J. Clustering huge protein sequence sets in linear time. Nat Commun. 2018;9(1):1–8.
doi: 10.1038/s41467-018-04964-5
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32(7):1088–90.
pubmed: 26614127
doi: 10.1093/bioinformatics/btv697
Craigie R, Bushman FD. HIV DNA integration. CSH Perspect Med. 2012;2(7):a006890.
Maldarelli F, et al. The role of HIV integration in viral persistence: no more whistling past the proviral graveyard. J Clin Invest. 2016;126(2):438–47.
pubmed: 26829624
pmcid: 4731194
doi: 10.1172/JCI80564
Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35(11):1026–8.
pubmed: 29035372
doi: 10.1038/nbt.3988
Callanan J, Stockdale SR, Shkoporov A, Draper LA, Ross RP, Hill C. Expansion of known ssRNA phage genomes: from tens to over a thousand. Sci Adv. 2020;6(6):eaay5981.
Wolf YI, Kazlauskas D, Iranzo J, Lucía-Sanz A, Kuhn JH, Krupovic M, et al. Origins and evolution of the global RNA virome. MBio. 2018;9(6):e02329-18.
pubmed: 30482837
pmcid: 6282212
doi: 10.1128/mBio.02329-18
Tars K. SsRNA phages: life cycle, structure and applications. In: Biocommunication of Phages. Springer; 2020. pp. 261–292.
Chamakura KR, Tran JS, O’Leary C, Lisciandro HG, Antillon SF, Garza KD, et al. Rapid de novo evolution of lysis genes in single-stranded RNA phages. Nat Commun. 2020;11(1):1–11.
doi: 10.1038/s41467-020-19860-0
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(D1):D590–6.
pubmed: 23193283
doi: 10.1093/nar/gks1219
Edgar RC. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics. 2018;34:2371–5.
pubmed: 29506021
doi: 10.1093/bioinformatics/bty113
Deng ZL, Dhingra A, Fritz A, Götting J, Münch PC, Steinbrück L, et al. Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses. Brief Bioinforma. 2021;22(3):bbaa123.
Bouras G, Judd LM, Edwards RA, Vreugde S, Stinear TP, Wick RR. How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies. Microb Genomics. 2024;10(6):001254.
doi: 10.1099/mgen.0.001254
Hall MB, Wick RR, Judd LM, Nguyen AN, Steinig EJ, Xie O, et al. Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data. bioRxiv. 2024:2024–03. https://doi.org/10.1101/2024.03.15.585313 .
Lauber C, Seitz S. Opportunities and challenges of data-driven virus discovery. Biomolecules. 2022;12(8):1073.
pubmed: 36008967
pmcid: 9406072
doi: 10.3390/biom12081073
Koonin EV, Yutin N. The crAss-like phage group: how metagenomics reshaped the human virome. Trends Microbiol. 2020;28(5):349–59.
pubmed: 32298613
doi: 10.1016/j.tim.2020.01.010
Benler S, Yutin N, Antipov D, Rayko M, Shmakov S, Gussow AB, et al. Thousands of previously unknown phages discovered in whole-community human gut metagenomes. Microbiome. 2021;9:1–17.
doi: 10.1186/s40168-021-01017-w
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–45.
pubmed: 26553804
doi: 10.1093/nar/gkv1189
Sayers EW, Cavanaugh M, Clark K, Ostell J, Pruitt KD, Karsch-Mizrachi I. GenBank. Nucleic Acids Res. 2019;47(D1):D94–9.
pubmed: 30365038
doi: 10.1093/nar/gky989
Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Lawrence Berkeley National Lab. (LBNL), Berkeley; 2014.
Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat Methods. 2017;14(11):1063–71.
pubmed: 28967888
pmcid: 5903868
doi: 10.1038/nmeth.4458
Meyer F, Fritz A, Deng ZL, Koslicki D, Lesker TR, Gurevich A, et al. Critical assessment of metagenome interpretation: the second round of challenges. Nat Methods. 2022;19(4):429–40.
pubmed: 35396482
pmcid: 9007738
doi: 10.1038/s41592-022-01431-4
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–2.
doi: 10.14806/ej.17.1.200
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11(1):1–11.
doi: 10.1186/1471-2105-11-119
Burge SW, Daub J, Eberhardt R, Tate J, Barquist L, Nawrocki EP, et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 2013;41(D1):D226–32.