Sequencing effort dictates gene discovery in marine microbial metagenomes
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Summary: Massive metagenomic sequencing combined with gene prediction methods were previously used to compile the gene catalogue of the ocean and host‐associated microbes. Global expeditions conducted over the past 15 years have sampled the ocean to build a catalogue of genes from pelagic microbes. Here we undertook a large sequencing effort of a perturbed Red Sea plankton community to uncover that the rate of gene discovery increases continuously with sequencing effort, with no indication that the retrieved 2.83 million non‐redundant (complete) genes predicted from the experiment represented a nearly complete inventory of the genes present in the sampled community (i.e., no evidence of saturation). The underlying reason is the Pareto‐like distribution of the abundance of genes in the plankton community, resulting in a very long tail of millions of genes present at remarkably low abundances, which can only be retrieved through massive sequencing. Microbial metagenomic projects retrieve a variable number of unique genes per Tera base‐pair (Tbp), with a median value of 14.7 million unique genes per Tbp sequenced across projects. The increase in the rate of gene discovery in microbial metagenomes with sequencing effort implies that there is ample room for new gene discovery in further ocean and holobiont sequencing studies.
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Comparative genomic analyses usually test for correlations between traits.
“Comparative genomic analyses usually test for correlations between traits.”
From A phylogenetic approach to comparative genomics (2025) · cites this paper as [78]
The Results section ('Gene discovery across marine and host-associated metagenomes'; page unavailable) compares metagenomic gene catalogues to examine sequencing effort and gene discovery. Searches for comparative genomics and trait correlations found no passage in our copy addressing what comparative genomic analyses usually test. (LLM, conf=0.76)
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Research works printed but not linked: 42
- Acinas, S.G., Sánchez, P., Salazar, G., Cornejo‐Castillo, F.M., Sebastián, M., Logares, R., Sunagawa, S., Hingamp, P., Ogata, H., Lima‐Mendez, G., Roux, S., González, J.M., Arrieta, J.M., Alam, I.S., Kamau, A., Bowler, C., Raes, J., Pesant, S., Bork, P., Agustí, S., Gojobori, T., Bajic, V., Vaqué, D., Sullivan, M.B., Pedrós‐Alió, C., Massana, R., Duarte, C.M., and Gasol, J.M. 2019. Metabolic architecture of the deep ocean microbiome, bioRxiv 635680
- Alam, I., Antunes, A., Kamau, A.A., Kalkatawi, M., Stingl, U., and Bajic, V.B. (2013) INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with examples from the Red Sea extremophiles. PLoS One 8: e82210. 24324765 PMC3855842
- Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths‐Jones, S., et al (2004) The Pfam protein families database. Nucleic Acids Res 28: 263– 266. PMC308855 14681378
- Biller, S.J., Berube, P.M., Dooley, K., Williams, M., Satinsky, B.M., Hackl, T., et al (2018) Marine microbial metagenomes sampled across space and time. Sci Data 5: 180176. 30179232 PMC6122167
- Bolger, A.M., Lohse, M., and Usadel, B. (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114– 2120. 24695404 PMC4103590
- Finlay, B.J. (2002) Global dispersal of free‐living microbial eukaryote species. Science 296: 1061– 1063. 12004115
- Forouzan, E., Shariati, P., Mousavi Maleki, M.S., Karkhane, A.A., and Yakhchali, B. (2018) Practical evaluation of 11 de novo assemblers in metagenome assembly. J Microbiol Methods 151: 99– 105. 29953874
- Fuhrman, J.A., Steele, J.A., Hewson, I., Schwalbach, M.S., Brown, M.V., Green, J.L., and Brown, J.H. (2008) A latitudinal diversity gradient in planktonic marine bacteria. Proc Natl Acad Sci U S A 105: 7774– 7778. 18509059 PMC2409396
- Gianoulis, T.A., Raes, J., Patel, P.V., Bjornson, R., Korbel, J.O., Letunic, I., et al (2009) Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc Natl Acad Sci U S A 106: 1374– 1379. 19164758 PMC2629784
- Haft, D.H., Selengut, J.D., and White, O. (2003) The TIGRFAMs database of protein families. Nucleic Acids Res 31: 371– 373. 12520025 PMC165575
- Hauser, M., Steinegger, M., and Söding, J. (2016) MMseqs software suite for fast and deep clustering and searching of large protein sequence sets. Bioinformatics 32: 1323– 1330. 26743509
- Hugerth, L.W., Larsson, J., Alneberg, J., Lindh, M.V., Legrand, C., Pinhassi, J., and Andersson, A.F. (2015) Metagenome‐assembled genomes uncover a global brackish microbiome. Genome Biol 16: 279. 26667648 PMC4699468
- Hyatt, D., Chen, G.L., LoCascio, P.F., Land, M.L., Larimer, F.W., and Hauser, L.J. (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 119. 20211023 PMC2848648
- Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. (2013) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42: D199– D205. 24214961 PMC3965122
- Karsenti, E., Acinas, S.G., Bork, P., Bowler, C., De Vargas, C., Raes, J., et al (2011) A holistic approach to marine eco‐systems biology. PLoS Biol 9: e1001177. 22028628 PMC3196472
- Li, D., Liu, C.‐M., Luo, R., Sadakane, K., and Lam, T.‐W. (2015) MEGAHIT: an ultra‐fast single‐node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31: 1674– 1676. 25609793
- Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., et al (2014) An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 32: 834– 841. 24997786
- Locey, K.J., and Lennon, J.T. (2016) Scaling laws predict global microbial diversity. Proc Natl Acad Sci U S A 113: 5970– 5975. 27140646 PMC4889364
- Lomsadze, A., Gemayel, K., Tang, S., and Borodovsky, M. (2018) Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes. Genome Res 28: 1079– 1089. 29773659 PMC6028130
- Mathé, C., Sagot, M.‐F., Schiex, T., and Rouzé, P. (2002) Current methods of gene prediction, their strengths and weaknesses. Nucleic Acids Res 30: 4103– 4117. 12364589 PMC140543
- Mayol, E., Arrieta, J.M., Jiménez, M.A., Martínez‐Asensio, A., Garcias‐Bonet, N., Dachs, J., et al (2017) Long‐range transport of airborne microbes over the global tropical and subtropical ocean. Nat Commun 8: 201. 28779070 PMC5544686
- McMurdie, P.J., and Holmes, S. (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8: e61217. 23630581 PMC3632530
- Mende, D.R., Bryant, J.A., Aylward, F.O., Eppley, J.M., Nielsen, T., Karl, D.M., and DeLong, E.F. (2017) Environmental drivers of a microbial genomic transition zone in the ocean's interior. Nat Microbiol 2: 1367– 1373. 28808230
- Nurk, S., Meleshko, D., Korobeynikov, A., and Pevzner, P.A. (2017) metaSPAdes: a new versatile metagenomic assembler. Genome Res 27: 824– 834. 28298430 PMC5411777
- Pan, H., Guo, R., Zhu, J., Wang, Q., Ju, Y., Xie, Y., et al (2018) A gene catalogue of the Sprague‐Dawley rat gut metagenome. GigaScience 7: giy055. PMC5967468 29762673
- Pedros‐Alio, C. (2012) The rare bacterial biosphere. Ann Rev Mar Sci 4: 449– 466. 22457983
- Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K.S., Manichanh, C., et al (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464: 59– 65. 20203603 PMC3779803
- Rusch, D.B., Halpern, A.L., Sutton, G., Heidelberg, K.B., Williamson, S., Yooseph, S., et al (2007) The Sorcerer II global ocean Sampling Expedition: Northwest Atlantic through eastern tropical Pacific. PLoS Biol 5: e77. 17355176 PMC1821060
- Salazar, G., Cornejo‐Castillo, F.M., Benítez‐Barrios, V., Fraile‐Nuez, E., Álvarez‐Salgado, X.A., Duarte, C.M., et al (2016) Global diversity and biogeography of deep‐sea pelagic prokaryotes. ISME J 10: 596– 608. 26251871 PMC4817678
- Sunagawa, S., Coelho, L.P., Chaffron, S., Kultima, J.R., Labadie, K., Salazar, G., et al (2015) Structure and function of the global ocean microbiome. Science 348: 1261359. 25999513
- Suzek, B.E., Wang, Y., Huang, H., McGarvey, P.B., Wu, C.H., and UniProt Consortium. (2014) UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31: 926– 932. 25398609 PMC4375400
- Thompson, L.R., Williams, G.J., Haroon, M.F., Shibl, A., Larsen, P., Shorenstein, J., et al (2017) Metagenomic covariation along densely sampled environmental gradients in the Red Sea. ISME J 11: 138– 151. 27420030 PMC5315489
- van der Walt, A.J., Van Goethem, M.W., Ramond, J.B., Makhalanyane, T.P., Reva, O., and Cowan, D.A. (2017) Assembling metagenomes, one community at a time. BMC Genomics 18: 521. 28693474 PMC5502489
- West, P.T., Probst, A.J., Grigoriev, I.V., Thomas, B.C., and Banfield, J.F. (2018) Genome‐reconstruction for eukaryotes from complex natural microbial communities. Genome Res 28: 569– 580. 29496730 PMC5880246
- Whitman, W.B., Coleman, D.C., and Wiebe, W.J. (1998) Prokaryotes: the unseen majority. Proc Natl Acad Sci U S A 95: 6578– 6583. 9618454 PMC33863
- Wickham, H. (2016) gplot: Elegant Graphics for Data Analysis. New York: Springer‐Verlag.
- Wyman, M., Davies, J.T., Crawford, D.W., and Purdie, D.A. (2000) Molecular and physiological responses of two classes of marine chromophytic phytoplankton (diatoms and prymnesiophytes) during the development of nutrient‐stimulated blooms. Appl Environ Microbiol 66: 2349– 2357. 10831410 PMC110529
- Xiao, L., Estellé, J., Kiilerich, P., Ramayo‐Caldas, Y., Xia, Z., Feng, Q., Liang, S., et al. (2016). A reference gene catalogue of the pig gut microbiome. Nature Microbiology, 1: 1– 66. 10.1038/nmicrobiol.2016.161. 27643971 · DOI
- Xiao, L., Feng, Q., Liang, S., Sonne, S.B., Xia, Z., Qiu, X., et al (2015). A catalog of the mouse gut metagenome. Nature Biotechnology 33: 1103– 1108. 10.1038/nbt.3353. 26414350 · DOI
- Xiao, L., Feng, Q., Liang, S., Sonne, S.B., Xia, Z., Qiu, X., et al (2015b) A reference gene catalogue of the pig gut microbiome. Nat Microbiol 1: 1– 6. 27643971
- Yooseph, S., Nealson, K.H., Rusch, D.B., McCrow, J.P., Dupont, C.L., Kim, M., et al (2010) Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature 468: 60– 66. 21048761
- Yooseph, S., Sutton, G., Rusch, D.B., Halpern, A.L., Williamson, S.J., Remington, K., et al (2007) The Sorcerer II Global Ocean Sampling Expedition: expanding the universe of protein families. PLoS Biol 5: e16. 17355171 PMC1821046
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A phylogenetic approach to comparative genomics
Dewar, A. E., Belcher, L. J., & West, S. A. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00803-0 · Cited by 41 · see citing works
- Authors
- Duarte, C. M., Ngugi, D. K., Alam, I., Pearman, J., Kamau, A., Eguiluz, V. M., Gojobori, T., Acinas, S. G., Gasol, J. M., Bajic, V., & Irigoien, X.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures (DE)
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), E07122 Palma de Mallorca Spain
- Consejo Superior de Investigaciones Científicas (ES)
- Edith Cowan University (AU)
- Leibniz Institute DSMZ ‐ German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstrasse 7B, D‐38124 Braunschweig Germany
- AZTI (ES)
- Corresponding author
- Duarte, Carlos M.
- Ngugi, David K.
- ORCID
- Venue
- Environmental Microbiology
- Publisher
- Wiley
- Published
- 2020-09-29
- Volume
- 22
- Issue
- 11
- Pages
- 4589-4603
- ISSN
- 1462-2912 (ISSN-L)
- 1462-2920
- PMC
- PMC7756799
- OpenAlex
- W3046605231
- Cited by
- 22
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 5 · 2022: 5 · 2021: 2
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Environmental Microbiology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Australasian Journal of Paramedicine (submittedVersion, cc-by-nc-nd)
- AZTI Institutional Repository (submittedVersion, cc-by-nc-sa)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- King Abdullah University of Science and Technology
- National Institutes of Health (BB123456, AA123456)
- Ministerio de Ciencia e Innovación (CSD2008‐00077)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- gene discovery, Pareto distribution, gene prediction, saturation, holobiont
- MeSH terms
- Plankton, Water Microbiology, Genome, Bacterial, Diatoms, Indian Ocean, Alphaproteobacteria, Gammaproteobacteria, Flavobacteriaceae, Metagenome, Metagenomics, Genetic Association Studies, Aquatic Organisms, High-Throughput Nucleotide Sequencing
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