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Cited by 1Genomics and Phylogenetic StudiesBioinformatics and Genomic NetworksMicrobial Community Ecology and Physiology

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CORGIAS: identifying correlated gene pairs by considering evolutionary history in a large-scale prokaryotic genome dataset

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Nishimura, Yuki, Omae, Kimiho, Tominaga, Kento, Iwasaki, Wataru·2025·NAR Genomics and Bioinformatics
DOI 10.1093/nargab/lqaf182PMC PMC12699329

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Abstract

The recent expansion of prokaryotic genomes reveals many ortholog groups (OGs) whose function cannot be inferred from conventional, sequence similarity-based annotation methods, especially in metagenome-assembled genomes. Phylogenetic profiling is one of the promising methods to annotate these OGs, by identifying functional relationships of OGs using co- or anti-occurrence of OG distributions, not sequence similarity. Here, we proposed two new phylogenetic methods for large-scale data, Ancestral State Adjustment (ASA) and Simultaneous EVolution test (SEV), which consider the ancestral state of OG presence/absence. In evaluations using three distinct prokaryotic datasets, ASA and SEV showed better or comparable performance to both established and recently proposed methods for large-scale data. We compared the functionally related OGs detected by each method and found that SEV and its predecessor can identify slowly evolving OGs, such as housekeeping genes. In contrast, ASA and its predecessors can detect functionally related OGs that tend to be gained or lost in a fixed order, indicating a strong evolutionary constraint that provides clues for functional prediction. Using matrix multiplication, we also showed that SEV is scalable in the latest genome databases.

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Research works printed but not linked: 55

  1. Hug, 2016, A new view of the tree of life, Nat Microbiol, 1, 16048, 10.1038/nmicrobiol.2016.48 · DOI · Paper page
  2. Nayfach, 2021, A genomic catalog of Earth’s microbiomes, Nat Biotechnol, 39, 499, 10.1038/s41587-020-0718-6 · DOI
  3. Almeida, 2021, A unified catalog of 204,938 reference genomes from the human gut microbiome, Nat Biotechnol, 39, 105, 10.1038/s41587-020-0603-3 · DOI · Paper page
  4. Baltoumas, 2024, NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes, Nucleic Acids Res, 52, D502, 10.1093/nar/gkad800 · DOI
  5. Río, 2024, Functional and evolutionary significance of unknown genes from uncultivated taxa, Nature, 626, 377, 10.1038/s41586-023-06955-z · DOI
  6. Castelle, 2018, Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations, Nat Rev Micro, 16, 629, 10.1038/s41579-018-0076-2 · DOI · Paper page
  7. Pellegrini, 1999, Assigning protein functions by comparative genome analysis: protein phylogenetic profiles, Proc Natl Acad Sci USA, 96, 4285, 10.1073/pnas.96.8.4285 · DOI
  8. Tabach, 2013, Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence, Nature, 493, 694, 10.1038/nature11779 · DOI
  9. Dey, 2015, Systematic discovery of human gene function and principles of modular organization through phylogenetic profiling, Cell Rep, 10, 993, 10.1016/j.celrep.2015.01.025 · DOI
  10. Moi, 2020, Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes, PLoS Comput Biol, 16, e1007553, 10.1371/journal.pcbi.1007553 · DOI
  11. Dembech, 2023, Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions, Proc Natl Acad Sci USA, 120, e2218329120, 10.1073/pnas.2218329120 · DOI
  12. Li, 2023, PPNet: identifying functional association networks by phylogenetic profiling of prokaryotic genomes, Microbiol Spectr, 11, e03871, 10.1128/spectrum.03871-22 · DOI
  13. Fukunaga, 2024, Phylogenetic profiling analysis of the phycobilisome revealed a novel state-transition regulator gene in synechocystis sp. PCC 6803, Plant Cell Physiol, 65, 1450, 10.1093/pcp/pcae083 · DOI
  14. Whelan, 2020, Coinfinder: detecting significant associations and dissociations in pangenomes, Microb Genom, 6, e000338, 10.1099/mgen.0.000338 · DOI · Paper page
  15. Fukunaga, 2022, Inverse Potts model improves accuracy of phylogenetic profiling, Bioinformatics, 38, 1794, 10.1093/bioinformatics/btac034 · DOI · Paper page
  16. Morett, 2003, Systematic discovery of analogous enzymes in thiamin biosynthesis, Nat Biotechnol, 21, 790, 10.1038/nbt834 · DOI
  17. Kumagai, 2018, Solar-panel and parasol strategies shape the proteorhodopsin distribution pattern in marine Flavobacteriia, ISME J, 12, 1329, 10.1038/s41396-018-0058-4 · DOI
  18. Gavriilidou, Goldfinder: unraveling networks of gene co-occurrence and avoidance in bacterial pangenomes, 10.1101/2024.04.29.591652 · DOI · Paper page
  19. Tominaga, 2024, Frequent nonhomologous replacement of replicative helicase loaders by viruses in Vibrionaceae, Proc Natl Acad Sci USA, 121, e2317954121, 10.1073/pnas.2317954121 · DOI
  20. Pagel, 1994, Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters, Proc R Soc Lond Ser B: Biol. Sci., 255, 37, 10.1098/rspb.1994.0006 · DOI · Paper page
  21. Cohen, 2013, CoPAP: coevolution of presence–absence patterns, Nucleic Acids Res, 41, W232, 10.1093/nar/gkt471 · DOI
  22. Li, 2014, Expansion of biological pathways based on evolutionary inference, Cell, 158, 213, 10.1016/j.cell.2014.05.034 · DOI · Paper page
  23. Liu, 2023, The community coevolution model with application to the study of evolutionary relationships between genes based on phylogenetic profiles, Systematic Biol, 72, 559, 10.1093/sysbio/syac052 · DOI
  24. Parks, 2018, A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life, Nat Biotechnol, 36, 996, 10.1038/nbt.4229 · DOI · Paper page
  25. Rinke, 2021, A standardized archaeal taxonomy for the genome taxonomy database, Nat Microbiol, 6, 946, 10.1038/s41564-021-00918-8 · DOI
  26. Mendler, 2019, AnnoTree: visualization and exploration of a functionally annotated microbial tree of life, Nucleic Acids Res, 47, 4442, 10.1093/nar/gkz246 · DOI
  27. Cokus, 2007, An improved method for identifying functionally linked proteins using phylogenetic profiles, BMC Bioinformatics, 8, S7, 10.1186/1471-2105-8-S4-S7 · DOI
  28. Tremblay, 2021, PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling, Bioinformatics, 37, 17, 10.1093/bioinformatics/btaa1105 · DOI
  29. von Mering, 2003, STRING: a database of predicted functional associations between proteins, Nucleic Acids Res, 31, 258, 10.1093/nar/gkg034 · DOI
  30. Ruano-Rubio, 2009, Comparison of eukaryotic phylogenetic profiling approaches using species tree aware methods, BMC Bioinformatics, 10, 383, 10.1186/1471-2105-10-383 · DOI
  31. Lakshman, 2025, EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals, Nat Commun, 16, 3878, 10.1038/s41467-025-59175-6 · DOI
  32. Ishikawa, 2019, A fast likelihood method to reconstruct and visualize ancestral scenarios, Mol Biol Evol, 36, 2069, 10.1093/molbev/msz131 · DOI
  33. Collins, 2018, A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination, PLoS Comput Biol, 14, e1005958, 10.1371/journal.pcbi.1005958 · DOI
  34. Parks, 2022, GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy, Nucleic Acids Res, 50, gkab776, 10.1093/nar/gkab776 · DOI · Paper page
  35. Chklovski, 2023, CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning, Nat Methods, 20, 1203, 10.1038/s41592-023-01940-w · DOI · Paper page
  36. Galperin, 2021, COG database update: focus on microbial diversity, model organisms, and widespread pathogens, Nucleic Acids Res, 49, D274, 10.1093/nar/gkaa1018 · DOI
  37. Szklarczyk, 2023, The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest, Nucleic Acids Res, 51, D638, 10.1093/nar/gkac1000 · DOI · Paper page
  38. Revell, 2012, phytools: an R package for phylogenetic comparative biology (and other things), Methods Ecol Evol, 3, 217, 10.1111/j.2041-210X.2011.00169.x · DOI
  39. Ramulu, 2014, Ribosomal proteins: toward a next generation standard for prokaryotic systematics?, Mol Phylogenet Evol, 75, 103, 10.1016/j.ympev.2014.02.013 · DOI
  40. Loenen, 2014, Type I restriction enzymes and their relatives, Nucleic Acids Res, 42, 20, 10.1093/nar/gkt847 · DOI
  41. Sberro, 2013, Discovery of functional toxin/antitoxin systems in bacteria by shotgun cloning, Mol Cell, 50, 136, 10.1016/j.molcel.2013.02.002 · DOI
  42. Konno, 2023, Machine learning enables prediction of metabolic system evolution in bacteria, Sci Adv, 9, eadc9130, 10.1126/sciadv.adc9130 · DOI
  43. Fukunaga, 2021, Mirage: estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families, Bioinform Adv, 1, vbab014, 10.1093/bioadv/vbab014 · DOI
  44. Fukunaga, 2022, Mirage 2.0: fast and memory-efficient reconstruction of gene-content evolution considering heterogeneous evolutionary patterns among gene families, Bioinformatics, 38, 4039, 10.1093/bioinformatics/btac433 · DOI
  45. Yamanouchi, CoLaML: inferring latent evolutionary modes from heterogeneous gene content, 10.1101/2024.12.02.626417 · DOI
  46. Dagan, 2007, Ancestral genome sizes specify the minimum rate of lateral gene transfer during prokaryote evolution, Proc Natl Acad Sci USA, 104, 870, 10.1073/pnas.0606318104 · DOI
  47. Williams, 2024, Phylogenetic reconciliation: making the most of genomes to understand microbial ecology and evolution, ISME J, 18, wrae129, 10.1093/ismejo/wrae129 · DOI
  48. Mishra, 2025, Horizontal gene transfer inference: gene presence–absence outperforms gene trees, Mol Biol Evol, 42, msaf166, 10.1093/molbev/msaf166 · DOI
  49. Koonin, 2017, Evolutionary genomics of defense systems in archaea and bacteria, Annu Rev Microbiol, 71, 233, 10.1146/annurev-micro-090816-093830 · DOI
  50. Yang, 2023, Evolink: a phylogenetic approach for rapid identification of genotype–phenotype associations in large-scale microbial multispecies data, Bioinformatics, 39, btad215, 10.1093/bioinformatics/btad215 · DOI
  51. Kim, 2011, Genetic co-occurrence network across sequenced microbes, PLoS Comput Biol, 7, e1002340, 10.1371/journal.pcbi.1002340 · DOI
  52. Beavan, 2024, Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome, Proc Natl Acad Sci USA, 121, e2304934120, 10.1073/pnas.2304934120 · DOI
  53. Shmakov, 2019, Systematic prediction of functionally linked genes in bacterial and archaeal genomes, Nat Protoc, 14, 3013, 10.1038/s41596-019-0211-1 · DOI
  54. Botas, 2022, GeCoViz: genomic context visualisation of prokaryotic genes from a functional and evolutionary perspective, Nucleic Acids Res, 50, W352, 10.1093/nar/gkac367 · DOI
  55. Chang, 2024, Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles, Nucleic Acids Res, 52, e17, 10.1093/nar/gkad1187 · DOI

Metadata

Authors
Nishimura, Y., Omae, K., Tominaga, K., & Iwasaki, W.
Affiliations
  • The University of Tokyo (JP)
  • RIKEN Pioneering Research Institute (JP)
Corresponding author
Nishimura, Yuki
ORCID
Venue
NAR Genomics and Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2025-10-07
Volume
7
Issue
4
Pages
lqaf182-lqaf182
ISSN
2631-9268 (ISSN-L)
DOI
10.1093/nargab/lqaf182
PMC
PMC12699329
OpenAlex
W4417303632
Cited by
1
References
4
Citations by year
2026: 1
FWCI
0.30
Type
journal article
Language
en
Open access
Licence
Locations
  • NAR Genomics and Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Japan Society for the Promotion of Science (22H04925)
  • Japan Science and Technology Agency (JPMJCR19S2, JPMJGX23B2)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords
orthogroups, phylogenetic profiling, gene function prediction, metagenome-assembled genomes, prokaryotic genomes, gene gain and loss, housekeeping genes, functional annotation
MeSH terms
Phylogeny, Genome, Bacterial, Evolution, Molecular, Databases, Genetic

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