Predicting functions of uncharacterized gene products from microbial communities

  • Armet, A. M. et al. Rethinking healthy eating in light of the gut microbiome. Cell Host Microbe 30, 764–785 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sharon, G. et al. Specialized metabolites from the microbiome in health and disease. Cell Metab 20, 719–730 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baldrian, P. et al. Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J. 6, 248–258 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Singleton, C. M. et al. Methanotrophy across a natural permafrost thaw environment. ISME J. 12, 2544–2558 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Joice, R., Yasuda, K., Shafquat, A., Morgan, X. C. & Huttenhower, C. Determining microbial products and identifying molecular targets in the human microbiome. Cell Metab. 20, 731–741 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. et al. Discovery of bioactive microbial gene products in inflammatory bowel disease. Nature 606, 754–760 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Peisl, B. Y. L., Schymanski, E. L. & Wilmes, P. Dark matter in host–microbiome metabolomics: tackling the unknowns—a review. Anal. Chim. Acta 1037, 13–27 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Vanni, C. et al. Unifying the known and unknown microbial coding sequence space. eLife 11, e67667 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lagier, J. C. et al. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat. Microbiol. 1, 16203 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schnoes, A. M., Ream, D. C., Thorman, A. W., Babbitt, P. C. & Friedberg, I. Biases in the experimental annotations of protein function and their effect on our understanding of protein function space. PLoS Comput. Biol. 9, e1003063 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rost, B., Liu, J., Nair, R., Wrzeszczynski, K. O. & Ofran, Y. Automatic prediction of protein function. Cell Mol. Life Sci. 60, 2637–2650 (2003).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, D., Redfern, O. & Orengo, C. Predicting protein function from sequence and structure. Nat. Rev. Mol. Cell Biol. 8, 995–1005 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Xin, F. & Radivojac, P. Computational methods for identification of functional residues in protein structures. Curr. Protein Pept. Sci. 12, 456–469 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221–227 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jiang, Y. et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biol. 17, 184 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, N. et al. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biol. 20, 244 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jensen, L. J. et al. Prediction of human protein function from post-translational modifications and localization features. J. Mol. Biol. 319, 1257–1265 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wass, M. N. & Sternberg, M. J. ConFunc—functional annotation in the twilight zone. Bioinformatics 24, 798–806 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Clark, W. T. & Radivojac, P. Analysis of protein function and its prediction from amino acid sequence. Proteins 79, 2086–2096 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • You, R. et al. GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank. Bioinformatics 34, 2465–2473 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Korbel, J. O., Jensen, L. J., von Mering, C. & Bork, P. Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nat. Biotechnol. 22, 911–917 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Enault, F., Suhre, K. & Claverie, J. M. Phydbac ‘Gene Function Predictor’: a gene annotation tool based on genomic context analysis. BMC Bioinformatics 6, 247 (2005).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D. & Yeates, T. O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl Acad. Sci. USA 96, 4285–4288 (1999).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Engelhardt, B. E., Jordan, M. I., Muratore, K. E. & Brenner, S. E. Protein molecular function prediction by Bayesian phylogenomics. PLoS Comput. Biol. 1, e45 (2005).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pazos, F. & Sternberg, M. J. Automated prediction of protein function and detection of functional sites from structure. Proc. Natl Acad. Sci. USA 101, 14754–14759 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deng, M., Zhang, K., Mehta, S., Chen, T. & Sun, F. Prediction of protein function using protein–protein interaction data. J. Comput. Biol. 10, 947–960 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nabieva, E., Jim, K., Agarwal, A., Chazelle, B. & Singh, M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, i302–i310 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wells, J. A. & McClendon, C. L. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450, 1001–1009 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Brown, M. P. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA 97, 262–267 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van Noort, V., Snel, B. & Huynen, M. A. Predicting gene function by conserved co-expression. Trends Genet. 19, 238–242 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Guan, Y. et al. Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol. 9, S3 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Piovesan, D. & Tosatto, S. C. E. INGA 2.0: improving protein function prediction for the dark proteome. Nucleic Acids Res. 47, W373–w378 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bashiardes, S., Zilberman-Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10, 19–25 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franzosa, E. A. et al. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Heintz-Buschart, A. et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol. 2, 16180 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Coolen, M. J. & Orsi, W. D. The transcriptional response of microbial communities in thawing Alaskan permafrost soils. Front. Microbiol. 6, 197 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vorobev, A. et al. Transcriptome reconstruction and functional analysis of eukaryotic marine plankton communities via high-throughput metagenomics and metatranscriptomics. Genome Res. 30, 647–659 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • van Dam, S., Vosa, U., van der Graaf, A., Franke, L. & de Magalhaes, J. P. Gene co-expression analysis for functional classification and gene–disease predictions. Brief. Bioinform. 19, 575–592 (2018).

    PubMed 

    Google Scholar
     

  • Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. & Maharjan, S. biobakery/fugassem: FUGAsseM v0.3.8. Zenodo https://doi.org/10.5281/zenodo.16477039 (2025).

  • Hvidsten, T. R., Komorowski, J., Sandvik, A. K. & Laegreid, A. Predicting gene function from gene expressions and ontologies. In Proceedings of the Pacific Symposium on Biocomputing (eds Altman, R. B., Dunker, A. K., Hunter, L., Lauderdale, K. & Klein, T. E.) (World Scientific, 2001).

  • Zhou, X., Kao, M. C. & Wong, W. H. Transitive functional annotation by shortest-path analysis of gene expression data. Proc. Natl Acad. Sci. USA 99, 12783–12788 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Myers, C. L., Barrett, D. R., Hibbs, M. A., Huttenhower, C. & Troyanskaya, O. G. Finding function: evaluation methods for functional genomic data. BMC Genomics 7, 187 (2006).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mitchell, A. et al. The InterPro protein families database: the classification resource after 15 years. Nucleic Acids Res. 43, D213–D221 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • von Mering, C. et al. STRING: known and predicted protein–protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33, D433–D437 (2005).

    Article 

    Google Scholar
     

  • Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yellaboina, S., Tasneem, A., Zaykin, D. V., Raghavachari, B. & Jothi, R. DOMINE: a comprehensive collection of known and predicted domain-domain interactions. Nucleic Acids Res. 39, D730–D735 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 42, D459–D471 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yao, S. et al. NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information. Nucleic Acids Res. 49, W469–w475 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kulmanov, M. & Hoehndorf, R. DeepGOPlus: improved protein function prediction from sequence. Bioinformatics 37, 1187 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rodríguez Del Río, Á. et al. Functional and evolutionary significance of unknown genes from uncultivated taxa. Nature 626, 377–384 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–d444 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Koppel, N., Maini Rekdal, V. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Das, N. K. et al. Microbial metabolite signaling is required for systemic iron homeostasis. Cell Metab. 31, 115–130.e116 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Seyoum, Y., Baye, K. & Humblot, C. Iron homeostasis in host and gut bacteria—a complex interrelationship. Gut Microbes 13, 1–19 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, Z. et al. The role of intestinal bacteria and gut–brain axis in hepatic encephalopathy. Front. Cell. Infect. Microbiol. 10, 595759 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Galdiero, S. et al. Microbe–host interactions: structure and role of gram-negative bacterial porins. Curr. Protein Pept. Sci. 13, 843–854 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hogbom, M. & Ihalin, R. Functional and structural characteristics of bacterial proteins that bind host cytokines. Virulence 8, 1592–1601 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jaehme, M. & Slotboom, D. J. Diversity of membrane transport proteins for vitamins in bacteria and archaea. Biochim. Biophys. Acta 1850, 565–576 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fujita, M. et al. A TonB-dependent receptor constitutes the outer membrane transport system for a lignin-derived aromatic compound. Commun. Biol. 2, 432 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Connors, J., Dawe, N. & van Limbergen, J. The role of succinate in the regulation of intestinal inflammation. Nutrients 11, 25 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boudreau, M. A., Fisher, J. F. & Mobashery, S. Messenger functions of the bacterial cell wall-derived muropeptides. Biochemistry 51, 2974–2990 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hosaka, H., Kawamura, M., Hirano, T., Hakamata, W. & Nishio, T. Utilization of sucrose and analog disaccharides by human intestinal bifidobacteria and lactobacilli: search of the bifidobacteria enzymes involved in the degradation of these disaccharides. Microbiol. Res. 240, 126558 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rawat, P. S., Li, Y., Zhang, W., Meng, X. & Liu, W. Hungatella hathewayi, an efficient glycosaminoglycan-degrading Firmicutes from human gut and its chondroitin ABC exolyase with high activity and broad substrate specificity. Appl. Environ. Microbiol. 88, e0154622 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Cullender, T. C. et al. Innate and adaptive immunity interact to quench microbiome flagellar motility in the gut. Cell Host Microbe 14, 571–581 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lopez-Siles, M., Duncan, S. H., Garcia-Gil, L. J. & Martinez-Medina, M. Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics. ISME J. 11, 841–852 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cornuault, J. K. et al. Phages infecting Faecalibacterium prausnitzii belong to novel viral genera that help to decipher intestinal viromes. Microbiome 6, 65 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bai, Z. et al. Comprehensive analysis of 84 Faecalibacterium prausnitzii strains uncovers their genetic diversity, functional characteristics, and potential risks. Front. Cell. Infect. Microbiol. 12, 919701 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Koropatkin, N. M. & Smith, T. J. SusG: a unique cell-membrane-associated α-amylase from a prominent human gut symbiont targets complex starch molecules. Structure 18, 200–215 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Martens, E. C. et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 9, e1001221 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, M. et al. Genetic determinants of in vivo fitness and diet responsiveness in multiple human gut Bacteroides. Science 350, aac5992 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Terrapon, N. et al. PULDB: the expanded database of polysaccharide utilization loci. Nucleic Acids Res. 46, D677–d683 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pavarina, G. C., Lemos, E. G. M., Lima, N. S. M. & Pizauro, J. M. Jr. Characterization of a new bifunctional endo-1,4-β-xylanase/esterase found in the rumen metagenome. Sci. Rep. 11, 10440 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Carneiro, L. et al. Selective xyloglucan oligosaccharide hydrolysis by a GH31 α-xylosidase from Escherichia coli. Carbohydr. Polym. 284, 119150 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lin, H. et al. Multiomics study reveals Enterococcus and Subdoligranulum are beneficial to necrotizing enterocolitis. Front. Microbiol. 12, 752102 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shi, T. T. et al. Comparative assessment of gut microbial composition and function in patients with Graves’ disease and Graves’ orbitopathy. J. Endocrinol. Invest. 44, 297–310 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Girardin, S. E. et al. Nod1 detects a unique muropeptide from gram-negative bacterial peptidoglycan. Science 300, 1584–1587 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hasegawa, M. et al. Differential release and distribution of Nod1 and Nod2 immunostimulatory molecules among bacterial species and environments. J. Biol. Chem. 281, 29054–29063 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Elshorbagy, A. et al. Amino acid changes during transition to a vegan diet supplemented with fish in healthy humans. Eur. J. Nutr. 56, 1953–1962 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dong, Z., Sinha, R. & Richie, J. P. Jr. Disease prevention and delayed aging by dietary sulfur amino acid restriction: translational implications. Ann. N. Y. Acad. Sci. 1418, 44–55 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Whisstock, J. C. & Lesk, A. M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36, 307–340 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sleator, R. D. & Walsh, P. An overview of in silico protein function prediction. Arch. Microbiol. 192, 151–155 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Overbeek, R., Fonstein, M., D’Souza, M., Pusch, G. D. & Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl Acad. Sci. USA 96, 2896–2901 (1999).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Teichmann, S. A. & Babu, M. M. Conservation of gene co-regulation in prokaryotes and eukaryotes. Trends Biotechnol. 20, 407–410 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Eisenberg, D., Marcotte, E. M., Xenarios, I. & Yeates, T. O. Protein function in the post-genomic era. Nature 405, 823–826 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sharan, R., Ulitsky, I. & Shamir, R. Network-based prediction of protein function. Mol. Syst. Biol. 3, 88 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, P. I. & Marcotte, E. M. It’s the machine that matters: predicting gene function and phenotype from protein networks. J. Proteomics 73, 2277–2289 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ryan, C. J. et al. High-resolution network biology: connecting sequence with function. Nat. Rev. Genet. 14, 865–879 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Costanzo, M. et al. Environmental robustness of the global yeast genetic interaction network. Science 372, eabf8424 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Serin, E. A., Nijveen, H., Hilhorst, H. W. & Ligterink, W. Learning from co-expression networks: possibilities and challenges. Front. Plant Sci. 7, 444 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Southard, J. N. Protein analysis using real-time PCR instrumentation: incorporation in an integrated, inquiry-based project. Biochem. Mol. Biol. Educ. 42, 142–151 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schirmer, M. et al. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat. Microbiol. 3, 337–346 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y., Thompson, K. N., Huttenhower, C. & Franzosa, E. A. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics 37, i34–i41 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parrow, N. L., Fleming, R. E. & Minnick, M. F. Sequestration and scavenging of iron in infection. Infect. Immun. 81, 3503–3514 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanchez-Jimenez, A., Marcos-Torres, F. J. & Llamas, M. A. Mechanisms of iron homeostasis in Pseudomonas aeruginosa and emerging therapeutics directed to disrupt this vital process. Microb. Biotechnol. 16, 1475–1491 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, C. S. et al. Seasonal and spatial environmental influence on Opisthorchis viverrini intermediate hosts, abundance, and distribution: insights on transmission dynamics and sustainable control. PLoS Negl. Trop. Dis. 10, e0005121 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Isobe, K. & Ohte, N. Ecological perspectives on microbes involved in N-cycling. Microbes Environ. 29, 4–16 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yi, M. et al. Temporal changes of microbial community structure and nitrogen cycling processes during the aerobic degradation of phenanthrene. Chemosphere 286, 131709 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Davila, A. M. et al. Intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences for the host. Pharmacol. Res. 68, 95–107 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hou, K. et al. Microbiota in health and diseases. Signal. Transduct. Target. Ther. 7, 135 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fitzgerald, C. B. et al. Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa. BMC Genomics 19, 931 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Silas, S. et al. Type III CRISPR–Cas systems can provide redundancy to counteract viral escape from type I systems. eLife 6, e27601 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cuiv, P. O. et al. Isolation of genetically tractable most-wanted bacteria by metaparental mating. Sci. Rep. 5, 13282 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deutscher, M. P. Degradation of RNA in bacteria: comparison of mRNA and stable RNA. Nucleic Acids Res. 34, 659–666 (2006).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reck, M. et al. Stool metatranscriptomics: a technical guideline for mRNA stabilisation and isolation. BMC Genomics 16, 494 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sczyrba, A. et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yue, Q. et al. Functional operons in secondary metabolic gene clusters in Glarea lozoyensis (Fungi, Ascomycota, Leotiomycetes). mBio 6, e00703 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Friedberg, I. Automated protein function prediction—the genomic challenge. Brief. Bioinform. 7, 225–242 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jeffery, C. J. Current successes and remaining challenges in protein function prediction. Front. Bioinform. 3, 1222182 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–d314 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, W., Jaroszewski, L. & Godzik, A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282–283 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, Y. et al. Metatranscriptomics for the human microbiome and microbial community functional profiling. Annu. Rev. Biomed. Data Sci. 4, 279–311 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Klingenberg, H. & Meinicke, P. How to normalize metatranscriptomic count data for differential expression analysis. PeerJ 5, e3859 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leave a Comment