Expanded microbial genome coverage and improved protein family annotation in the COG database

By Michael Y. Galperin, Kira S. Makarova, Yuri I. Wolf, Eugene V. Koonin. 2014

1,435 citations recorded by the source corpus.

Microbial genome sequencing projects produce numerous sequences of deduced proteins, only a small fraction of which have been or will ever be studied experimentally. This leaves sequence analysis as the only feasible way to annotate these proteins and assign to them tentative functions. The Clusters of Orthologous Groups of proteins (COGs) database (http://www.ncbi.nlm.nih.gov/COG/), first created in 1997, has been a popular tool for functional annotation. Its success was largely based on (i) its reliance on complete microbial genomes, which allowed reliable assignment of orthologs and paralogs for most genes; (ii) orthology-based approach, which used the function(s) of the characterized member(s) of the protein family (COG) to assign function(s) to the entire set of carefully identified orthologs and describe the range of potential functions when there were more than one; and (iii) careful manual curation of the annotation of the COGs, aimed at detailed prediction of the biological function(s) for each COG while avoiding annotation errors and overprediction. Here we present an update of the COGs, the first since 2003, and a comprehensive revision of the COG annotations and expansion of the genome coverage to include representative complete genomes from all bacterial and archaeal lineages down to the genus level. This re-analysis of the COGs shows that the original COG assignments had an error rate below 0.5% and allows an assessment of the progress in functional genomics in the past 12 years. During this time, functions of many previously uncharacterized COGs have been elucidated and tentative functional assignments of many COGs have been validated, either by targeted experiments or through the use of high-throughput methods. A particularly important development is the assignment of functions to several widespread, conserved proteins many of which turned out to participate in translation, in particular rRNA maturation and tRNA modification. The new version of the COGs is expected to become an important tool for microbial genomics.

Subfields: Molecular Biology

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Citations in the corpus

eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses (2018, methodology): “Given the large number of species in this version of eggNOG, we have however retained some size control heuristics, such as the rule that COGs should not be merged.”
MGnify: the microbiome analysis resource in 2020 (2019, methodology): “Once contigs have been selected, annotations for the predicted proteins can be coloured according to their functional annotations (InterPro, GO, Pfam, EggNOG, COG and KEGG).”
Large-scale determination of previously unsolved protein structures using evolutionary information (2015, background): “In the following sections, the predictions are grouped by known biological functions assigned by Clusters of Orthologous Groups ( Galperin et al., 2015 ).”
InterPro in 2017—beyond protein family and domain annotations (2016, background): “CDD content also includes domain models imported from a number of external source databases (Pfam, SMART, COG (37), PRK (38) and TIGRFAMs).”
Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database (2015, methodology): “As an alternative, orthologous group classifications such as Clusters of Orthologous Groups of proteins, COGs (17) that some Pseudomonas proteins cannot be classified into, we provide a separate analysis that assigns Pseudomonas proteins to more inclusive groups of orthologs and putative paralogs ca”
Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper (2016, background): “Functional descriptors are based on the most recent eggNOG build, and currently include curated Gene Ontology (GO) terms (Gene Ontology Consortium 2015), KEGG pathways (Kanehisa et al. 2014) and COG functional categories (Galperin et al. 2015).”
A comparative and functional genomics analysis of the genus Romboutsia provides insight into adaptation to an intestinal lifestyle (2019, methodology): “COGs (43) were 140 determined via best bidirectional blast (44), with an e-value of 0.”
COG database update: focus on microbial diversity, model organisms, and widespread pathogens (2020, methodology): “As described previously (3,4,58), the most straightforward application of the COGs is the quality control of genome sequences.”
RefSeq: an update on prokaryotic genome annotation and curation (2017, neutral): “CDD has aggregated a set of protein family definitions from Pfam and TIGRFAMs HMM libraries, PRK clusters, COGs (25), CDD-curated models which are organized into sub-family hierarchies, as well as various other sources, and generated position-specific scoring matrices (PSSMs), the RPS-BLAST analog o”
RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12 (2018, methodology): “We have reorganized their classification (See: ‘Integrated Views & Tools/GENSOR Unit Groups’) using the COG classification of genes (26) with 22 functional categories.”
SCycDB: A curated functional gene database for metagenomic profiling of sulphur cycling pathways (2020, methodology): “The orthology databases used for database merging and homologous gene identification in this study included arCOG (Makarova et al., 2015), COG (Galperin et al., 2015), eggNOG (Huerta-Cepas et al., 2019) and KEGG (Kanehisa et al., 2016).”
Archaeal Clusters of Orthologous Genes (arCOGs): An Update and Application for Analysis of Shared Features between Thermococcales, Methanococcales, and Methanobacteriales (2015, methodology): “This arCOG belongs to a different COG (COG01365), and, in addition to Thermococci and class I methanogens, is found only in a few bacterial genomes [38].”
eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences (2015, methodology): “Furthermore, three optimized databases are provided that cover the three domains of life: Archaea, Bacteria and Eukaryota.”
IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes (2018, methodology): “These involve assignment to COG (12) using position-specific scoring matrices and RPS-BLAST, comparing to Pfam-A (13) and TIGRfam (14) Hidden Markov Models using HMMER 3.1 (15), assignment to a subset of InterPro families using a customized version of InterProScan5 (16) and associating proteins with”