The COG database: a tool for genome-scale analysis of protein functions and evolution

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.     

The COG database: new developments in phylogenetic classification of proteins from complete genomes

The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih. gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.     

COGcollator: a web server for analysis of distant relationships between homologous protein families

Background

The Clusters of Orthologous Groups (COGs) of proteins systematize evolutionary related proteins into specific groups with similar functions. However, the available databases do not provide means to assess the extent of similarity between the COGs.

Aim

We intended to provide a method for identification and visualization of evolutionary relationships between the COGs, as well as a respective web server.

Results

Here we introduce the COGcollator, a web tool for identification of evolutionarily related COGs and their further analysis. We demonstrate the utility of this tool by identifying the COGs that contain distant homologs of (i) the catalytic subunit of bacterial rotary membrane ATP synthases and (ii) the DNA/RNA helicases of the superfamily 1.

Reviewers

This article was reviewed by Drs. Igor N. Berezovsky, Igor Zhulin and Yuri Wolf.

     

双向电泳结合质谱分析长双歧杆菌XY01分泌蛋白质图谱

菌体的分泌蛋白质在宿主和菌体的相互作用之间起着重要的作用. 本研究采用双向凝胶电泳的方法建立了长双歧杆菌XY01分泌蛋白质图谱,通过MALDI-TOF/TOF质 谱鉴定和数据库搜索,对鉴定到的分泌蛋白进行了分析. 共检测到21个蛋白质点, 成功鉴定18个蛋白质点,分别代表14个不同的蛋白质,等电点分布在4.5～7.0之间 ,分子质量分布在20 ～65 kD之间;通过COGs分类和功能分析,信号肽和细胞定位及KEGG代谢通路分析. 结果表明,这些蛋白质对菌体细胞壁/膜的形成、生物信号传导和物质代谢等起着重要作用. 研究结果为长双歧杆菌蛋白质组学和基因组学的研究提供了参考.     

ANCAC: amino acid, nucleotide, and codon analysis of COGs -- a tool for sequence bias analysis in microbial orthologs

ABSTRACT: BACKGROUND: The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins. RESULTS: Here we present ANCAC, a web tool and MySQL database for the analysis of amino acid, nucleotide, and codon frequencies in COGs on the basis of freely definable phylogenetic patterns. We demonstrate the usefulness of ANCAC by analyzing amino acid frequencies, codon usage, and GC-content in a species- or function-specific context. With respect to amino acids we, at least in part, confirm the cognate bias hypothesis by using ANCAC's NUCOCOG dataset as the largest one available for that purpose thus far. CONCLUSIONS: Using the NUCOCOG datasets, ANCAC connects taxonomic, amino acid, and nucleotide sequence information with the functional classification via COGs and provides a GUI for flexible mining for sequence-bias. Thereby, to our knowledge, it is the only tool for the analysis of sequence composition in the light of physiological roles and phylogenetic context without requirement of substantial programming-skills.     

PCOGR: Phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms

Background  

The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing (almost) exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available.     

Clusters of orthologous genes for 41 archaeal genomes and implications for evolutionary genomics of archaea

Background  

An evolutionary classification of genes from sequenced genomes that distinguishes between orthologs and paralogs is indispensable for genome annotation and evolutionary reconstruction. Shortly after multiple genome sequences of bacteria, archaea, and unicellular eukaryotes became available, an attempt on such a classification was implemented in Clusters of Orthologous Groups of proteins (COGs). Rapid accumulation of genome sequences creates opportunities for refining COGs but also represents a challenge because of error amplification. One of the practical strategies involves construction of refined COGs for phylogenetically compact subsets of genomes.     

Connected gene neighborhoods in prokaryotic genomes

A computational method was developed for delineating connected gene neighborhoods in bacterial and archaeal genomes. These gene neighborhoods are not typically present, in their entirety, in any single genome, but are held together by overlapping, partially conserved gene arrays. The procedure was applied to comparing the orders of orthologous genes, which were extracted from the database of Clusters of Orthologous Groups of proteins (COGs), in 31 prokaryotic genomes and resulted in the identification of 188 clusters of gene arrays, which included 1001 of 2890 COGs. These clusters were projected onto actual genomes to produce extended neighborhoods including additional genes, which are adjacent to the genes from the clusters and are transcribed in the same direction, which resulted in a total of 2387 COGs being included in the neighborhoods. Most of the neighborhoods consist predominantly of genes united by a coherent functional theme, but also include a minority of genes without an obvious functional connection to the main theme. We hypothesize that although some of the latter genes might have unsuspected roles, others are maintained within gene arrays because of the advantage of expression at a level that is typical of the given neighborhood. We designate this phenomenon ‘genomic hitchhiking’. The largest neighborhood includes 79 genes (COGs) and consists of overlapping, rearranged ribosomal protein superoperons; apparent genome hitchhiking is particularly typical of this neighborhood and other neighborhoods that consist of genes coding for translation machinery components. Several neighborhoods involve previously undetected connections between genes, allowing new functional predictions. Gene neighborhoods appear to evolve via complex rearrangement, with different combinations of genes from a neighborhood fixed in different lineages.     

Methods of horizontal gene transfer determination using phylogenetic data

A new approach for comparative analysis of multiple trees reconstructed for representative protein families is proposed. This approach is based on the hypothesis of gene duplication, gene loss and horizontal gene transfer and makes use of stochastic methods and optimization. We present a species tree of 40 prokaryotic organisms obtained by our algorithm on the basis of 132 clusters of orthologous groups of proteins (COGs) from the GenBank of the National Center for Biotechnology Information (USA). We also present a computer technology intended to determine horizontally transferred genes. Some application results of the technology, based on comparative analysis of protein and species trees, are given.     

Bioinformatics analysis of disordered proteins in prokaryotes

Background  

A significant number of proteins have been shown to be intrinsically disordered, meaning that they lack a fixed 3 D structure or contain regions that do not posses a well defined 3 D structure. It has also been proven that a protein's disorder content is related to its function. We have performed an exhaustive analysis and comparison of the disorder content of proteins from prokaryotic organisms (i.e., superkingdoms Archaea and Bacteria) with respect to functional categories they belong to, i.e., Clusters of Orthologous Groups of proteins (COGs) and groups of COGs-Cellular processes (Cp), Information storage and processing (Isp), Metabolism (Me) and Poorly characterized (Pc).     

The relationship between protein structure and function: a comprehensive survey with application to the yeast genome.

For most proteins in the genome databases, function is predicted via sequence comparison. In spite of the popularity of this approach, the extent to which it can be reliably applied is unknown. We address this issue by systematically investigating the relationship between protein function and structure. We focus initially on enzymes functionally classified by the Enzyme Commission (EC) and relate these to by structurally classified domains the SCOP database. We find that the major SCOP fold classes have different propensities to carry out certain broad categories of functions. For instance, alpha/beta folds are disproportionately associated with enzymes, especially transferases and hydrolases, and all-alpha and small folds with non-enzymes, while alpha+beta folds have an equal tendency either way. These observations for the database overall are largely true for specific genomes. We focus, in particular, on yeast, analyzing it with many classifications in addition to SCOP and EC (i.e. COGs, CATH, MIPS), and find clear tendencies for fold-function association, across a broad spectrum of functions. Analysis with the COGs scheme also suggests that the functions of the most ancient proteins are more evenly distributed among different structural classes than those of more modern ones. For the database overall, we identify the most versatile functions, i.e. those that are associated with the most folds, and the most versatile folds, associated with the most functions. The two most versatile enzymatic functions (hydro-lyases and O-glycosyl glucosidases) are associated with seven folds each. The five most versatile folds (TIM-barrel, Rossmann, ferredoxin, alpha-beta hydrolase, and P-loop NTP hydrolase) are all mixed alpha-beta structures. They stand out as generic scaffolds, accommodating from six to as many as 16 functions (for the exceptional TIM-barrel). At the conclusion of our analysis we are able to construct a graph giving the chance that a functional annotation can be reliably transferred at different degrees of sequence and structural similarity. Supplemental information is available from http://bioinfo.mbb.yale.edu/genome/foldfunc++ +.     

Gene content and organization of an 85-kb DNA segment from the genome of the phytopathogenic mollicute Spiroplasma kunkelii

Spiroplasma kunkelii, the causative agent of corn stunt disease in maize ( Zea mays L.), is a helical, cell wall-less prokaryote assigned to the class Mollicutes. As part of a project to sequence the entire S. kunkelii genome, we analyzed an 85-kb DNA segment from the pathogenic strain CR2-3x. This genome segment contains 101 ORFs and two tRNA genes. The majority of the ORFs code for predicted proteins that can be assigned to respective clusters of orthologous groups (COGs). These COGs cover diverse functional categories including genetic information storage and processing, cellular processes, and metabolism. The most notable gene cluster in this genome segment is a super-operon capable of encoding 24 ribosomal proteins. The organization of genes in this operon reflects the unique evolutionary position of the spiroplasma. Gene duplications, domain rearrangements, and frameshift mutations in the segment are interpreted as indicators of phase variation in the spiroplasma. To our knowledge, this is the first analysis of a large genome segment from a plant pathogenic spiroplasma.Communicated by W. Goebel     

The COG database: an updated version includes eukaryotes

Background

The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies.

Results

We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or ~54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

Conclusion

The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.     

Similarity-based gene detection: using COGs to find evolutionarily-conserved ORFs

Background  

Experimental verification of gene products has not kept pace with the rapid growth of microbial sequence information. However, existing annotations of gene locations contain sufficient information to screen for probable errors. Furthermore, comparisons among genomes become more informative as more genomes are examined. We studied all open reading frames (ORFs) of at least 30 codons from the genomes of 27 sequenced bacterial strains. We grouped the potential peptide sequences encoded from the ORFs by forming Clusters of Orthologous Groups (COGs). We used this grouping in order to find homologous relationships that would not be distinguishable from noise when using simple BLAST searches. Although COG analysis was initially developed to group annotated genes, we applied it to the task of grouping anonymous DNA sequences that may encode proteins.     

Using the COG Database to Improve Gene Recognition in Complete Genomes

A complete understanding of the biology of an organism necessarily starts with knowledge of its genetic makeup. Proteins encoded in a genome must be identified and characterized, and the presence or absence of specific sets of proteins must be noted in order to determine the possible biochemical pathways or functional systems utilized by that organism. The COG database presents a set of tools suited to these purposes, including the ability to select protein families (COGs) that contain proteins from a specified set of species. The selection is based upon a phylogenetic pattern, which is a shorthand representation of the presence or absence of a particular species in a COG. Here we present the use of phylogenetic patterns as a means to perform targeted searches for undetected protein-coding genes in complete genomes.     

Towards understanding the first genome sequence of a crenarchaeon by genome annotation using clusters of orthologous groups of proteins (COGs)

Background  

Standard archival sequence databases have not been designed as tools for genome annotation and are far from being optimal for this purpose. We used the database of Clusters of Orthologous Groups of proteins (COGs) to reannotate the genomes of two archaea, Aeropyrum pernix, the first member of the Crenarchaea to be sequenced, and Pyrococcus abyssi.     

Detailed proteome analysis of growing cells of the planctomycete Rhodopirellula baltica SH1T

Rhodopirellula baltica SH1(T), which was isolated from the water column of the Kieler Bight, a bay in the southwestern Baltic Sea, is a marine aerobic, heterotrophic representative of the ubiquitous bacterial phylum Planctomycetes. We analyzed the R. baltica proteome by applying different preanalytical protein as well as peptide separation techniques (1-D and 2-DE, HPLC separation) prior to MS. That way, we could identify a total of 1115 nonredundant proteins from the intracellular proteome and from different cell wall protein fractions. With the contribution of 709 novel proteins resulting from this study, the current comprehensive R. baltica proteomic dataset consists of 1267 unique proteins (accounting for 17.3% of the total putative protein-coding ORFs), including 261 proteins with a predicted signal peptide. The identified proteins were functionally categorized using Clusters of Orthologous Groups (COGs), and their potential cellular locations were predicted by bioinformatic tools. A unique protein family that contains several YTV domains and is rich in cysteine and proline was found to be a component of the R. baltica proteinaceous cell wall. Based on this comprehensive proteome analysis a global schema of the major metabolic pathways of growing R. baltica cells was deduced.