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1.  Quantitative Organelle Proteomics of MCF-7 Breast Cancer Cells Reveals Multiple Subcellular Locations for Proteins in Cellular Functional Processes 
Journal of proteome research  2010;9(1):495-508.
We have combined sucrose density gradient subcellular fractionation with quantitative, tandem-mass-spectrometry-based shotgun proteomics to investigate spatial distributions of proteins in MCF-7 breast cancer cells. Emphasis was placed on four major organellar compartments: cytosol, plasma membrane, endoplasmic reticulum, and mitochondrion. Two-thousand one-hundred eighty-four proteins were securely identified. Four-hundred eighty-one proteins (22.0% of total proteins identified) were found in unique sucrose gradient fractions, suggesting they may have unique subcellular locations. 454 proteins (20.8%) were found to be ubiquitously distributed. The remaining 1249 proteins (57.2%) were consistent with intermediate distribution over multiple, but not all, subcellular locations. Ninety-four proteins implicated in breast cancer and 478 other proteins which share the same five major cellular biological processes with a majority of the breast cancer proteins were observed in 334 and 1223 subcellular locations, respectively. The data obtained is used to evaluate the possibility of defining more exact sets of subcellular organelles, the completeness of current descriptions of spatial distribution of cellular proteins, the importance of multiple subcellular locations for proteins in functional processes, the subcellular distribution of proteins related to breast cancer, and the possibility of using these methods for dynamic spatio/temporal studies of function/regulation in MCF-7 breast cancer cells.
doi:10.1021/pr9008332
PMCID: PMC4261601  PMID: 19911851
quantitative proteomics; mass spectrometry; breast cancer; MCF-7 cells; subcellular organelles; subcellular location
2.  OMIT: Dynamic, Semi-Automated Ontology Development for the microRNA Domain 
PLoS ONE  2014;9(7):e100855.
As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.
doi:10.1371/journal.pone.0100855
PMCID: PMC4099014  PMID: 25025130
3.  Protein Ontology: a controlled structured network of protein entities 
Nucleic Acids Research  2013;42(Database issue):D415-D421.
The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.
doi:10.1093/nar/gkt1173
PMCID: PMC3964965  PMID: 24270789
4.  Recent advances in biocuration: Meeting Report from the fifth International Biocuration Conference 
The 5th International Biocuration Conference brought together over 300 scientists to exchange on their work, as well as discuss issues relevant to the International Society for Biocuration’s (ISB) mission. Recurring themes this year included the creation and promotion of gold standards, the need for more ontologies, and more formal interactions with journals. The conference is an essential part of the ISB's goal to support exchanges among members of the biocuration community. Next year's conference will be held in Cambridge, UK, from 7 to 10 April 2013. In the meanwhile, the ISB website provides information about the society's activities (http://biocurator.org), as well as related events of interest.
doi:10.1093/database/bas036
PMCID: PMC3483532  PMID: 23110974
6.  InterPro in 2011: new developments in the family and domain prediction database 
Nucleic Acids Research  2011;40(Database issue):D306-D312.
InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.
doi:10.1093/nar/gkr948
PMCID: PMC3245097  PMID: 22096229
7.  The representation of protein complexes in the Protein Ontology (PRO) 
BMC Bioinformatics  2011;12:371.
Background
Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes.
Description
We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/.
Conclusion
PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species.
doi:10.1186/1471-2105-12-371
PMCID: PMC3189193  PMID: 21929785
8.  Representative Proteomes: A Stable, Scalable and Unbiased Proteome Set for Sequence Analysis and Functional Annotation 
PLoS ONE  2011;6(4):e18910.
The accelerating growth in the number of protein sequences taxes both the computational and manual resources needed to analyze them. One approach to dealing with this problem is to minimize the number of proteins subjected to such analysis in a way that minimizes loss of information. To this end we have developed a set of Representative Proteomes (RPs), each selected from a Representative Proteome Group (RPG) containing similar proteomes calculated based on co-membership in UniRef50 clusters. A Representative Proteome is the proteome that can best represent all the proteomes in its group in terms of the majority of the sequence space and information. RPs at 75%, 55%, 35% and 15% co-membership threshold (CMT) are provided to allow users to decrease or increase the granularity of the sequence space based on their requirements. We find that a CMT of 55% (RP55) most closely follows standard taxonomic classifications. Further analysis of this set reveals that sequence space is reduced by more than 80% relative to UniProtKB, while retaining both sequence diversity (over 95% of InterPro domains) and annotation information (93% of experimentally characterized proteins). All sets can be browsed and are available for sequence similarity searches and download at http://www.proteininformationresource.org/rps, while the set of 637 RPs determined using a 55% CMT are also available for text searches. Potential applications include sequence similarity searches, protein classification and targeted protein annotation and characterization.
doi:10.1371/journal.pone.0018910
PMCID: PMC3083393  PMID: 21556138
9.  Novel sequence feature variant type analysis of the HLA genetic association in systemic sclerosis 
Human Molecular Genetics  2009;19(4):707-719.
We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.
doi:10.1093/hmg/ddp521
PMCID: PMC2807365  PMID: 19933168
10.  The Protein Ontology: a structured representation of protein forms and complexes 
Nucleic Acids Research  2010;39(Database issue):D539-D545.
The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.
doi:10.1093/nar/gkq907
PMCID: PMC3013777  PMID: 20935045
11.  Community annotation in biology 
Biology Direct  2010;5:12.
Attempts to engage the scientific community to annotate biological data (such as protein/gene function) stored in databases have not been overly successful. There are several hypotheses on why this has not been successful but it is not clear which of these hypotheses are correct. In this study we have surveyed 50 biologists (who have recently published a paper characterizing a gene or protein) to better understand what would make them interested in providing input/contributions to biological databases. Based on our survey two things become clear: a) database managers need to proactively contact biologists to solicit contributions; and b) potential contributors need to be provided with an easy-to-use interface and clear instructions on what to annotate. Other factors such as 'reward' and 'employer/funding agency recognition' previously perceived as motivators was found to be less important. Based on this study we propose community annotation projects should devote resources to direct solicitation for input and streamlining of the processes or interfaces used to collect this input.
Reviewers
This article was reviewed by I. King Jordan, Daniel Haft and Yuriy Gusev
doi:10.1186/1745-6150-5-12
PMCID: PMC2834641  PMID: 20167071
12.  TGF-beta signaling proteins and the Protein Ontology 
BMC Bioinformatics  2009;10(Suppl 5):S3.
Background
The Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO.
Results
PRO is manually curated on the basis of PrePRO, an automatically generated file with content derived from standard protein data sources. Manual curation ensures that the treatment of the protein classes and the internal and external relationships conform to the PRO framework. The current release of PRO is based upon experimental data from mouse and human proteins wherein equivalent protein forms are represented by single terms. In addition to the PRO ontology, the annotation of PRO terms is released as a separate PRO association file, which contains, for each given PRO term, an annotation from the experimentally characterized sub-types as well as the corresponding database identifiers and sequence coordinates. The annotations are added in the form of relationship to other ontologies. Whenever possible, equivalent forms in other species are listed to facilitate cross-species comparison. Splice and allelic variants, gene fusion products and modified protein forms are all represented as entities in the ontology. Therefore, PRO provides for the representation of protein entities and a resource for describing the associated data. This makes PRO useful both for proteomics studies where isoforms and modified forms must be differentiated, and for studies of biological pathways, where representations need to take account of the different ways in which the cascade of events may depend on specific protein modifications.
Conclusion
PRO provides a framework for the formal representation of protein classes and protein forms in the OBO Foundry. It is designed to enable data retrieval and integration and machine reasoning at the molecular level of proteins, thereby facilitating cross-species comparisons, pathway analysis, disease modeling and the generation of new hypotheses.
doi:10.1186/1471-2105-10-S5-S3
PMCID: PMC2679403  PMID: 19426460
13.  InterPro: the integrative protein signature database 
Nucleic Acids Research  2008;37(Database issue):D211-D215.
The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total ∼58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein–protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).
doi:10.1093/nar/gkn785
PMCID: PMC2686546  PMID: 18940856
14.  Framework for a Protein Ontology 
BMC Bioinformatics  2007;8(Suppl 9):S1.
Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies – one that describes the protein entities themselves and their relationships. We have designed the PRotein Ontology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways.
doi:10.1186/1471-2105-8-S9-S1
PMCID: PMC2217659  PMID: 18047702
15.  The Universal Protein Resource (UniProt): an expanding universe of protein information 
Nucleic Acids Research  2005;34(Database issue):D187-D191.
The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at or downloaded at .
doi:10.1093/nar/gkj161
PMCID: PMC1347523  PMID: 16381842
16.  Computational identification of strain-, species- and genus-specific proteins 
BMC Bioinformatics  2005;6:279.
Background
The identification of unique proteins at different taxonomic levels has both scientific and practical value. Strain-, species- and genus-specific proteins can provide insight into the criteria that define an organism and its relationship with close relatives. Such proteins can also serve as taxon-specific diagnostic targets.
Description
A pipeline using a combination of computational and manual analyses of BLAST results was developed to identify strain-, species-, and genus-specific proteins and to catalog the closest sequenced relative for each protein in a proteome. Proteins encoded by a given strain are preliminarily considered to be unique if BLAST, using a comprehensive protein database, fails to retrieve (with an e-value better than 0.001) any protein not encoded by the query strain, species or genus (for strain-, species- and genus-specific proteins respectively), or if BLAST, using the best hit as the query (reverse BLAST), does not retrieve the initial query protein. Results are manually inspected for homology if the initial query is retrieved in the reverse BLAST but is not the best hit. Sequences unlikely to retrieve homologs using the default BLOSUM62 matrix (usually short sequences) are re-tested using the PAM30 matrix, thereby increasing the number of retrieved homologs and increasing the stringency of the search for unique proteins. The above protocol was used to examine several food- and water-borne pathogens. We find that the reverse BLAST step filters out about 22% of proteins with homologs that would otherwise be considered unique at the genus and species levels. Analysis of the annotations of unique proteins reveals that many are remnants of prophage proteins, or may be involved in virulence. The data generated from this study can be accessed and further evaluated from the CUPID (Core and Unique Protein Identification) system web site (updated semi-annually) at .
Conclusion
CUPID provides a set of proteins specific to a genus, species or a strain, and identifies the most closely related organism.
doi:10.1186/1471-2105-6-279
PMCID: PMC1310627  PMID: 16305751
17.  A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes 
Genome Biology  2004;5(2):R7.
We examined functional and evolutionary patterns in the recently constructed set of 5,873 clusters of predicted orthologs from seven eukaryotic genomes. The analysis reveals a conserved core of largely essential eukaryotic genes as well as major diversification and innovation associated with evolution of eukaryotic genomes.
Background
Sequencing the genomes of multiple, taxonomically diverse eukaryotes enables in-depth comparative-genomic analysis which is expected to help in reconstructing ancestral eukaryotic genomes and major events in eukaryotic evolution and in making functional predictions for currently uncharacterized conserved genes.
Results
We examined functional and evolutionary patterns in the recently constructed set of 5,873 clusters of predicted orthologs (eukaryotic orthologous groups or KOGs) from seven eukaryotic genomes: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Encephalitozoon cuniculi. Conservation of KOGs through the phyletic range of eukaryotes strongly correlates with their functions and with the effect of gene knockout on the organism's viability. The approximately 40% of KOGs that are represented in six or seven species are enriched in proteins responsible for housekeeping functions, particularly translation and RNA processing. These conserved KOGs are often essential for survival and might approximate the minimal set of essential eukaryotic genes. The 131 single-member, pan-eukaryotic KOGs we identified were examined in detail. For around 20 that remained uncharacterized, functions were predicted by in-depth sequence analysis and examination of genomic context. Nearly all these proteins are subunits of known or predicted multiprotein complexes, in agreement with the balance hypothesis of evolution of gene copy number. Other KOGs show a variety of phyletic patterns, which points to major contributions of lineage-specific gene loss and the 'invention' of genes new to eukaryotic evolution. Examination of the sets of KOGs lost in individual lineages reveals co-elimination of functionally connected genes. Parsimonious scenarios of eukaryotic genome evolution and gene sets for ancestral eukaryotic forms were reconstructed. The gene set of the last common ancestor of the crown group consists of 3,413 KOGs and largely includes proteins involved in genome replication and expression, and central metabolism. Only 44% of the KOGs, mostly from the reconstructed gene set of the last common ancestor of the crown group, have detectable homologs in prokaryotes; the remainder apparently evolved via duplication with divergence and invention of new genes.
Conclusions
The KOG analysis reveals a conserved core of largely essential eukaryotic genes as well as major diversification and innovation associated with evolution of eukaryotic genomes. The results provide quantitative support for major trends of eukaryotic evolution noticed previously at the qualitative level and a basis for detailed reconstruction of evolution of eukaryotic genomes and biology of ancestral forms.
PMCID: PMC395751  PMID: 14759257
18.  UniProt: the Universal Protein knowledgebase 
Nucleic Acids Research  2004;32(Database issue):D115-D119.
To provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information, the Swiss-Prot, TrEMBL and PIR protein database activities have united to form the Universal Protein Knowledgebase (UniProt) consortium. Our mission is to provide a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and query interfaces. The central database will have two sections, corresponding to the familiar Swiss-Prot (fully manually curated entries) and TrEMBL (enriched with automated classification, annotation and extensive cross-references). For convenient sequence searches, UniProt also provides several non-redundant sequence databases. The UniProt NREF (UniRef) databases provide representative subsets of the knowledgebase suitable for efficient searching. The comprehensive UniProt Archive (UniParc) is updated daily from many public source databases. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). The scientific community is encouraged to submit data for inclusion in UniProt.
doi:10.1093/nar/gkh131
PMCID: PMC308865  PMID: 14681372
19.  PIRSF: family classification system at the Protein Information Resource 
Nucleic Acids Research  2004;32(Database issue):D112-D114.
The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent–child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.
doi:10.1093/nar/gkh097
PMCID: PMC308831  PMID: 14681371
20.  The COG database: an updated version includes eukaryotes 
BMC Bioinformatics  2003;4:41.
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.
doi:10.1186/1471-2105-4-41
PMCID: PMC222959  PMID: 12969510
21.  Congruent evolution of different classes of non-coding DNA in prokaryotic genomes 
Nucleic Acids Research  2002;30(19):4264-4271.
Prokaryotic genomes are considered to be ‘wall-to-wall’ genomes, which consist largely of genes for proteins and structural RNAs, with only a small fraction of the genomic DNA allotted to intergenic regions, which are thought to typically contain regulatory signals. The majority of bacterial and archaeal genomes contain 6–14% non-coding DNA. Significant positive correlations were detected between the fraction of non-coding DNA and inter- and intra-operonic distances, suggesting that different classes of non-coding DNA evolve congruently. In contrast, no correlation was found between any of these characteristics of non-coding sequences and the number of genes or genome size. Thus, the non-coding regions and the gene sets in prokaryotes seem to evolve in different regimes. The evolution of non-coding regions appears to be determined primarily by the selective pressure to minimize the amount of non-functional DNA, while maintaining essential regulatory signals, because of which the content of non-coding DNA in different genomes is relatively uniform and intra- and inter-operonic non-coding regions evolve congruently. In contrast, the gene set is optimized for the particular environmental niche of the given microbe, which results in the lack of correlation between the gene number and the characteristics of non-coding regions.
PMCID: PMC140549  PMID: 12364605
22.  The COG database: new developments in phylogenetic classification of proteins from complete genomes 
Nucleic Acids Research  2001;29(1):22-28.
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.
PMCID: PMC29819  PMID: 11125040
23.  Towards understanding the first genome sequence of a crenarchaeon by genome annotation using clusters of orthologous groups of proteins (COGs) 
Genome Biology  2000;1(5):research0009.1-research0009.19.
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.
Results:
A. pernix and P. abyssi proteins were assigned to COGs using the COGNITOR program; the results were verified on a case-by-case basis and augmented by additional database searches using the PSI-BLAST and TBLASTN programs. Functions were predicted for over 300 proteins from A. pernix, which could not be assigned a function using conventional methods with a conservative sequence similarity threshold, an approximately 50% increase compared to the original annotation. A. pernix shares most of the conserved core of proteins that were previously identified in the Euryarchaeota. Cluster analysis or distance matrix tree construction based on the co-occurrence of genomes in COGs showed that A. pernix forms a distinct group within the archaea, although grouping with the two species of Pyrococci, indicative of similar repertoires of conserved genes, was observed. No indication of a specific relationship between Crenarchaeota and eukaryotes was obtained in these analyses. Several proteins that are conserved in Euryarchaeota and most bacteria are unexpectedly missing in A. pernix, including the entire set of de novo purine biosynthesis enzymes, the GTPase FtsZ (a key component of the bacterial and euryarchaeal cell-division machinery), and the tRNA-specific pseudouridine synthase, previously considered universal. A. pernix is represented in 48 COGs that do not contain any euryarchaeal members. Many of these proteins are TCA cycle and electron transport chain enzymes, reflecting the aerobic lifestyle of A. pernix.
Conclusions:
Special-purpose databases organized on the basis of phylogenetic analysis and carefully curated with respect to known and predicted protein functions provide for a significant improvement in genome annotation. A differential genome display approach helps in a systematic investigation of common and distinct features of gene repertoires and in some cases reveals unexpected connections that may be indicative of functional similarities between phylogenetically distant organisms and of lateral gene exchange.
PMCID: PMC15027  PMID: 11178258
24.  The COG database: a tool for genome-scale analysis of protein functions and evolution 
Nucleic Acids Research  2000;28(1):33-36.
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 ~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.
PMCID: PMC102395  PMID: 10592175

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