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BMC Bioinformatics | 2003

The COG database: an updated version includes eukaryotes

Roman L. Tatusov; Natalie D. Fedorova; John D. Jackson; Aviva R. Jacobs; Boris Kiryutin; Eugene V. Koonin; Dmitri M. Krylov; Raja Mazumder; Sergei L. Mekhedov; Anastasia N. Nikolskaya; B Sridhar Rao; Sergei Smirnov; Alexander V. Sverdlov; Sona Vasudevan; Yuri I. Wolf; Jodie J. Yin; Darren A. Natale

BackgroundThe 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.ResultsWe 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 euk aryotic o rthologous g roups. 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 euk aryotic o rthologous g roups (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.ConclusionThe 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.


Nucleic Acids Research | 2004

UniProt: the Universal Protein knowledgebase

Rolf Apweiler; Amos Marc Bairoch; Cathy H. Wu; Winona C. Barker; Brigitte Boeckmann; Serenella Ferro; Elisabeth Gasteiger; Hongzhan Huang; Rodrigo Lopez; Michele Magrane; María Martín; Darren A. Natale; Claire O’Donovan; Nicole Redaschi; Lai-Su L. Yeh

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.


Nucleic Acids Research | 2000

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

Roman L. Tatusov; Michael Y. Galperin; Darren A. Natale; Eugene V. Koonin

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.


Nucleic Acids Research | 2001

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

Roman L. Tatusov; Darren A. Natale; Igor Garkavtsev; Tatiana Tatusova; Uma Shankavaram; Bachoti S. Rao; Boris Kiryutin; Michael Y. Galperin; Natalie D. Fedorova; Eugene V. Koonin

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.


Nucleic Acids Research | 2009

InterPro: the integrative protein signature database

Sarah Hunter; Rolf Apweiler; Teresa K. Attwood; Amos Marc Bairoch; Alex Bateman; David Binns; Peer Bork; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Aurélie Laugraud; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Jaina Mistry; Alex L. Mitchell; Nicola Mulder; Darren A. Natale; Christine A. Orengo; Antony F. Quinn; Jeremy D. Selengut

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/).


Nucleic Acids Research | 2012

InterPro in 2011: new developments in the family and domain prediction database

Sarah Hunter; P. D. Jones; Alex L. Mitchell; Rolf Apweiler; Teresa K. Attwood; Alex Bateman; Thomas Bernard; David Binns; Peer Bork; Sarah W. Burge; Edouard de Castro; Penny Coggill; Matthew Corbett; Ujjwal Das; Louise Daugherty; Lauranne Duquenne; Robert D. Finn; Matthew Fraser; Julian Gough; Daniel H. Haft; Nicolas Hulo; Daniel Kahn; Elizabeth Kelly; Ivica Letunic; David M. Lonsdale; Rodrigo Lopez; John Maslen; Craig McAnulla; Jennifer McDowall; Conor McMenamin

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.


Nucleic Acids Research | 2006

The Universal Protein Resource (UniProt): an expanding universe of protein information

Cathy H. Wu; Rolf Apweiler; Amos Marc Bairoch; Darren A. Natale; Winona C. Barker; Brigitte Boeckmann; Serenella Ferro; Elisabeth Gasteiger; Hongzhan Huang; Rodrigo Lopez; Michele Magrane; María Martín; Raja Mazumder; Claire O'Donovan; Nicole Redaschi; Baris E. Suzek

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 .


Nucleic Acids Research | 2015

The InterPro protein families database: the classification resource after 15 years

Alex L. Mitchell; Hsin-Yu Chang; Louise Daugherty; Matthew Fraser; Sarah Hunter; Rodrigo Lopez; Craig McAnulla; Conor McMenamin; Gift Nuka; Sebastien Pesseat; Amaia Sangrador-Vegas; Maxim Scheremetjew; Claudia Rato; Siew-Yit Yong; Alex Bateman; Marco Punta; Teresa K. Attwood; Christian J. A. Sigrist; Nicole Redaschi; Catherine Rivoire; Ioannis Xenarios; Daniel Kahn; Dominique Guyot; Peer Bork; Ivica Letunic; Julian Gough; Matt E. Oates; Daniel H. Haft; Hongzhan Huang; Darren A. Natale

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.


Nucleic Acids Research | 2017

InterPro in 2017—beyond protein family and domain annotations

Robert D. Finn; Teresa K. Attwood; Patricia C. Babbitt; Alex Bateman; Peer Bork; Alan Bridge; Hsin Yu Chang; Zsuzsanna Dosztányi; Sara El-Gebali; Matthew Fraser; Julian Gough; David R Haft; Gemma L. Holliday; Hongzhan Huang; Xiaosong Huang; Ivica Letunic; Rodrigo Lopez; Shennan Lu; Huaiyu Mi; Jaina Mistry; Darren A. Natale; Marco Necci; Gift Nuka; Christine A. Orengo; Youngmi Park; Sebastien Pesseat; Damiano Piovesan; Simon Potter; Neil D. Rawlings; Nicole Redaschi

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPros predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.


Proceedings of the National Academy of Sciences of the United States of America | 2002

The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens

Alexei I. Slesarev; Katja V. Mezhevaya; Kira S. Makarova; Nikolai Polushin; Ov Shcherbinina; Vera V. Shakhova; Galina I. Belova; L. Aravind; Darren A. Natale; Igor B. Rogozin; Roman L. Tatusov; Yuri I. Wolf; Karl O. Stetter; Andrei Malykh; Eugene V. Koonin; Sergei A. Kozyavkin

We have determined the complete 1,694,969-nt sequence of the GC-rich genome of Methanopyrus kandleri by using a whole direct genome sequencing approach. This approach is based on unlinking of genomic DNA with the ThermoFidelase version of M. kandleri topoisomerase V and cycle sequencing directed by 2′-modified oligonucleotides (Fimers). Sequencing redundancy (3.3×) was sufficient to assemble the genome with less than one error per 40 kb. Using a combination of sequence database searches and coding potential prediction, 1,692 protein-coding genes and 39 genes for structural RNAs were identified. M. kandleri proteins show an unusually high content of negatively charged amino acids, which might be an adaptation to the high intracellular salinity. Previous phylogenetic analysis of 16S RNA suggested that M. kandleri belonged to a very deep branch, close to the root of the archaeal tree. However, genome comparisons indicate that, in both trees constructed using concatenated alignments of ribosomal proteins and trees based on gene content, M. kandleri consistently groups with other archaeal methanogens. M. kandleri shares the set of genes implicated in methanogenesis and, in part, its operon organization with Methanococcus jannaschii and Methanothermobacter thermoautotrophicum. These findings indicate that archaeal methanogens are monophyletic. A distinctive feature of M. kandleri is the paucity of proteins involved in signaling and regulation of gene expression. Also, M. kandleri appears to have fewer genes acquired via lateral transfer than other archaea. These features might reflect the extreme habitat of this organism.

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Cathy H. Wu

University of Delaware

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Hongzhan Huang

University of New Hampshire

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Eugene V. Koonin

Uniformed Services University of the Health Sciences

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Raja Mazumder

George Washington University

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Winona C. Barker

Georgetown University Medical Center

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