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      Kinomer v. 1.0: a database of systematically classified eukaryotic protein kinases

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        , , *
      Nucleic Acids Research
      Oxford University Press

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          Abstract

          The regulation of protein function through reversible phosphorylation by protein kinases and phosphatases is a general mechanism controlling virtually every cellular activity. Eukaryotic protein kinases can be classified into distinct, well-characterized groups based on amino acid sequence similarity and function. We recently reported a highly sensitive and accurate hidden Markov model-based method for the automatic detection and classification of protein kinases into these specific groups. The Kinomer v. 1.0 database presented here contains annotated classifications for the protein kinase complements of 43 eukaryotic genomes. These span the taxonomic range and include fungi (16 species), plants (6), diatoms (1), amoebas (2), protists (1) and animals (17). The kinomes are stored in a relational database and are accessible through a web interface on the basis of species, kinase group or a combination of both. In addition, the Kinomer v. 1.0 HMM library is made available for users to perform classification on arbitrary sequences. The Kinomer v. 1.0 database is a continually updated resource where direct comparison of kinase sequences across kinase groups and across species can give insights into kinase function and evolution. Kinomer v. 1.0 is available at http://www.compbio.dundee.ac.uk/kinomer/.

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          Most cited references37

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          The protein kinase complement of the human genome.

          G. Manning (2002)
          We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
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            Profile hidden Markov models.

            S. Eddy (1998)
            The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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              The genome sequence of the malaria mosquito Anopheles gambiae.

              Anopheles gambiae is the principal vector of malaria, a disease that afflicts more than 500 million people and causes more than 1 million deaths each year. Tenfold shotgun sequence coverage was obtained from the PEST strain of A. gambiae and assembled into scaffolds that span 278 million base pairs. A total of 91% of the genome was organized in 303 scaffolds; the largest scaffold was 23.1 million base pairs. There was substantial genetic variation within this strain, and the apparent existence of two haplotypes of approximately equal frequency ("dual haplotypes") in a substantial fraction of the genome likely reflects the outbred nature of the PEST strain. The sequence produced a conservative inference of more than 400,000 single-nucleotide polymorphisms that showed a markedly bimodal density distribution. Analysis of the genome sequence revealed strong evidence for about 14,000 protein-encoding transcripts. Prominent expansions in specific families of proteins likely involved in cell adhesion and immunity were noted. An expressed sequence tag analysis of genes regulated by blood feeding provided insights into the physiological adaptations of a hematophagous insect.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2009
                January 2009
                30 October 2008
                30 October 2008
                : 37
                : Database issue , Database issue
                : D244-D250
                Affiliations
                College of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland, UK
                Author notes
                *To whom correspondence should be addressed. Tel: +44 1382 385860; Fax: +44 1382 385764; Email: geoff@ 123456compbio.dundee.ac.uk

                Present address: Diego Miranda-Saavedra, Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0XY, UK.

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as the joint First Authors.

                Article
                gkn834
                10.1093/nar/gkn834
                2686601
                18974176
                fb0c3244-0771-44ca-9072-1a91068eb6e7
                © 2008 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 August 2008
                : 13 October 2008
                : 14 October 2008
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                Genetics
                Genetics

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