Wed, 20 Aug 2008 21:26:07 BST CiteULike: neils's internet http://www.citeulike.org/user/neils/tag/internet CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/neils/article/2402373 <i>Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3635-3641.</i><br /><br />Scansite identifies short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with protein or phospholipid ligands. Each sequence motif is represented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and phage display experiments. Predicted domain-motif interactions from Scansite can be sequentially combined, allowing segments of biological pathways to be constructed in silico. The current release of Scansite, version 2.0, includes 62 motifs characterizing the binding and/or substrate specificities of many families of Ser/Thr- or Tyr-kinases, SH2, SH3, PDZ, 14-3-3 and PTB domains, together with signature motifs for PtdIns(3,4,5)P(3)-specific PH domains. Scansite 2.0 contains significant improvements to its original interface, including a number of new generalized user features and significantly enhanced performance. Searches of all SWISS-PROT, TrEMBL, Genpept and Ensembl protein database entries are now possible with run times reduced by approximately 60% when compared with Scansite version 1.0. Scansite 2.0 allows restricted searching of species-specific proteins, as well as isoelectric point and molecular weight sorting to facilitate comparison of predictions with results from two-dimensional gel electrophoresis experiments. Support for user-defined motifs has been increased, allowing easier input of user-defined matrices and permitting user-defined motifs to be combined with pre-compiled Scansite motifs for dual motif searching. In addition, a new series of Sequence Match programs for non-quantitative user-defined motifs has been implemented. Scansite is available via the World Wide Web at http://scansite.mit.edu. Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. JC Obenauer LC Cantley MB Yaffe doi:10.1093/nar/gkg584 Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3635-3641. 2008-02-20T10:21:00-00:00 2003 Nucleic Acids Res 1362-4962 31 13 3635 3641 algorithm amino-acid analysis article-nar article-predikin binding database internet motifs phosphorylation protein proteome sequence signal sites software structure transduction http://www.citeulike.org/user/neils/article/2396519 <i>Source Code for Biology and Medicine, Vol. 3 (18 February 2008), 2.</i><br /><br />Background Life Science Identifiers (LSIDs) are persistent, globally unique identifiers for biological objects. The decentralised nature of LSIDs makes them attractive for identifying distributed resources. Data of interest to biodiversity researchers (including specimen records, images, taxonomic names, and DNA sequences) are distributed over many different providers, and this community has adopted LSIDs as the identifier of choice. Results LSID Tester is a web application written in PHP. Given a LSID the application performs seven tests, reporting the results at each step. If all tests are successful the metadata associated with the LSID is displayed, and can be viewed in a range of formats. Conclusions The software provides a tool for testing a LSID resolution service. Source code is available from http://code.google.com/p/lsid-php/, and an instance of the application can be viewed at http://linnaeus.zoology.gla.ac.uk/~rpage/lsid/tester. LSID Tester, a tool for testing Life Science Identifier resolution services Roderic Page Source Code for Biology and Medicine, Vol. 3 (18 February 2008), 2. 2008-02-18T22:18:12-00:00 2008 Source Code for Biology and Medicine 3 2 internet lsid names resolution service http://www.citeulike.org/user/neils/article/2054461 <i>Nucleic Acids Res, Vol. 33, No. Web Server issue. (Jul 2005), pp. W184-W187.</i><br /><br />Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at http://973-proteinweb.ustc.edu.cn/gps/gps_web/. GPS: a comprehensive www server for phosphorylation sites prediction. Yu Xue Fengfeng Zhou Minjie Zhu Kashif Ahmed Guoliang Chen Xuebiao Yao Nucleic Acids Res, Vol. 33, No. Web Server issue. (Jul 2005), pp. W184-W187. 2007-12-04T03:22:11-00:00 2005 Nucleic Acids Res 33 Web Server issue W184 W187 analysis article-nar article-predikin internet kinase microfilament phosphoprotein phosphorylation protein sequence software specificity substrate tissue http://www.citeulike.org/user/neils/article/2054458 <i>Genome Res, Vol. 12, No. 10. (Oct 2002), pp. 1611-1618.</i><br /><br />The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort. The Bioperl toolkit: Perl modules for the life sciences. Jason Stajich David Block Kris Boulez Steven Brenner Stephen Chervitz Chris Dagdigian Georg Fuellen James Gilbert Ian Korf Hilmar Lapp Heikki Lehväslaiho Chad Matsalla Chris Mungall Brian Osborne Matthew Pocock Peter Schattner Martin Senger Lincoln Stein Elia Stupka Mark Wilkinson Ewan Birney Genome Res, Vol. 12, No. 10. (Oct 2002), pp. 1611-1618. 2007-12-04T03:22:10-00:00 2002 Genome Res 12 10 1611 1618 algorithm animal article-nar article-predikin biological biology computational computer database design genetics graphics human integration internet management online perl sciences software system http://www.citeulike.org/user/neils/article/2054443 <i>Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D257-D260.</i><br /><br />The Simple Modular Architecture Research Tool (SMART) is an online resource (http://smart.embl.de/) used for protein domain identification and the analysis of protein domain architectures. Many new features were implemented to make SMART more accessible to scientists from different fields. The new 'Genomic' mode in SMART makes it easy to analyze domain architectures in completely sequenced genomes. Domain annotation has been updated with a detailed taxonomic breakdown and a prediction of the catalytic activity for 50 SMART domains is now available, based on the presence of essential amino acids. Furthermore, intrinsically disordered protein regions can be identified and displayed. The network context is now displayed in the results page for more than 350 000 proteins, enabling easy analyses of domain interactions. SMART 5: domains in the context of genomes and networks. Ivica Letunic Richard Copley Birgit Pils Stefan Pinkert Jörg Schultz Peer Bork Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D257-D260. 2007-12-04T03:22:10-00:00 2006 Nucleic Acids Res 34 Database issue D257 D260 alignment analysis article-nar article-pka-pkg article-predikin biological catalysis catalytic complex database domain genomics interface internet models multiprotein protein sequence structure tertiary user-computer http://www.citeulike.org/user/neils/article/2054439 <i>J Mol Biol, Vol. 305, No. 3. (Jan 2001), pp. 567-580.</i><br /><br />We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 \% of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 \%, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 \% of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. A Krogh B Larsson G von Heijne EL Sonnhammer J Mol Biol, Vol. 305, No. 3. (Jan 2001), pp. 567-580. 2007-12-04T03:22:10-00:00 2001 J Mol Biol 305 3 567 580 article-nar article-predikin bacteria chain computational database design fungi genome internet markov membrane of plant porin protein reproducibility research results secondary sensitivity signal software solubility sorting specificity structure http://www.citeulike.org/user/neils/article/2054429 <i>Nucleic Acids Res, Vol. 33, No. Web Server issue. (Jul 2005), pp. W226-W229.</i><br /><br />KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/. KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Hsien Da Huang Tzong Lee Shih Tzeng Jorng Horng Nucleic Acids Res, Vol. 33, No. Web Server issue. (Jul 2005), pp. W226-W229. 2007-12-04T03:22:10-00:00 2005 Nucleic Acids Res 33 Web Server issue W226 W229 analysis article-nar article-predikin biology chain computational internet kinase markov phosphoprotein phosphorylation protein sequence software http://www.citeulike.org/user/neils/article/2054424 <i>Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D247-D251.</i><br /><br />Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://pfam.cgb.ki.se/). Pfam: clans, web tools and services. Robert Finn Jaina Mistry Benjamin Böckler Sam Jones Volker Hollich Timo Lassmann Simon Moxon Mhairi Marshall Ajay Khanna Richard Durbin Sean Eddy Erik Sonnhammer Alex Bateman Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D247-D251. 2007-12-04T03:22:09-00:00 2006 Nucleic Acids Res 34 Database issue D247 D251 alignment article-predikin chain computer database graphics interface internet markov protein sequence software structure tertiary user-computer http://www.citeulike.org/user/neils/article/2054418 <i>Nucleic Acids Res, Vol. 34, No. Web Server issue. (Jul 2006), pp. W362-W365.</i><br /><br />ScanProsite--http://www.expasy.org/tools/scanprosite/--is a new and improved version of the web-based tool for detecting PROSITE signature matches in protein sequences. For a number of PROSITE profiles, the tool now makes use of ProRules--context-dependent annotation templates--to detect functional and structural intra-domain residues. The detection of those features enhances the power of function prediction based on profiles. Both user-defined sequences and sequences from the UniProt Knowledgebase can be matched against custom patterns, or against PROSITE signatures. To improve response times, matches of sequences from UniProtKB against PROSITE signatures are now retrieved from a pre-computed match database. Several output modes are available including simple text views and a rich mode providing an interactive match and feature viewer with a graphical representation of results. ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Edouard de Castro Christian Sigrist Alexandre Gattiker Virginie Bulliard Petra Elisabeth Gasteiger Amos Bairoch Nicolas Hulo Nucleic Acids Res, Vol. 34, No. Web Server issue. (Jul 2006), pp. W362-W365. 2007-12-04T03:22:09-00:00 2006 Nucleic Acids Res 34 Web Server issue W362 W365 amino-acid analysis article-predikin database homology interface internet protein sequence software structure tertiary user-computer http://www.citeulike.org/user/neils/article/2054417 <i>Bioinformatics, Vol. 18, No. 9. (Sep 2002), pp. 1274-1275.</i><br /><br />SUMMARY: The Kinase Sequence Database (KSD) located at http://kinase.ucsf.edu/ksd contains information on 290 protein kinase families derived by profile-based clustering of the non-redundant list of sequences obtained from a GenBank-wide search. Included in the database are a total of 5,041 protein kinases from over 100 organisms. Clustering into families is based on the extent of homology within the kinase catalytic domain (250-300 residues in length). Alignments of the families are viewed by interactive Excel-based sequence spreadsheets. In addition, KSD features evolutionary trees derived for each family and detailed information on each sequence as well as links to the corresponding GenBank entries. Sequence manipulation tools, such as evolutionary tree generation, novel sequence assignment, and statistical analysis, are also provided. AVAILABILITY: The kinase sequence database is a web-based service accessible at http://kinase.ucsf.edu/ksd CONTACT: buzko@cmp.ucsf.edu; shokat@cmp.ucsf.edu/ksd A kinase sequence database: sequence alignments and family assignment. Oleksandr Buzko Kevan Shokat Bioinformatics, Vol. 18, No. 9. (Sep 2002), pp. 1274-1275. 2007-12-04T03:22:09-00:00 2002 Bioinformatics 18 9 1274 1275 alignment analysis article-predikin cluster database homology information internet kinase management protein retrieval sequence storage system http://www.citeulike.org/user/neils/article/2053695 <i>Nucleic Acids Res, Vol. 35, No. Database issue. (Jan 2007), pp. D599-D603.</i><br /><br />Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource 'AgBase' (www.agbase.msstate.edu). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The 'GO Consortium' gene association file contains the most rigorous GO annotations based solely on experimental data. The 'Community' gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations. AgBase: a unified resource for functional analysis in agriculture. Fiona Mccarthy Susan Bridges Nan Wang Bryce Magee Paul Williams Dawn Luthe Shane Burgess Nucleic Acids Res, Vol. 35, No. Database issue. (Jan 2007), pp. D599-D603. 2007-12-04T01:53:36-00:00 2007 Nucleic Acids Res 35 Database issue D599 D603 agriculture animal article-pka-pkg crops database domestic genetics genomics integration interface internet protein proteomics system user-computer http://www.citeulike.org/user/neils/article/2053691 <i>Nucleic Acids Res, Vol. 32, No. 3. (2004), pp. 1037-1049.</i><br /><br />Reversible protein phosphorylation provides a major regulatory mechanism in eukaryotic cells. Due to the high variability of amino acid residues flanking a relatively limited number of experimentally identified phosphorylation sites, reliable prediction of such sites still remains an important issue. Here we report the development of a new web-based tool for the prediction of protein phosphorylation sites, DISPHOS (DISorder-enhanced PHOSphorylation predictor, http://www.ist.temple. edu/DISPHOS). We observed that amino acid compositions, sequence complexity, hydrophobicity, charge and other sequence attributes of regions adjacent to phosphorylation sites are very similar to those of intrinsically disordered protein regions. Thus, DISPHOS uses position-specific amino acid frequencies and disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites. Based on the estimates of phosphorylation rates in various protein categories, the outputs of DISPHOS are adjusted in order to reduce the total number of misclassified residues. When tested on an equal number of phosphorylated and non-phosphorylated residues, the accuracy of DISPHOS reaches 76\% for serine, 81\% for threonine and 83\% for tyrosine. The significant enrichment in disorder-promoting residues surrounding phosphorylation sites together with the results obtained by applying DISPHOS to various protein functional classes and proteomes, provide strong support for the hypothesis that protein phosphorylation predominantly occurs within intrinsically disordered protein regions. The importance of intrinsic disorder for protein phosphorylation. Lilia Iakoucheva Predrag Radivojac Celeste Brown Timothy O'Connor Jason Sikes Zoran Obradovic Keith Dunker Nucleic Acids Res, Vol. 32, No. 3. (2004), pp. 1037-1049. 2007-12-04T01:53:36-00:00 2004 Nucleic Acids Res 32 3 1037 1049 amino-acid analysis animal article-pka-pkg database internet of phosphorylation protein proteome reproducibility results sequence http://www.citeulike.org/user/neils/article/2053690 <i>Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D322-D326.</i><br /><br />The Gene Ontology (GO) project (http://www.geneontology.org) develops and uses a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://song.sourceforge.net/). The GO Consortium continues to improve to the vocabulary content, reflecting the impact of several novel mechanisms of incorporating community input. A growing number of model organism databases and genome annotation groups contribute annotation sets using GO terms to GO's public repository. Updates to the AmiGO browser have improved access to contributed genome annotations. As the GO project continues to grow, the use of the GO vocabularies is becoming more varied as well as more widespread. The GO project provides an ontological annotation system that enables biologists to infer knowledge from large amounts of data. The Gene Ontology (GO) project in 2006. gene Consortium Nucleic Acids Res, Vol. 34, No. Database issue. (Jan 2006), pp. D322-D326. 2007-12-04T01:53:36-00:00 2006 Nucleic Acids Res 34 Database issue D322 D326 analysis article-pka-pkg control controlled database expression gene genetics interface internet management quality sequence software system user-computer vocabulary