02330nas a2200265   4500008004100000022001400041245012000055210006900175260001300244300001300257490000700270520140800277653002301685653001301708653003201721653004301753100001901796700001901815700001601834700002401850700002001874700002401894700001501918856013101933       2015                        eng d  a1362-496200aPTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.0 aPTMcode v2 a resource for functional associations of posttransla  c2015 Jan  aD494-5020 v433 a<p>The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.</p>10aDatabases, Protein10aInternet10aProtein Interaction Mapping10aProtein Processing, Post-Translational1 aMinguez, Pablo1 aLetunic, Ivica1 aParca, Luca1 aGarcía-Alonso, Luz1 aDopazo, Joaquin1 aHuerta-Cepas, Jaime1 aBork, Peer  uhttps://www.clinbioinfosspa.es/content/ptmcode-v2-resource-functional-associations-post-translational-modifications-within-and01737nas a2200277   4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520083000253653002201083653003701105653002301142653001101165653001301176653003201189653001301221100001901234700001801253700002001271700002501291700002001316856012301336       2009                        eng d  a1362-496200aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.0 aSNOW a webbased tool for the statistical analysis of proteinprot  c2009 Jul  aW109-140 v373 a<p>Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.</p>10aComputer Graphics10aData Interpretation, Statistical10aDatabases, Protein10aHumans10aInternet10aProtein Interaction Mapping10aSoftware1 aMinguez, Pablo1 aGötz, Stefan1 aMontaner, David1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/snow-web-based-tool-statistical-analysis-protein-protein-interaction-networks-001723nas a2200277   4500008004100000022001400041245009000055210006900145260001600214300001100230490000700241520077800248653002801026653002301054653003001077653003801107653003201145653001301177653002001190653002401210100001901234700002501253700002001278700002001298856012701318       2007                        eng d  a1367-481100aFunctional profiling of microarray experiments using text-mining derived bioentities.0 aFunctional profiling of microarray experiments using textmining   c2007 Nov 15  a3098-90 v233 a<p><b>MOTIVATION: </b>The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.</p>10aArtificial Intelligence10aDatabases, Protein10aGene Expression Profiling10aInformation Storage and Retrieval10aNatural Language Processing10aProteins10aResearch Design10aSystems Integration1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/functional-profiling-microarray-experiments-using-text-mining-derived-bioentities-0