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-and02621nas a2200325   4500008004100000022001400041245012100055210006900176260001600245300000900261490000700270520154400277653002101821653001901842653002901861653002001890653003401910653001301944653001101957653003201968100002402000700002002024700001902044700001902063700002402082700001902106700002002125700002002145856013002165       2012                        eng d  a1362-496200aDiscovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.0 aDiscovering the hidden subnetwork component in a ranked list of   c2012 Nov 01  ae1580 v403 a<p>Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.</p>10aBipolar Disorder10aFanconi Anemia10aGene Regulatory Networks10aGenes, Neoplasm10aGenome-Wide Association Study10aGenomics10aHumans10aProtein Interaction Mapping1 aGarcía-Alonso, Luz1 aAlonso, Roberto1 aVidal, Enrique1 aAmadoz, Alicia1 aDe Maria, Alejandro1 aMinguez, Pablo1 aMedina, Ignacio1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/discovering-hidden-sub-network-component-ranked-list-genes-or-proteins-derived-genomic01737nas 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-0