PINTA: a web server for network-based gene prioritization from expression data

Abstract

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user.

On Different Aspects of Network Analysis in Systems Biology

“…to improve the generation of gene co-expression networks by analysing the scale-free property for tentative networks [81]. By contrast, local topological network characteristics have already been exploited by a wide variety of new data mining approaches recently, including methods to identify dense communities [82] of nodes [83,84], methods to compare mapped gene and protein sets in terms of their network topological properties [85], and approaches to score distances between nodes for prioritizing disease genes [86]. Interestingly, recent studies have shown that cancer-associated genes tend to have outstanding topological characteristics [87], even when accounting for study-specific biases, and that topological information can facilitate cancer classification [88].…”

Disease Gene Prioritization Using Network and Feature

“…We validated our approach with independent benchmark studies, which revealed an AUC value of 0.86 and a 22% error reduction rate compared with previous tools, including Endeavour (Tranchevent et al, 2008) and PINTA (Nitsch et al, 2011). Finally, we applied our method to a case study for the identification of genetic factors contributing to autism and intellectual disability, and predicted novel promising candidate genes for these phenotypes.…”

“…Suspects, Toppgene, and Endeavour compare candidate genes with the extracted representative features by fuzzy similarity measurement (Chen et al, 2009), Pearl correlation (Aerts et al, 2006), or kernels (Aerts et al, 2009) to produce similarity scores for candidate genes that can be combined through order statistics or kernel-based methods (Aerts et al, 2009). The other tools, including ToppNet (Chen et al, 2009) and PINTA (Nitsch et al, 2011), rather than relying on high-dimensional features instead utilize networks and prioritize candidate genes by 314 XIE ET AL.…”

“…Since there was no prior distribution of disease genes, the prior probability was set to 0.5. Next, we compared our results with Endeavour (Tranchevent et al, 2008) and PINTA (Nitsch et al, 2011) from the previous benchmark study (Börnigen et al, 2011). Here, Endeavour used 14 distinct data sources, including annotation (GeneOntology, Swissprot, Interpro, Kegg, EnsemblEst); Interaction (Bind, String); Expression (SonEtAl, SuEtAl); Precalculated (Ouzounis, Prospectr); Motif; Blast; and Text mining, while PINTA only used network information from String version 8.2.…”

Toxicogenomics Investigation Under the eTOX Project

“…For example, a tool like GenMAPP integrates several biological pathways relevant for rat and human toxicicty [19]. PINTA is another web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein-protein interaction network [20]. Finally, a Predictive Power Estimation Algorithm (PPEA) has been developed to facilitate genomic biomarker discovery for predictive toxicity and drug responses [21].…”