|Title||Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Park, CY, Krishnan, A, Zhu, Q, Wong, AK, Lee, Y-suk, Troyanskaya, OG|
|Date Published||2014 Nov 26|
MOTIVATION: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses.
RESULTS: We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry). Availability and implementation: An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/lib sleipnir.bitbucket.org.
CONTACT: email@example.com Supplementary information: Supplementary data are available at Bioinformatics online.