| Literature DB >> 32702417 |
David Armanious1, Jessica Schuster2, George A Tollefson3, Anthony Agudelo3, Andrew T DeWan4, Sorin Istrail5, James Padbury6, Alper Uzun7.
Abstract
We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.Entities:
Keywords: Data visualization; Multi-sample; Networks; Protein-protein interactions; Software
Year: 2020 PMID: 32702417 PMCID: PMC7749048 DOI: 10.1016/j.ygeno.2020.07.028
Source DB: PubMed Journal: Genomics ISSN: 0888-7543 Impact factor: 5.736