Kazan, H. | bioRxiv Science

bioRxiv2019

RBPSponge: genome-wide identification of lncRNAs that sponge RBPs

SummaryLong noncoding RNAs (lncRNAs) can act as molecular sponges or decoys for an RNA-binding protein (RBP) through their RBP binding sites, thereby modulating the expression of all target genes of the corresponding RBP of interest. Here, we present a web tool named RBPSponge to explore lncRNAs based on their potential to act as a sponge for an RBP of interest. RBPSponge identifies the occurrences of RBP binding sites and CLIP peaks on lncRNAs, and enables users to run statistical analyses to investigate the regulatory network between lncRNAs, RBPs and targets of RBPs.\n\nAvailabilityThe web server is available at https://www.RBPSponge.com\n\nContacthilal.kazan@antalya.edu.tr

bioinformatics↗

bioRxiv2019

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

MotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. Contacthilal.kazan@antalya.edu.tr

bioinformatics↗