Biology subjects

Houdjedj, A.

Publications and source records attributed to Houdjedj, A..

4 recordsLinked to original sources

bioRxiv2021

PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers

MotivationA major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. ResultsWe propose a novel method that integrates genomic data, biological pathways, and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted pairwise pathway coverage scores across all the patients, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against three state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell-line data. Additionally, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information towards the development of personalized treatments and therapies. AvailabilityAll the code and necessary datasets are available at https://github.com/abu-compbio/PersonaDrive. Contactcesim.erten@antalya.edu.tr or hilal.kazan@antalya.edu.tr

bioinformatics↗

bioRxiv2021

A Network-centric Framework for theEvaluation of Mutual Exclusivity Tests onCancer Drivers

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

bioinformatics↗

bioRxiv2020

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

BackgroundRecent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results: We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions: Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

bioinformatics↗

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↗