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arXiv · 2107.13247

OncoEnrichR: cancer-dedicated gene set interpretation

Sigve Nakken·Sveinung Gundersen·Fabian L. M. Bernal·Dimitris Polychronopoulos·Eivind Hovig·Jørgen Wesche
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Abstract

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations, and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based workflow hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR).

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Sigve Nakken, Sveinung Gundersen, Fabian L. M. Bernal, Dimitris Polychronopoulos, Eivind Hovig, Jørgen Wesche. 2022-09-27. OncoEnrichR: cancer-dedicated gene set interpretation. https://arxiv.org/abs/2107.13247

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