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Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR
Authors:Sigve Nakken  Sveinung Gundersen  Fabian L M Bernal  Dimitris Polychronopoulos  Eivind Hovig  Jørgen Wesche
Affiliation:1. Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway;2. Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway;3. University Center for Information Technology, University of Oslo, Oslo, Norway;4. Ochre Bio Ltd, Oxford, UK;5. Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway;6. Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

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 service 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 ).
Keywords:cancer relevance  gene set analysis  genome-scale screening  hit prioritization  target discovery
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