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Computational identification of insertional mutagenesis targets for cancer gene discovery.

ABSTRACT: Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package.

SUBMITTER: de Jong J

PROVIDER: S-EPMC3159484 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Computational identification of insertional mutagenesis targets for cancer gene discovery.

de Jong Johann J de Ridder Jeroen J van der Weyden Louise L Sun Ning N van Uitert Miranda M Berns Anton A van Lohuizen Maarten M Jonkers Jos J Adams David J DJ Wessels Lodewyk F A LF

Nucleic acids research 20110607 15

Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genom ...[more]

PMID: 21652642

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Computational identification of insertional mutagenesis targets for cancer gene discovery.

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Computational identification of insertional mutagenesis targets for cancer gene discovery.

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