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Nucleic Acids Res. 2011 Aug;39(15):e105. doi: 10.1093/nar/gkr447. Epub 2011 Jun 7.

Computational identification of insertional mutagenesis targets for cancer gene discovery.

Author information

1
Bioinformatics and Statistics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.

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.

PMID:
21652642
PMCID:
PMC3159484
DOI:
10.1093/nar/gkr447
[Indexed for MEDLINE]
Free PMC Article

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