Computational identification of insertional mutagenesis targets for cancer gene discovery.

Computational identification of insertional mutagenesis targets for cancer gene discovery.
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DOI:
10.1093/nar/gkr447
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发表时间:
2011-08
影响因子:
14.9
通讯作者:
Wessels LF
Wessels LF
中科院分区:
生物学2区
文献类型:
--
作者:
de Jong J;de Ridder J;van der Weyden L;Sun N;van Uitert M;Berns A;van Lohuizen M;Jonkers J;Adams DJ;Wessels LF

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插入诱变是一种有效的正向遗传筛选技术,用于识别小鼠模型系统中的候选癌症基因。在这些筛选的分析中,一个重要但尚未解决的问题是受插入影响的基因的鉴定。为了解决这个问题,我们开发了基于内核卷积规则的映射(KC-RBM)。 KC-RBM 利用跨肿瘤的距离、方向和插入密度来自动将整合位点映射到目标基因。我们对逆转录病毒和转座子数据集中插入发生与预测目标的异常基因表达之间的关联进行了首次全基因组评估。我们通过展示 KC-RBM 在从一系列独立、手动管理的癌症基因中恢复真阳性方面优于现有方法的卓越性能来证明 KC-RBM 的效率。这项工作的结果将显着提高正向遗传筛选中癌症基因发现的准确性和速度。 KC-RBM 可作为 R 封装提供。
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.
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