Mort Matthew, Sterne-Weiler Timothy, Li Biao, Ball Edward V, Cooper David N, Radivojac Predrag, Sanford Jeremy R, Mooney Sean D
Genome Biol. 2014 Jan 13;15(1):R19. doi: 10.1186/gb-2014-15-1-r19.
We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.
我们开发了一种新型的机器学习方法MutPred Splice，用于识别破坏前体mRNA剪接的编码区替代。将MutPred Splice应用于人类致病外显子突变表明，导致遗传性疾病的突变中有16%以及癌症中的体细胞突变中有10%至14%可能破坏前体mRNA剪接。对于遗传性疾病，导致剪接缺陷的主要机制是剪接位点缺失，而对于癌症，剪接破坏的主要机制预计是通过外显子剪接增强子的缺失或外显子剪接沉默子元件的获得导致外显子跳跃。可在http://mutdb.org/mutpredsplice获取MutPred Splice。
