MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.
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.
Matthew
M
Mort
Timothy
T
Sterne-Weiler
Biao
B
Li
Edward V
EV
Ball
David N
DN
Cooper
Predrag
P
Radivojac
Jeremy R
JR
Sanford
Sean D
SD
Mooney
D017398:Alternative Splicing / Q000235:genetics*; D005091:Exons; D016147:Genes, Tumor Suppressor; D014644:Genetic Variation; D006801:Humans; D007438:Introns; D000069550:Machine Learning; D009154:Mutation; D020125:Mutation, Missense; D009369:Neoplasms / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D012322:RNA Precursors / Q000235:genetics; D022821:RNA Splice Sites / Q000235:genetics; D037941:Silencer Elements, Transcriptional / Q000235:genetics
D012322:RNA Precursors; D022821:RNA Splice Sites