MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.
Genome Biol
; 15(1): R19, 2014 Jan 13.
Article
em En
| MEDLINE
| ID: mdl-24451234
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
Base de dados:
MEDLINE
Assunto principal:
Variação Genética
/
Éxons
/
Processamento Alternativo
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Genome Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2014
Tipo de documento:
Article