Predicting the functional consequences of cancer-associated amino acid substitutions.
Bioinformatics
; 29(12): 1504-10, 2013 Jun 15.
Article
em En
| MEDLINE
| ID: mdl-23620363
ABSTRACT
MOTIVATION The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations. RESULTS:
The performance of our algorithm was evaluated using two separate benchmarks. In our analysis, we observed improved performances when distinguishing between driver mutations and other germ line variants (both disease-causing and putatively neutral mutations). In addition, when discriminating between somatic driver and passenger mutations, we observed performances comparable with the leading computational prediction algorithms SPF-Cancer and TransFIC. AVAILABILITY AND IMPLEMENTATION A web-based implementation of our cancer-specific model, including a downloadable stand-alone package, is available at http//fathmm.biocompute.org.uk.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise Mutacional de DNA
/
Substituição de Aminoácidos
/
Neoplasias
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Reino Unido