GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
Bioinformatics
; 32(4): 490-6, 2016 Feb 15.
Article
em En
| MEDLINE
| ID: mdl-26476779
ABSTRACT
MOTIVATION The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies. RESULTS:
We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis. AVAILABILITY AND IMPLEMENTATION The implementation of GERV and related data are available at http//gerv.csail.mit.edu/.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fatores de Transcrição
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Algoritmos
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Sequências Reguladoras de Ácido Nucleico
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Modelos Estatísticos
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Biologia Computacional
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Polimorfismo de Nucleotídeo Único
Tipo de estudo:
Evaluation_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2016
Tipo de documento:
Article