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Predicting enhancers in mammalian genomes using supervised hidden Markov models.
Zehnder, Tobias; Benner, Philipp; Vingron, Martin.
Afiliação
  • Zehnder T; Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany.
  • Benner P; Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany.
  • Vingron M; Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin, 14195, Germany.
BMC Bioinformatics ; 20(1): 157, 2019 Mar 27.
Article em En | MEDLINE | ID: mdl-30917778
BACKGROUND: Eukaryotic gene regulation is a complex process comprising the dynamic interaction of enhancers and promoters in order to activate gene expression. In recent years, research in regulatory genomics has contributed to a better understanding of the characteristics of promoter elements and for most sequenced model organism genomes there exist comprehensive and reliable promoter annotations. For enhancers, however, a reliable description of their characteristics and location has so far proven to be elusive. With the development of high-throughput methods such as ChIP-seq, large amounts of data about epigenetic conditions have become available, and many existing methods use the information on chromatin accessibility or histone modifications to train classifiers in order to segment the genome into functional groups such as enhancers and promoters. However, these methods often do not consider prior biological knowledge about enhancers such as their diverse lengths or molecular structure. RESULTS: We developed enhancer HMM (eHMM), a supervised hidden Markov model designed to learn the molecular structure of promoters and enhancers. Both consist of a central stretch of accessible DNA flanked by nucleosomes with distinct histone modification patterns. We evaluated the performance of eHMM within and across cell types and developmental stages and found that eHMM successfully predicts enhancers with high precision and recall comparable to state-of-the-art methods, and consistently outperforms those in terms of accuracy and resolution. CONCLUSIONS: eHMM predicts active enhancers based on data from chromatin accessibility assays and a minimal set of histone modification ChIP-seq experiments. In comparison to other 'black box' methods its parameters are easy to interpret. eHMM can be used as a stand-alone tool for enhancer prediction without the need for additional training or a tuning of parameters. The high spatial precision of enhancer predictions gives valuable targets for potential knockout experiments or downstream analyses such as motif search.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Elementos Facilitadores Genéticos / Genoma / Genômica / Mamíferos Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Elementos Facilitadores Genéticos / Genoma / Genômica / Mamíferos Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Alemanha