In silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrences.

ABSTRACT: Enhancers interact with gene promoters and form chromatin looping structures that serve important functions in various biological processes, such as the regulation of gene transcription and cell differentiation. However, enhancers are difficult to identify because they generally do not have fixed positions or consensus sequence features, and biological experiments for enhancer identification are costly in terms of labor and expense. In this work, several models were built by using various sequence-based feature sets and their combinations for enhancer prediction. The selected features derived from a recursive feature elimination method showed that the model using a combination of 141 transcription factor binding motif occurrences from 1,422 transcription factor position weight matrices ach

SUBMITTER: Fang Y 

PROVIDER: S-EPMC5007594 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature