DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Journal: Bioinformatics (Oxford, England)
PMID: 37294799

Abstract

MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. The methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs.

Authors

  • Tianjiao Zhang
    College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
  • Liangyu Li
    College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
  • Hailong Sun
    College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
  • Guohua Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

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