Promoter analysis and prediction in the human genome using sequence-based deep learning models.

Journal: Bioinformatics (Oxford, England)
PMID: 30601980

Abstract

MOTIVATION: Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences.

Authors

  • Ramzan Umarov
    Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), Computer, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
  • Hiroyuki Kuwahara
    Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
  • Victor Solovyev
    Department of Cell Biology, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.

Keywords

External Resources

Popular Topics
Recent Journals