DECRES

DECRES (DEep learning for identifying Cis-Regulatory ElementS) is a supervised deep learning model to identify enhancer and promoter regions in the human genome. The training data includes ENCODE (the Encyclopedia of DNA Elements) and FANTOM (the Functional Annotation of the Mammalian Genome) project data.

Topic

Enhancers;Transcriptional regulatory sites;Promoters

Detail

  • Operation: Annotation

  • Software interface: Library;Script

  • Language: Python (Theano)

  • License: Other

  • Cost: Free

  • Version name: -

  • Credit: The Genome Canada-Genome BC, Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), the National Institutes of Health (USA), the Michael Smith Foundation for Health Research (MSFHR), the Canada Foundation for Innovation and the BC Knowledge Development Fund.

  • Input: -

  • Output: -

  • Contact: Wyeth W Wasserman wyeth@cmmt.ubc.ca, Yifeng Li yifeng@cmmt.ubc.ca, Yifeng Li yifeng.li.cn@gmail.com

  • Collection: -

  • Maturity: Stable

Publications

  • Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.
  • Li Y, Shi W, Wasserman WW. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods. BMC Bioinformatics. 2018 May 31;19(1):202. doi: 10.1186/s12859-018-2187-1. PMID: 29855387; PMCID: PMC5984344.
  • https://doi.org/10.1186/s12859-018-2187-1
  • PMID: 29855387
  • PMC: PMC5984344

Download and documentation

< Back to DB search