BindSpace decodes transcription factor binding signals by large-scale sequence embedding Journal Article

   


Authors: Yuan, H.; Kshirsagar, M.; Zamparo, L.; Lu, Y.; Leslie, C. S.
Article Title: BindSpace decodes transcription factor binding signals by large-scale sequence embedding
Abstract: The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data from hundreds of TFs and embedding over 1 M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish between signals of closely related TFs. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.
Journal Title: Nature Methods
Volume: 16
Issue: 9
ISSN: 1548-7091
Publisher: Nature Publishing Group  
Date Published: 2019-09-01
Start Page: 858
End Page: 861
Language: English
DOI: 10.1038/s41592-019-0511-y
PUBMED: 31406384
PROVIDER: scopus
PMCID: PMC6717532
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070823204&doi=10.1038%2fs41592-019-0511-y&partnerID=40&md5=5a1fff57cf2fb36c8cc0d5a5d2b6f943
Notes: Source: Scopus
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MSK Authors
  1. Christina Leslie
    188 Leslie
  2. Yuheng Lu
    17 Lu
  3. Han Yuan
    8 Yuan
  4. Lee Zamparo
    5 Zamparo
  5. Meghana Kshirsagar
    3 Kshirsagar
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