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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.

SUBMITTER: Yuan H

PROVIDER: S-EPMC6717532 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Yuan Han H Kshirsagar Meghana M Zamparo Lee L Lu Yuheng Y Leslie Christina S CS

Nature methods 20190812 9

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. ...[more]

PMID: 31406384

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