BindSpace decodes transcription factor binding signals by large-scale sequence embedding
Nature methods, 2019•nature.com
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.
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.
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.
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