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

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.