BindSpace: decoding transcription factor binding signals by large-scale joint embedding

Abstract

Decoding 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 class/family labels into the same space. By training on binding data for hundreds of TFs and embedding over 1M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish signals of closely related TFs. MainDirect measurement of genome-wide transcription factor (TF) occupancy for all expressed factors in a cell type of interest is practically infeasible outside of large consortium projects. Therefore, computational prediction of TF binding to cognate sites at relevant loci -e.g. chromatin accessible regions or putative enhancers defined by active histone marks -is of critical importance. Massive efforts to define the intrinsic binding affinities of TFs by protein binding microarray (PBMs) 1 , cognate site identification (CSI) 2 , genomic-context PBM (gc-PBM) 3 , mechanically induced trapping of molecular interactions (MITOMI) 4 and high-throughput SELEX followed by sequencing (HT-SELEX) 5 provide large-scale data sets for training binding models. However, these in vitro binding experiments are typically summarized as position-specific weight matrix (PWM) motifs, losing both specificity and sensitivity and leading to nearidentical motifs for closely related TFs. Supervised learning methods have improved accuracy of discrimination between bound and unbound sequences of individual TFs 6-9 but have not addressed the multiclass nature of the problem and therefore are not optimized to distinguish between TFs with similar binding signals.Here we present a novel multiclass and multilabel method to jointly learn binding preferences of hundreds of assayed TFs by embedding their bound/unbound DNA sequences and class labels into a common space. Our method, called BindSpace, learns accurate binding models for individual TFs while enabling discrimination between different TFs in the same family. To train BindSpace, we combined HT-SELEX in vitro binding experiments for 461 mouse and human TFs from previous large-scale studies 5,8 . After applying rigorous quality control (Methods), we used 270 experiments for 243 transcription factors for our training set. The top 2000 enriched probes from each of these experiments were used as positive examples, yielding over 500K positive training sequences. We randomly sampled universal negatives from initial HT-SELEX probe libraries as well as non-accessible genomic regions to obtain ~500K negative training sequences (Methods). Each sequence is represented as a bag of 8-mers, each containing up to two consecutive wild cards, and each bag is associated either with both a TF label (e.g. HOXA2) and a TF family label (e.g. Homeodomain) or with a universal negative label. In this study, we used two thirds of the HT-SELEX data for training and one third for testing, and we performed 5-fold cross validation on the training data...

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