Extended Data Fig. 4: BPNet training on ChIP-nexus profiles is faster and yields more accurate motif instances than a binary classification model.

a, Predictive performance as measured by the precision-recall curve of the binary classification models predicting the presence or absence of ChIP-nexus peaks from 1 kb DNA sequences evaluated across the held-out (tuning/validation) chromosomes 2, 3 and 4. The model trained to classify the sequences is outperformed when the model is trained to also predict the ChIP-nexus profiles from DNA sequence (without or without profile bias-correction) in addition to classifying them is shown in blue (without or without profile bias-correction) in light blue and with bias-correction in dark blue). b, Training time of the binary classification model trained genome-wide and the sequence-to-profile model (BPNet) trained in ChIP-nexus peaks. c, Detected motifs by TF-MoDISco using the contribution scores in ChIP-nexus peaks of the sequence-to-profile BPNet (profile reg.) or the binary classification model (binary class). A light color denotes a high number of seqlets for each motif. Motifs not discovered or motifs supported by less than 100 seqlets are shown in black. Questionable motifs are displayed separately on the right. d, The number of motif instances (500 bp within ChIP-nexus peak summit) showing a ChIP-nexus footprint (y-axis) within the top N motif instances with highest contribution scores (x-axis) from the held-out (test) chromosomes 1, 8 and 9. A site was considered to show a ChIP-nexus footprint if the number of reads at the position of the aggregate footprint summit (averaged across both strands) is higher than the 90^th percentile value of all motif instances detected by the profile regression model for the corresponding TF (that is same as in Extended Data Fig. 3b).