ZeitlingerLab

(9/10) Our BPReveal package provides tools to engineer sequences with desired properties. For example, we designed mutations to alter a nucleosome’s presence in vivo, and our design was corroborated experimentally.

(8/10) These models detect barrier elements in nucleosome occupancy and chromatin organization. Around barriers, motif effects are asymmetric, and the most asymmetric regions align with known domain boundaries.

(7/10) Overcoming enzymatic sequence biases using PISA reveals the nucleosome motif grammar. Upon correcting the AT-rich sequences of a BPNet model trained to predict MNase-seq nucleosome occupancy, resulting attribution scores clearly identify motifs.

(6/10) PISA can detect enzymatic biases in various sequencing data. Sequence-to-function models learn both enzymatic bias of the sequencing experiment and the underlying biology. Here, a BPNet MNase-seq model trained in yeast exhibits expected preference for AT-rich sequences.

(5/10) PISA reveals complex motif effects on histone modification ChIP-seq data. In a H3K27ac BPNet model predicting activity in early embryo fly, a pioneering Zelda motif produces a dual response in H3K27ac profile: a central depletion, flanked by an increase in activity.

(4/10) PISA can reveal “hidden” motifs that possess disparate effects on the output window. In a ChromBPNet model predicting accessibility in early embryo fly, a CA-rich “Cackle” motif is revealed to possess a positive and negative contributions depending on the output locus.

(3/10) PISA can visualize the influence range of sequences, including TF motifs. In a mESC BPNet model predicting Nanog binding, PISA can distinguish the nucleosome-range effects of pioneering motif Oct4-Sox2 versus the binding-range effects of the Nanog motif itself.

(2/10) Our new interpretation tool called PISA (pairwise influence by sequence attribution) overcomes this limitation and quantifies how each individual base impacts the predicted readout at each genomic coordinate.

(1/10) When interpreting sequence-to-function models, current attribution methods typically summarize an input base’s effect on the entire output. But what happens if a single nucleotide causes one effect at one output position, but a different effect elsewhere?

(7/7) Our Model: All promoters use TFIID to load TBP, but TATA promoters additionally allow direct TBP binding to the TATA box. Such dual initiation likely enables faster TBP re-loading and larger transcriptional bursts at TATA promoters. For more details, check out our work!

(6/7) DPR promoters, which contain downstream sequences favorable for TFIID binding, show the highest levels of downstream TBP. Downstream TBP shows the strongest correlation with TAF2, TAF1 and TAF7, consistent with this being the promoter loading state of TFIID.