The multi-lineage transcription factor ISL1 controls cardiomyocyte cell fate through interaction with NKX2.5.

Congenital heart disease often arises from perturbations of transcription factors (TFs) that guide cardiac development. ISLET1 (ISL1) is a TF that influences early cardiac cell fate, as well as differentiation of other cell types including motor neuron progenitors (MNPs) and pancreatic islet cells. While lineage specificity of ISL1 function is likely achieved through combinatorial interactions, its essential cardiac interacting partners are unknown.

Chromatin accessibility in the Drosophila embryo is determined by transcription factor pioneering and enhancer activation.

Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding and chromatin accessibility data with interpretable deep learning and performed genetic validation experiments.

distillation of thermodynamic affinity from deep learning regulatory sequence models of protein-DNA binding.

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation.

Base-resolution models of transcription-factor binding reveal soft motif syntax.

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions.

A heterodyne interferometer for angle metrology.

We have developed a compact, high-resolution, angle measurement instrument based on a heterodyne interferometer. Common-path heterodyne interferometer metrology is used to measure displacements of a reflective target surface. In the interferometer set up, an optical mask is used to sample the laser beam reflecting back from four areas on a target surface.