Portfolio

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A deep learning approach to predicting transcription initiation from sequence at single nucleotide resolution.

Fast calculation of approximately independent linkage disequilibrium blocks.

A small modification to bpnetlite’s BPNet to accomodate large validation datasets.

Preprocessing and deep learning-based analysis of the ENCODE PRO-cap atlas.

A PyTorch port of PromoterAI, a deep learning model predicting the regulatory impact of promoter variants.

A Python port of dREG (Detecting Regulatory Elements using GRO-seq and PRO-seq).

Python modules and scripts for analyzing high resolution chromatin contact data.