🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨

Elucidating the Utility of Genomic Elements with Neural Nets

CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.

surrogate quantitative interpretability for deepnets

Genomic sequence preprocessing toolkit

Data-driven design of context-specific regulatory elements

lsgkm+gkmexplain with regression functionality. Builds off kundajelab/lsgkm (which has gkmexplain), which in turn builds off Dongwon-Lee/lsgkm (the original lsgkm repo)

Interpreting sequence-to-function machine learning models

A set of tutorials for how to use all the tools in ML4GLand

Dual Threshold Optimization compares two ranked lists of features (e.g. genes) to determine the rank threshold for each list that minimizes the hypergeometric p-value of the overlap of features. It then calculates a permutation based empirical p-value and an FDR

Robust and efficient analysis of single-cell perturbation studies

A curated list of regulatory genomics papers and resources.

Threshold and p-value computations for Position Weight Matrices

squid repository for manuscript analysis

Prokaryote Gene Regulatory Network (ProGRN) Inference Pipeline

Deep learning model for non-coding regulatory variants

Repository documenting applications of the ML4GLand suite on published datasets

Datasets for benchmarking, testing and developing in EUGENe

Motif representation and analysis toolkit in Python

R package for plotting Islet Regulome data.