🧬 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)

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

Interpreting sequence-to-function machine learning models

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

squid repository for manuscript analysis

A curated list of regulatory genomics papers and resources.

Threshold and p-value computations for Position Weight Matrices

Deep learning model for non-coding regulatory variants

Deep Unfolded Convolutional Dictionary Learning for motif discovery.

Repository documenting applications of the ML4GLand suite on published datasets

Datasets for benchmarking, testing and developing in EUGENe

A Hugo-based deployment of biocomputeobject.org

R package for plotting Islet Regulome data.

Analyze the active regulatory region of DNA using FFNN and CNN