All code associated with manuscript detailing scATAC and scRNA-seq following stimulus of PBMCs

Python tool for converting files and office documents to Markdown.

systematic explanation of attribution-based mechanisms for regulatory genomics

Repository of USC's LA's BEST program. https://lasbest.usc.edu/

GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other databases and tools.

R boot camp: Scientific Computing

pycistarget is a python module to perform motif enrichment analysis in sets of regions with different tools and identify high confidence TF cistromes.

Coding related skill definitions for numerical computing, JAX, Equinox, general Rust and polars

Minimalist API for parsing genotype files into Numpy data structures, backed by Rust.

Transcription Factor Motif Instance Neighborhood Decomposition and Interpretation

A statistical framework for identifying desynchronized cell states and their driver genes/regulatory elements from paired single-cell RNA and ATAC data.

The official repository for the ARSENAL model

Fast, flexible and easy to use probabilistic modelling in Python.

Clustering motif models to remove redundancy

Code & documentation for Multiomics and deep learning dissect regulatory syntax in human development (Liu*, Jessa*, Kim*, Ng*, et al, Nature, 2026).

A framework to score and analyze variant effects genome-wide using ChromBPNet models

Biological sequence analysis for the modern age.

mfSuSiE implementation using susieR backbone

Camiel's scATAC-seq analysis pipeline

TF MOtif Discovery from Importance SCOres

Bias factorized, base-resolution deep learning models of chromatin accessibility (chromBPNet)

WASP: allele-specific pipeline for unbiased read mapping and molecular QTL discovery

The computational and statistical analyses of TenK10K phase 1 multiome data

Repo for analyses of the pilot phase of TenK10K

Gene regulatory network containing signed transcription factor-target gene interactions

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.