ATAC-seq peak-calling and QC analysis pipeline

A comprehensive tool for processing, analyzing and visulizing single cell chromatin accessibility sequencing data

Code for the paper "Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts"

PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data

Big data Regression for predicting DNase I hypersensitivity

A python package for chromVAR

scNOVA : Single-Cell Nucleosome Occupancy and genetic Variation Analysis

A de novo prediction tool of chromatin accessible regions for plant genomes

Robust Open Chromatin Detection via Convex Optimization: Multisample Consensus Peak Calling

PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data

An R package designed to integrate and visualize various levels of epigenomic information, including but not limited to: ChIP, Histone, ATAC, and RNA sequencing. epiRomics is also designed to identify enhancer and enhanceosome regions from these data.

Analysis of Single Molecule Footprinting (SMF) data for the analysis of DNA methylation, chromatin accessibility and TF binding. The repository contains all primary code to reproduce the main analyses for the publication "Single molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation" (Kreibich et al., 2023)

See the main fork of this repository here >>>

A robust statistical test for TF footprint data analyses

Snakemake pipeline for analysis and normalization of ATAC-seq data starting from fastq.gz files.

A two-step model that combines neural network and ensemble learning to predict OCR–mediated interactions.

Comprehensive analysis pipeline for ATAC-seq data including QC, alignment, peak calling, annotation, motif analysis, and gene enrichment. Delivers key insights and reproducible results for chromatin accessibility studies.

Single Molecule Footprinting Analysis in Python

LIONHEART is a method for detecting cancer from whole genome sequenced plasma cell-free DNA.

A crash course in scATAC-seq data processing