ATAC-seq peak-calling and QC analysis pipeline

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

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

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

A python package for chromVAR

Big data Regression for predicting DNase I hypersensitivity

LIONHEART detects cancer from whole genome sequenced plasma cell-free DNA.

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

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)

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.

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

Complete ATAC-seq analysis pipeline for beginners. From FASTQ to publication-ready peaks with comprehensive documentation.

See the main fork of this repository here >>>

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

A robust statistical test for TF footprint data analyses

A wavelet-based linear programming method using L1-minimal reconstruction loss for accessible chromatin data deconvolution

Single Molecule Footprinting Analysis in Python

In this module, you will learn how to perform the bioinformatics analysis of differential chromatin occupancy using data generated by three well-known high-throughput sequencing assaying, including differences between them and integration of chromatin accessibility and differential gene expression