chromatin-accessibiity

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

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)

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

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

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

See the main fork of this repository here >>>

Single Molecule Footprinting Analysis in Python

A method for predicting chromatin features and prioritizing non-coding rice variants using DNA language models.

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

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