ChIP-seq analysis notes from Ming Tang
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Updated
Aug 5, 2024 - Python
ChIP-seq analysis notes from Ming Tang
MACS -- Model-based Analysis of ChIP-Seq
Automated and customizable preprocessing of Next-Generation Sequencing data, including full (sc)ATAC-seq, ChIP-seq, and (sc)RNA-seq workflows. Works equally easy with public as local data.
Publication quality NGS track plotting
Regulatory Genomics Toolbox: Python library and set of tools for the integrative analysis of high throughput regulatory genomics data.
A Snakemake workflow and MrBiomics module for performing genomic region set and gene set enrichment analyses using LOLA, GREAT, GSEApy, pycisTarget and RcisTarget.
(DEPRECATED) epic: diffuse domain ChIP-Seq caller based on SICER
Transcription Factor Binding Prediction from ATAC-seq and scATAC-seq with Deep Neural Networks
A Python package for fast operations on 1-dimensional genomic signal tracks
A robust model for quantitative comparison of ChIP-Seq data sets.
Pipelines for NGS data preprocessing by the Bock lab and friends
A toolkit for NGS analysis with Python
The ChIP-Seq peak calling algorithm using convolution neural networks
A Snakemake workflow and MrBiomics module to split, filter, normalize, integrate and select highly variable features of count matrices resulting from experiments with sequencing readout (e.g., RNA-seq, ATAC-seq, ChIP-seq, Methyl-seq, miRNA-seq,...) including diagnostic visualizations.
Pipeline to analyse ChIP-Rx data, i.e ChIP-Seq with reference exogenous genome spike-in normalization
To pre-process a set of ChIP-seq samples and coordinate with MAnorm2 for differential analysis
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