ChIP-seq analysis notes from Ming Tang

The official code implementation for Chromoformer in PyTorch. (Lee et al., Nature Communications. 2022)

🧬 🦀 A fast and efficient tool to perform a genome wide Single cell Chromatin State Analysis using multimodal histone modification data.

Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility

A unified framework for discovering, analyzing, integrating, and visualizing regulatory motifs and transcription factor binding sites across bulk, single-cell, and long-read sequencing modalities.

geneSpark is a bioinformatics software program written in Python and Apache Spark for big data epigenetic histone modification ChIP-seq analysis.

This is a R package that intends to perform all the features possible by tensor decomposition based unsupervised feature extraction

Cell type-specific Histone Acetylation Score

Using CNNs to model affect of histone modification on gene expression

Processing, benchmarking and analysis of CUT&Tag against ENCODE ChIP-seq.

geneXtendeR analysis on 198 human histone modification ChIP-seq ENCODE datasets

Practical and home works in the discipline Bioinformatics.

This is the fork of the Bioconductor-mirror repository. Package Homepage: http://bioconductor.org/packages/devel/bioc/html/chipenrich.html Bug Reports: https://support.bioconductor.org/p/new/post/?tag_val=chipenrich.

Usage analysis of histone post-translational modifications using msqrob2PTM

The abstract and poster for my internship under the Stanford Institutes of Medical Research at the Khatri Lab in the summer of 2019. More code/scripts can be made available upon request (I used primarily R and SQL)

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

my master's research in bioinformatics

ChIP-seq normalization according to background reads density. (R package)

Gene expression prediction with Convolutional Recurrent Neural Networks

Independent project I undertook to perform a full ChIP-Seq analysis of the transcription factor Nanog in Zebrafish embryos.