Predicting Tissue Specific Enhancer Activity from Epigenetic Marks and Sequence
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Updated
Dec 3, 2014 - Python
Predicting Tissue Specific Enhancer Activity from Epigenetic Marks and Sequence
This repo contains python scripts used in Mack et al. 2022 (doi.org/10.1016/j.jmb.2022.167482). This includes MD simulations code and analysis scripts. The usage of these Python scripts is further explained in the enclosed README.pdf
A collection of simple scripts to facilitate the setup of everyday epigenetics experiments and to handle genomic methylation data
Snakemake workflow for processing small RNA-seq libaries
CutNtag pipeline for Pasini's lab written in snakemake
Python code to control my physical model of an Epigenetic Landscape.
ChIP-seq pipeline specific for the Zwart lab
generates reference matrix of average beta methylation values
Population analysis of DNA methylation
This workflow uses Dorado, Samtools, Clair3, WhatHap and Modkit to extract a modification count table containing information for each relevant site.
Combinatorial and Semantic Analysis of Functional Elements
Ultralight and fast implementation of GoPeaks to call peaks in CUT&Tag/CUT&RUN data
Produces coverage and methylation percentage data from FASTQ files.
Analysis and retrieval of Regulatory EleMents (REMs) linked to genes
Resource for summarizing and tracking new array samples published to NCBI's Gene Expression Omnibus (GEO) database.
A Python API library for exploration and data retrieval from NCBI
A SnakeMake workflow to analyse whole genome bisulfite sequencing data from allopolyploids.
Pipeline for BS-Seq data based on snakemake
Toolkit for concretely describing non-canonical DNA, RNA, and proteins
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