Codes for data processing and figure generation
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Updated
May 10, 2018 - Python
Codes for data processing and figure generation
A dataset for big data prediction.
Scripts to run footprinting and motif-flanking accessibility analysis in DNase-seq/ ATAC-seq data
A robust statistical test for TF footprint data analyses
This repository contains the prebuilt models for BIRD.
PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data
Improving the feature density based peak caller with dynamic statistics
Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility
🐛 How to use CENTIPEDE to determine if a transcription factor is bound.
Big data Regression for predicting DNase I hypersensitivity
deepStats: a stastitical toolbox for deeptools and genomic signals
PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data
Regulatory Genomics Toolbox: Python library and set of tools for the integrative analysis of high throughput regulatory genomics data.
chromatin Variability Across Regions (of the genome!)
ATAC-seq and DNase-seq processing pipeline
MACS -- Model-based Analysis of ChIP-Seq
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