MACS -- Model-based Analysis of ChIP-Seq
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Updated
Feb 21, 2025 - Python
MACS -- Model-based Analysis of ChIP-Seq
Transcription factor Occupancy prediction By Investigation of ATAC-seq Signal
ATAC-seq and DNase-seq processing pipeline
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.
Regulatory Genomics Toolbox: Python library and set of tools for the integrative analysis of high throughput regulatory genomics data.
Ultimate ATAC-seq Data Processing, Quantification and Annotation Snakemake Workflow and MrBiomics Module.
A Snakemake workflow and MrBiomics module for performing genomic region set and gene set enrichment analyses using LOLA, GREAT, GSEApy, pycisTarget and RcisTarget.
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 toolkit for working with ATAC-seq data.
Pipelines for NGS data preprocessing by the Bock lab and friends
Co-accessibility network from single-cell ATAC-seq data. Python code, based on Cicero package (R).
A Snakemake workflow and MrBiomics module for easy visualization of genome browser tracks of aligned BAM files (e.g., RNA-seq, ATAC-seq, scRNA-seq, ...) powered by the wrapper gtracks for the package pyGenomeTracks, and IGV-reports.
A toolkit for NGS analysis with Python
The ChIP-Seq peak calling algorithm using convolution neural networks
Coupled clustering of single cell genomic data
Variational Auto Encoders for learning binding signatures of transcription factors
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