ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline.

Area of Science:

• Genomics and Molecular Biology
• Bioinformatics and Computational Biology

Background:

• ChIP-seq and DNase-seq are standard for studying gene regulation, protein-DNA interactions, and chromatin accessibility.
• Comprehensive quality control (QC) and analysis tools are crucial but often lack unified frameworks for both data types.
• Existing tools often do not provide a comprehensive, unbiased reference of data quality metrics for ChIP-seq and DNase-seq.

Purpose of the Study:

• To develop ChiLin, a computational pipeline for automated QC and analysis of ChIP-seq and DNase-seq data.
• To create a unified framework that combines analysis and QC for both ChIP-seq and DNase-seq.
• To establish a comprehensive atlas of quality metrics by leveraging a large dataset of public samples.

Main Methods:

• Developed ChiLin as a flexible, modular computational pipeline for batch processing of ChIP-seq and DNase-seq data.
• Integrated automated QC and data analysis functionalities within the pipeline.
• Compiled and classified quality metrics from over 23,677 public ChIP-seq and DNase-seq samples to create a quality atlas.

Main Results:

• ChiLin provides automated, scalable processing for large batches of ChIP-seq and DNase-seq datasets.
• Generated comprehensive QC reports with comparisons to a large historical dataset (23,677 samples).
• The developed quality atlas is the most comprehensive resource for ChIP-seq and DNase-seq quality metrics, aiding experimental quality assessment.

Conclusions:

• ChiLin is a scalable and powerful tool for processing large numbers of ChIP-seq and DNase-seq datasets.
• The pipeline offers user-friendly directories and reports for analysis output and quality metrics.
• A comprehensive quality atlas with fine classification has been compiled from 23,677 profiles.