SpikeFlow: automated and flexible analysis of ChIP-Seq data with spike-in control

Area of Science:

• Genomics
• Bioinformatics
• Epigenetics

Background:

• ChIP with reference exogenous genome (ChIP-Rx) is vital for studying histone modifications.
• Standard ChIP-seq pipelines are insufficient for ChIP-Rx data normalization.
• A comprehensive workflow for complete ChIP-Rx analysis is needed.

Purpose of the Study:

• Introduce SpikeFlow, an integrated Snakemake workflow for streamlined ChIP-Rx data analysis.
• Automate spike-in data scaling and offer multiple normalization options.
• Provide peak calling, differential analysis, and quality control for enhanced usability.

Main Methods:

• Developed SpikeFlow, a Snakemake workflow integrating existing bioinformatics tools.
• Implemented automated spike-in data scaling and diverse normalization strategies.
• Included peak calling, differential analysis, and comprehensive quality control.

Main Results:

• SpikeFlow automates key ChIP-Rx data processing steps, including normalization and analysis.
• The workflow demonstrated robust performance in comparative analyses with DiffBind and SpikChIP.
• Generated analysis reports with tables and graphs to aid biological interpretation.

Conclusions:

• SpikeFlow simplifies complex ChIP-Rx data analysis for researchers.
• The integrated workflow enhances usability and provides comprehensive analysis capabilities.
• SpikeFlow facilitates the detection of enrichment regions for histone modifications and transcription factor binding.