GNOMES: an integrated framework for genome-wide normalization and differential binding analysis of CUT&RUN and...

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Quantitative comparison of epigenomic datasets (ChIP-seq, CUT&RUN) is challenging due to normalization difficulties.
• Standard normalization methods (sequencing depth) are insufficient; spike-in controls have limitations.
• Existing tools separate normalization and differential binding analysis, hindering visual inspection and evaluation.

Purpose of the Study:

• To develop a unified framework, GNOMES (Genome-wide NOrmalization of Mapped Epigenomic Signals), for integrated epigenomic data analysis.
• To address challenges in signal normalization, quality control, and differential binding analysis for ChIP-seq and CUT&RUN data.
• To provide a user-friendly tool that simplifies the identification of chromatin changes.

Main Methods:

• GNOMES processes aligned reads from ChIP-seq and CUT&RUN experiments.
• Implements genome-wide normalization using percentile scaling of signal local maxima.
• Integrates normalization, optional consensus peak identification, and differential binding analysis using tools like MACS2, DESeq2, and edgeR.

Main Results:

• GNOMES generates normalized coverage profiles and differential binding results.
• The framework provides robust normalization, applicable to broad and narrow enrichment patterns.
• Extensive quality control metrics and visual outputs (bigWig tracks, heatmaps, PCA) are generated.

Conclusions:

• GNOMES offers an integrated, customizable environment for epigenomic data analysis.
• It simplifies ChIP-seq and CUT&RUN analysis by combining normalization, differential analysis, and quality control.
• The tool facilitates the identification of chromatin changes through a unified workflow.