Copy number normalization distinguishes differential signals driven by copy number differences in ATAC-seq and...

Area of Science:

• Genomics
• Epigenetics
• Bioinformatics

Background:

• ATAC-seq and ChIP-seq are crucial for identifying differential genomic signals.
• Copy number variation (CNV) is frequently overlooked in differential analyses, potentially confounding results.
• CNV can significantly influence observed differential signals in sequencing data.

Purpose of the Study:

• To develop and validate a bioinformatics pipeline for copy number normalization in ATAC-seq and ChIP-seq data.
• To differentiate true biological signals from those driven by copy number variations.
• To assess the impact of copy number normalization on understanding gene expression in conditions like Down syndrome.

Main Methods:

• Development of a novel bioinformatics pipeline incorporating copy number normalization.
• Comparison of averaged signal per gene copy to isolate CNV-driven effects.
• Application of the pipeline to analyze differential signals in Down syndrome, focusing on chromosome 21.

Main Results:

• Demonstrated that copy number differences can drive or dominate differential signals in sequencing data.
• The proposed copy number normalization pipeline effectively segregates CNV-driven signals from other biological factors.
• Analysis of Down syndrome revealed distinct dosage-dependent and -independent molecular changes on chromosome 21.

Conclusions:

• Copy number normalization is a critical and recommended general approach for ATAC-seq and ChIP-seq differential analyses.
• Accurate identification of differential signals requires accounting for copy number variations.
• This method enhances the reliability of epigenetic and genomic signal interpretation.