Rawcopy: Improved copy number analysis with Affymetrix arrays

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Microarray data analysis is challenged by noise and systematic variations, impacting copy number analysis resolution.
• Accurate copy number analysis is crucial for understanding genetic variations and diseases.

Purpose of the Study:

• To introduce Rawcopy, an R package designed for processing Affymetrix microarray raw intensity data (CEL files).
• To improve signal-to-noise ratio and the accuracy of copy number alterations detection.

Main Methods:

• Rawcopy utilizes noise characteristics from reference samples to estimate log ratio and B-allele frequency.
• It performs total and allele-specific copy number analysis for Affymetrix CytoScan HD, CytoScan 750k, and SNP 6.0 arrays.
• The package includes visualization tools for assessing sample quality and genome-wide copy number states.

Main Results:

• Rawcopy demonstrates a superior signal-to-noise ratio compared to existing alternatives.
• It achieves a higher proportion of validated copy number alterations.
• Provides enhanced visualization for technical quality assessment and genome-wide copy number profiling.

Conclusions:

• Rawcopy offers a robust solution for processing Affymetrix microarray data, improving the accuracy of copy number analysis.
• The package enhances the reliability of detecting genetic alterations and assessing sample quality.
• Rawcopy represents a valuable tool for genomic research and clinical diagnostics.