seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Copy number alterations (CNAs) are genomic changes critical to cancer development and progression.
• Accurate CNA profiling from high-throughput sequencing data requires GC bias correction and minimization of false positives.

Purpose of the Study:

• To introduce seqCNA, a parallelized R package for comprehensive copy number analysis of high-throughput sequencing cancer data.
• To improve the quality and reliability of CNA profiles.

Main Methods:

• Developed novel filtering methodology to reduce false positives in CNA detection.
• Implemented GC content correction to enhance CNA profile quality, particularly with high read coverage.
• Automated selection of analysis steps based on data characteristics like paired-end mapping and genome annotation.

Main Results:

• seqCNA offers an integrated and parallelized workflow for CNA analysis.
• The package effectively filters data and corrects for GC bias, leading to improved CNA profile accuracy.
• Accurate copy number predictions are achieved in tumoral data.

Conclusions:

• seqCNA provides accurate copy number predictions in tumor data.
• The package's extensive filtering and GC bias correction enhance reliability.
• seqCNA offers an integrated and parallelized workflow for high-throughput sequencing cancer data analysis.