seqCNA

seqCNA performs copy number analysis on high-throughput sequencing cancer data to detect and characterize copy number alterations (CNAs) in tumor genomes.

Key Features:

• R package implementation: Implemented as an R package for integration into R-based bioinformatics workflows.
• Parallelized workflow: Leverages parallel computing to accelerate processing of large high-throughput sequencing datasets.
• Novel filtering methodology: Applies advanced filtering techniques to reduce false positives in CNA detection.
• GC content correction: Implements GC bias correction to adjust copy number estimates affected by GC content and read coverage correlation.
• Automatic analysis selection: Selects analysis steps based on the availability of paired-end mapping data, matched normal samples, and genome annotations.
• Integration with Bioconductor: Enables interoperability with Bioconductor packages and workflows.

Scientific Applications:

• Cancer genomics: Detection and characterization of CNAs that contribute to tumorigenesis.
• Structural variation analysis: Identification of structural genomic rearrangements from sequencing-derived copy number profiles.
• Cancer discrimination and progression studies: Comparison of CNA profiles to support cancer subtype discrimination and study of disease progression.
• Reducing false positives in CNA calls: Application of filtering and normalization to improve reliability of copy number predictions.

Methodology:

Data filtering, GC bias correction, normalization, parallelized processing, and automatic selection of analysis steps based on paired-end mapping data, matched normal samples, and genome annotations.