BACKGROUND: Deviations in the amount of genomic content that arise during 
tumorigenesis, called copy number alterations, are structural rearrangements 
that can critically affect gene expression patterns. Additionally, copy number 
alteration profiles allow insight into cancer discrimination, progression and 
complexity. On data obtained from high-throughput sequencing, improving quality 
through GC bias correction and keeping false positives to a minimum help build 
reliable copy number alteration profiles.
RESULTS: We introduce seqCNA, a parallelized R package for an integral copy 
number analysis of high-throughput sequencing cancer data. The package includes 
novel methodology on (i) filtering, reducing false positives, and (ii) GC 
content correction, improving copy number profile quality, especially under 
great read coverage and high correlation between GC content and copy number. 
Adequate analysis steps are automatically chosen based on availability of 
paired-end mapping, matched normal samples and genome annotation.
CONCLUSIONS: seqCNA, available through Bioconductor, provides accurate copy 
number predictions in tumoural data, thanks to the extensive filtering and 
better GC bias correction, while providing an integrated and parallelized 
workflow.