CNAseg--a novel framework for identification of copy number changes in cancer from second-generation sequencing data.
BACKGROUND
Copy number abnormalities (CNAs) represent an important type of genetic mutation that can lead to abnormal cell growth and proliferation. New high-throughput sequencing technologies promise comprehensive characterization of CNAs. In contrast to microarrays, where probe design follows a carefully developed protocol, reads represent a random sample from a library and may be prone to representation biases due to GC content and other factors. The discrimination between true and false positive CNAs becomes an important issue.


RESULTS
We present a novel approach, called CNAseg, to identify CNAs from second-generation sequencing data. It uses depth of coverage to estimate copy number states and flowcell-to-flowcell variability in cancer and normal samples to control the false positive rate. We tested the method using the COLO-829 melanoma cell line sequenced to 40-fold coverage. An extensive simulation scheme was developed to recreate different scenarios of copy number changes and depth of coverage by altering a real dataset with spiked-in CNAs. Comparison to alternative approaches using both real and simulated datasets showed that CNAseg achieves superior precision and improved sensitivity estimates.


BACKGROUND
The CNAseg package and test data are available at http://www.compbio.group.cam.ac.uk/software.html.
Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK. Sergii.Ivakhno@cancer.org.uk
Sergii
S
Ivakhno
Tom
T
Royce
Anthony J
AJ
Cox
Dirk J
DJ
Evers
R Keira
RK
Cheetham
Simon
S
Tavaré
D000465:Algorithms; D001482:Base Composition; D045744:Cell Line, Tumor; D056915:DNA Copy Number Variations; D015894:Genome, Human; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D017422:Sequence Analysis, DNA