Name
Title | ||
|---|---|---|
CNAseg--a novel framework for identification of copy number changes in cancer from second-generation sequencing data. |
Abstract | ||
|---|---|---|
Motivation: 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1093/bioinformatics/btq587 | BIOINFORMATICS |
Keywords | Field | DocType |
copy number | False positive rate,Data mining,Computer science,Genome human,Test data,Bioinformatics,DNA microarray,Cancer | Journal |
Volume | Issue | ISSN |
26 | 24 | 1367-4803 |
Citations | PageRank | References |
19 | 1.50 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergii Ivakhno | 1 | 47 | 4.11 |
| Tom Royce | 2 | 19 | 1.50 |
| Anthony J Cox | 3 | 198 | 13.63 |
| Dirk J Evers | 4 | 19 | 1.50 |
| R Keira Cheetham | 5 | 60 | 4.24 |
| simon tavare | 6 | 229 | 24.40 |