rSW-seq: Algorithm for Detection of Copy Number Alterations in Deep Sequencing Data
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CitationKim, Tae-Min, Lovelace J. Luquette, Ruibin Xi, and Peter J. Park. 2010. rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data. BMC Bioinformatics 11: 432.
AbstractBackground: Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy. Results: We develop a method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, our algorithm shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome. We further refine the algorithm to develop rSW-seq, (recursive Smith-Waterman-seq) to identify alterations in a complex configuration, which are commonly observed in the human cancer genome. To validate our approach, we compare our algorithm with an existing algorithm using simulated and publicly available datasets. We also compare the sequencing-based profiles to microarray-based results. Conclusion: We propose rSW-seq as an efficient method for detecting copy number changes in the tumor genome.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:5361134
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