MOTIVATION: DNA copy number variants (CNVs) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization-based techniques have been developed for high-throughput measurement of DNA copy number. In many studies, multiple technical platforms or different versions of the same platform were used to interrogate the same samples; and it became necessary to pool information across these multiple sources to derive a consensus molecular profile for each sample. An integrated analysis is expected to maximize resolution and accuracy, yet currently there is no well-formulated statistical method to address the between-platform differences in probe coverage, assay methods, sensitivity and analytical complexity. RESULTS: The conventional approach is to apply one of the CNV detection ('segmentation') algorithms to search for DNA segments of altered signal intensity. The results from multiple platforms are combined after segmentation. Here we propose a new method, Multi-Platform Circular Binary Segmentation (MPCBS), which pools statistical evidence across platforms during segmentation, and does not require pre-standardization of different data sources. It involves a weighted sum of t-statistics, which arises naturally from the generalized log-likelihood ratio of a multi-platform model. We show by comparing the integrated analysis of Affymetrix and Illumina SNP array data with Agilent and fosmid clone end-sequencing results on eight HapMap samples that MPCBS achieves improved spatial resolution, detection power and provides a natural consensus across platforms. We also apply the new method to analyze multi-platform data for tumor samples. AVAILABILITY: The R package for MPCBS is registered on R-Forge (http://r-forge.r-project.org/) under project name MPCBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: DNA copy number variants (CNVs) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization-based techniques have been developed for high-throughput measurement of DNA copy number. In many studies, multiple technical platforms or different versions of the same platform were used to interrogate the same samples; and it became necessary to pool information across these multiple sources to derive a consensus molecular profile for each sample. An integrated analysis is expected to maximize resolution and accuracy, yet currently there is no well-formulated statistical method to address the between-platform differences in probe coverage, assay methods, sensitivity and analytical complexity. RESULTS: The conventional approach is to apply one of the CNV detection ('segmentation') algorithms to search for DNA segments of altered signal intensity. The results from multiple platforms are combined after segmentation. Here we propose a new method, Multi-Platform Circular Binary Segmentation (MPCBS), which pools statistical evidence across platforms during segmentation, and does not require pre-standardization of different data sources. It involves a weighted sum of t-statistics, which arises naturally from the generalized log-likelihood ratio of a multi-platform model. We show by comparing the integrated analysis of Affymetrix and Illumina SNP array data with Agilent and fosmid clone end-sequencing results on eight HapMap samples that MPCBS achieves improved spatial resolution, detection power and provides a natural consensus across platforms. We also apply the new method to analyze multi-platform data for tumor samples. AVAILABILITY: The R package for MPCBS is registered on R-Forge (http://r-forge.r-project.org/) under project name MPCBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Steven A McCarroll; Finny G Kuruvilla; Joshua M Korn; Simon Cawley; James Nemesh; Alec Wysoker; Michael H Shapero; Paul I W de Bakker; Julian B Maller; Andrew Kirby; Amanda L Elliott; Melissa Parkin; Earl Hubbell; Teresa Webster; Rui Mei; James Veitch; Patrick J Collins; Robert Handsaker; Steve Lincoln; Marcia Nizzari; John Blume; Keith W Jones; Rich Rava; Mark J Daly; Stacey B Gabriel; David Altshuler Journal: Nat Genet Date: 2008-09-07 Impact factor: 38.330
Authors: Donald F Conrad; Dalila Pinto; Richard Redon; Lars Feuk; Omer Gokcumen; Yujun Zhang; Jan Aerts; T Daniel Andrews; Chris Barnes; Peter Campbell; Tomas Fitzgerald; Min Hu; Chun Hwa Ihm; Kati Kristiansson; Daniel G Macarthur; Jeffrey R Macdonald; Ifejinelo Onyiah; Andy Wing Chun Pang; Sam Robson; Kathy Stirrups; Armand Valsesia; Klaudia Walter; John Wei; Chris Tyler-Smith; Nigel P Carter; Charles Lee; Stephen W Scherer; Matthew E Hurles Journal: Nature Date: 2009-10-07 Impact factor: 49.962
Authors: Jeffrey M Kidd; Gregory M Cooper; William F Donahue; Hillary S Hayden; Nick Sampas; Tina Graves; Nancy Hansen; Brian Teague; Can Alkan; Francesca Antonacci; Eric Haugen; Troy Zerr; N Alice Yamada; Peter Tsang; Tera L Newman; Eray Tüzün; Ze Cheng; Heather M Ebling; Nadeem Tusneem; Robert David; Will Gillett; Karen A Phelps; Molly Weaver; David Saranga; Adrianne Brand; Wei Tao; Erik Gustafson; Kevin McKernan; Lin Chen; Maika Malig; Joshua D Smith; Joshua M Korn; Steven A McCarroll; David A Altshuler; Daniel A Peiffer; Michael Dorschner; John Stamatoyannopoulos; David Schwartz; Deborah A Nickerson; James C Mullikin; Richard K Wilson; Laurakay Bruhn; Maynard V Olson; Rajinder Kaul; Douglas R Smith; Evan E Eichler Journal: Nature Date: 2008-05-01 Impact factor: 49.962
Authors: Gro Nilsen; Knut Liestøl; Peter Van Loo; Hans Kristian Moen Vollan; Marianne B Eide; Oscar M Rueda; Suet-Feung Chin; Roslin Russell; Lars O Baumbusch; Carlos Caldas; Anne-Lise Børresen-Dale; Ole Christian Lingjaerde Journal: BMC Genomics Date: 2012-11-04 Impact factor: 3.969