Thu, 21 Aug 2008 17:19:19 BST CiteULike: jfr Di http://www.citeulike.org/user/jfr/author/Di CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/jfr/article/796264 <i>BMC Bioinformatics, Vol. 7 (2006)</i><br /><br />BACKGROUND: DNA copy number alterations are one of the main characteristics of the cancer cell karyotype and can contribute to the complex phenotype of these cells. These alterations can lead to gains in cellular oncogenes as well as losses in tumor suppressor genes and can span small intervals as well as involve entire chromosomes. The ability to accurately detect these changes is central to understanding how they impact the biology of the cell. RESULTS: We describe a novel algorithm called CARAT (Copy Number Analysis with Regression And Tree) that uses probe intensity information to infer copy number in an allele-specific manner from high density DNA oligonuceotide arrays designed to genotype over 100,000 SNPs. Total and allele-specific copy number estimations using CARAT are independently evaluated for a subset of SNPs using quantitative PCR and allelic TaqMan reactions with several human breast cancer cell lines. The sensitivity and specificity of the algorithm are characterized using DNA samples containing differing numbers of X chromosomes as well as a test set of normal individuals. Results from the algorithm show a high degree of agreement with results from independent verification methods. CONCLUSION: Overall, CARAT automatically detects regions with copy number variations and assigns a significance score to each alteration as well as generating allele-specific output. When coupled with SNP genotype calls from the same array, CARAT provides additional detail into the structure of genome wide alterations that can contribute to allelic imbalance. CARAT: a novel method for allelic detection of DNA copy number changes using high density oligonucleotide arrays. J Huang W Wei J Chen J Zhang G Liu X Di R Mei S Ishikawa H Aburatani KW Jones MH Shapero doi:10.1186/1471-2105-7-83 BMC Bioinformatics, Vol. 7 (2006) 2006-08-10T16:26:23-00:00 2006 BMC Bioinformatics 1471-2105 7 bioinformatics snp-arrays software http://www.citeulike.org/user/jfr/article/344674 <i>Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 1958-1963.</i><br /><br />MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website. Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. X Di H Matsuzaki TA Webster E Hubbell G Liu S Dong D Bartell J Huang R Chiles G Yang MM Shen D Kulp GC Kennedy R Mei KW Jones S Cawley Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 1958-1963. 2005-10-07T14:26:15-00:00 2005 Bioinformatics 1367-4803 21 9 1958 1963 crc_bib genotyping methodology snp-arrays