caa a22 4500 378926012 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20041208xx s 000 0 eng 10.2202/1544-6115.1099 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1099 Statistical Analysis of Genomic Tag Data [Elektronische Daten] [Thomas L LaFramboise, D. Neil Hayes, Torstein Tengs] We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library. The statistical approaches outlined in this paper are divided into three parts. First, we demonstrate the application of classical capture-recapture theory to the question of library complexity, i.e. the number of unique fragments in the library. Simulation studies verify the accuracy, for sample sizes of magnitudes typical in genomic studies, of the formulas we use to make our estimates. Second, we present a straightforward statistical cost analysis of tag experiments designed to uncover either disease-causing pathogens or new genes. Third, we develop a hidden Markov model approach to karyotyping a sample using a tag library derived from the sample's genomic DNA. While the resolution of the approach depends upon the number of tags sequenced from the library, we show via simulation that copy number alterations can be reliably detected for lengths as small as 1 Mb, even when a moderate number of tags are sequenced. Simulations predict very good specificity as well. Finally, all three of our approaches are applied to data from real tag library experiments. The hidden Markov model results are in line with what was expected from simulation, and genomic alterations found by applying the method to a cancer cell line library are confirmed using PCR.The methods and data described in this paper are contained in an R package, tagAnalysis, freely available at http://meyerson.dfci.harvard.edu/~tl974/tagAnalysis. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Disease Modeling nationallicence Epidemiology nationallicence Genetics nationallicence Health Services Research nationallicence Computation nationallicence Computational Biology/Bioinformatics nationallicence genomic tag libraries nationallicence capture-recapture nationallicence hidden Markov model nationallicence karyotyping nationallicence LaFramboise Thomas L. Dana-Farber Cancer Institute aut Hayes D. Neil University of North Carolina aut Tengs Torstein Broad Institute of Harvard and MIT aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-12-08), 1-22 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1099 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1099 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- LaFramboise Thomas L. Dana-Farber Cancer Institute aut NATIONALLICENCE 700 1- Hayes D. Neil University of North Carolina aut NATIONALLICENCE 700 1- Tengs Torstein Broad Institute of Harvard and MIT aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-12-08), 1-22 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter