Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
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Statistical Analysis of Genomic Tag Data
1Dana-Farber Cancer Institute
2University of North Carolina
3Broad Institute of Harvard and MIT
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 3, Issue 1, Pages 1–22, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1099, December 2004
- Published Online:
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.