Target inference from collections of genomic intervals

Krasnitz, A., Sun, G., Andrews, P., Wigler, M. (June 2013) Target inference from collections of genomic intervals. Proceedings of the National Academy of Sciences of the United States of America, 110 (25). E2271-E2278. ISSN 0027-8424

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DOI: 10.1073/pnas.1306909110


Finding regions of the genome that are significantly recurrent in noisy data are a common but difficult problem in present day computational biology. Cores of recurrent events (CORE) is a computational approach to solving this problem that is based on a formalized notion by which "core" intervals explain the observed data, where the number of cores is the "depth" of the explanation. Given that formalization, we implement CORE as a combinatorial optimization procedure with depth chosen from considerations of statistical significance. An important feature of CORE is its ability to explain data with cores of widely varying lengths. We examine the performance of this system with synthetic data, and then provide two demonstrations of its utility with actual data. Applying CORE to a collection of DNA copy number profiles from single cells of a given tumor, we determine tumor population phylogeny and find the features that separate subpopulations. Applying CORE to comparative genomic hybridization data from a large set of tumor samples, we define regions of recurrent copy number aberration in breast cancer.

Item Type: Paper
Subjects: bioinformatics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
CSHL Authors:
Communities: CSHL labs > Krasnitz lab
CSHL labs > Wigler lab
CSHL Cancer Center Program > Cancer Genetics
Depositing User: Matt Covey
Date: 6 June 2013
Date Deposited: 21 Jun 2013 18:11
Last Modified: 22 Dec 2017 16:29
PMCID: PMC3690846
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