Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 110, Issue 25, Pages E2271-E2278Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1306909110
Keywords
genome analysis; interval data; statistical inference
Categories
Funding
- Simons Foundation (Simons Foundation Autism Research Initiative) [SF51]
- Breast Cancer Research Foundation
- US Department of Defense [W81XWH-11-1-0747]
- National Institute of Health [5RC2CA148532-02, 1U01CA168409-01]
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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.
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