Abstract:
: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.
journal_name
Proc Natl Acad Sci U S Aauthors
Krasnitz A,Sun G,Andrews P,Wigler Mdoi
10.1073/pnas.1306909110subject
Has Abstractpub_date
2013-06-18 00:00:00pages
E2271-8issue
25eissn
0027-8424issn
1091-6490pii
1306909110journal_volume
110pub_type
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