Target inference from collections of genomic intervals.

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.

authors

Krasnitz A,Sun G,Andrews P,Wigler M

doi

10.1073/pnas.1306909110

subject

Has Abstract

pub_date

2013-06-18 00:00:00

pages

E2271-8

issue

25

eissn

0027-8424

issn

1091-6490

pii

1306909110

journal_volume

110

pub_type

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