Details:

Alternative Title:
PLoS One
Publisher's site:
http://dx.doi.org/10.1371/journal.pone.0086272
Personal Author:
Satten, Glen A. ; Allen, Andrew S. ; Ikeda, Morna ; Mulle, Jennifer G. ; Warren, Stephen T.
Satten, Glen A. ; Allen, Andrew S. ; Ikeda, Morna ; Mulle, Jennifer G. ; Warren, Stephen T. Less -
Description:
Motivation

The discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimensions (length and intensity) it is unclear how to order called CNVs to prioritize experimental validation.

Results

We developed a univariate score that correlates with the likelihood that a CNV is true. This score can be used to order CNV calls in such a way that calls having larger scores are more likely to overlap a true CNV. We developed cnv.beast, a computationally efficient algorithm for calling CNVs that uses robust backward elimination regression to keep CNV calls with scores that exceed a user-defined threshold. Using an independent dataset that was measured using a different platform, we validated our score and showed that our approach performed better than six other currently-available methods.

Availability

cnv.beast is available at http://www.duke.edu/~asallen/Software.html.


Subject:
[+]
Biology
Mathematics
Medicine
Research Article

Source:
PLoS One. 2014; 9(2).
Document Type:
Journal Article
Funding:
HL077663/HL/NHLBI NIH HHS/United States ; MH080129/MH/NIMH NIH HHS/United States ; MH080583/MH/NIMH NIH HHS/United States ; MH083722/MH/NIMH NIH HHS/United States ; MH084680/MH/NIMH NIH HHS/United States
HL077663/HL/NHLBI NIH HHS/United States ; MH080129/MH/NIMH NIH HHS/United States ; MH080583/MH/NIMH NIH HHS/United States ; MH083722/MH/NIMH NIH HHS/United States ; MH084680/MH/NIMH NIH HHS/United States Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:633775e26488f64903e13426878cab2e6c21475227ef515a7ada309127890dc5
File Type:

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