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  Multiscale DNA partitioning: Statistical evidence for segments.

Futschik, A., Hotz, T., Munk, A., & Sieling, H. (2014). Multiscale DNA partitioning: Statistical evidence for segments. Bioinformatics, 30(16), 2255-2262. doi:10.1093/bioinformatics/btu180.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0023-C068-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0028-3600-E
Genre: Journal Article

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 Creators:
Futschik, A., Author
Hotz, T., Author
Munk, A.1, Author              
Sieling, H., Author
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_1113580              

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 Abstract: Motivation: DNA segmentation, i.e. the partitioning of DNA in compositionally homogeneous segments, is a basic task in bioinformatics. Different algorithms have been proposed for various partitioning criteria such as Guanine/Cytosine (GC) content, local ancestry in population genetics or copy number variation. A critical component of any such method is the choice of an appropriate number of segments. Some methods use model selection criteria and do not provide a suitable error control. Other methods that are based on simulating a statistic under a null model provide suitable error control only if the correct null model is chosen. Results: Here, we focus on partitioning with respect to GC content and propose a new approach that provides statistical error control: as in statistical hypothesis testing, it guarantees with a user-specified probability Graphic that the number of identified segments does not exceed the number of actually present segments. The method is based on a statistical multiscale criterion, rendering this as a segmentation method that searches segments of any length (on all scales) simultaneously. It is also accurate in localizing segments: under benchmark scenarios, our approach leads to a segmentation that is more accurate than the approaches discussed in the comparative review of Elhaik et al. In our real data examples, we find segments that often correspond well to features taken from standard University of California at Santa Cruz (UCSC) genome annotation tracks.

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Language(s): eng - English
 Dates: 2014-04-212014-08-15
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1093/bioinformatics/btu180
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 30 (16) Sequence Number: - Start / End Page: 2255 - 2262 Identifier: -