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  Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns

Huska, M., & Vingron, M. (2016). Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns. PLoS Computational Biology, 12(12): e1005249. doi:10.1371/journal.pcbi.1005249.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-4773-9 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-4774-7
Genre: Journal Article

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© 2016 Huska, Vingron

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 Creators:
Huska, M.1, Author              
Vingron, M.2, Author              
Affiliations:
1IMPRS for Computational Biology and Scientific Computing - IMPRS-CBSC (Kirsten Kelleher), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Abstract: Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013;2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a region's methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately.

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Language(s): eng - English
 Dates: 2016-12-162016
 Publication Status: Published in print
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 Identifiers: PMID: 27984582
DOI: 10.1371/journal.pcbi.1005249
ISSN: 1553-7358 (Electronic)
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (12) Sequence Number: e1005249 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1