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Bioinformatics. 2013 Mar 15;29(6):711-6. doi: 10.1093/bioinformatics/bts719. Epub 2013 Jan 6.

NucleoFinder: a statistical approach for the detection of nucleosome positions.

Author information

1
Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Abstract

MOTIVATION:

The identification of nucleosomes along the chromatin is key to understanding their role in the regulation of gene expression and other DNA-related processes. However, current experimental methods (MNase-ChIP, MNase-Seq) sample nucleosome positions from a cell population and contain biases, making thus the precise identification of individual nucleosomes not straightforward. Recent works have only focused on the first point, where noise reduction approaches have been developed to identify nucleosome positions.

RESULTS:

In this article, we propose a new approach, termed NucleoFinder, that addresses both the positional heterogeneity across cells and experimental biases by seeking nucleosomes consistently positioned in a cell population and showing a significant enrichment relative to a control sample. Despite the absence of validated dataset, we show that our approach (i) detects fewer false positives than two other nucleosome calling methods and (ii) identifies two important features of the nucleosome organization (the nucleosome spacing downstream of active promoters and the enrichment/depletion of GC/AT dinucleotides at the centre of in vitro nucleosomes) with equal or greater ability than the other two methods.

PMID:
23297036
PMCID:
PMC3597142
DOI:
10.1093/bioinformatics/bts719
[Indexed for MEDLINE]
Free PMC Article
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