Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554

Mathematical Citation Quotient (MCQ) 2015: 0.06

Online
ISSN
1544-6115
See all formats and pricing
In This Section
Volume 8, Issue 1 (Jun 2009)

Issues

A Non-Homogeneous Hidden-State Model on First Order Differences for Automatic Detection of Nucleosome Positions

Pei Fen Kuan
  • University of Wisconsin-Madison
/ Dana Huebert
  • University of Wisconsin-Madison
/ Audrey Gasch
  • University of Wisconsin-Madison
/ Sunduz Keles
  • University of Wisconsin-Madison
Published Online: 2009-06-19 | DOI: https://doi.org/10.2202/1544-6115.1454

The ability to map individual nucleosomes accurately across genomes enables the study of relationships between dynamic changes in nucleosome positioning/occupancy and gene regulation. However, the highly heterogeneous nature of nucleosome densities across genomes and short linker regions pose challenges in mapping nucleosome positions based on high-throughput microarray data of micrococcal nuclease (MNase) digested DNA. Previous works rely on additional detrending and careful visual examination to detect low-signal nucleosomes, which may exist in a subpopulation of cells. We propose a non-homogeneous hidden-state model based on first order differences of experimental data along genomic coordinates that bypasses the need for local detrending and can automatically detect nucleosome positions of various occupancy levels. Our proposed approach is applicable to both low and high resolution MNase-Chip and MNase-Seq (high throughput sequencing) data, and is able to map nucleosome-linker boundaries accurately. This automated algorithm is also computationally efficient and only requires a simple preprocessing step. We provide several examples illustrating the pitfalls of existing methods, the difficulties of detrending the observed hybridization signals and demonstrate the advantages of utilizing first order differences in detecting nucleosome occupancies via simulations and case studies involving MNase-Chip and MNase-Seq data of nucleosome occupancy in yeast S. cerevisiae.

Keywords: nucleosomes; MNase-chip; MNase-Seq; non-homogeneous hidden Markov model; first order differences; smoothing

About the article

Published Online: 2009-06-19



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1454. Export Citation

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Corinna Lieleg, Nils Krietenstein, Maria Walker, and Philipp Korber
Chromosoma, 2015, Volume 124, Number 2, Page 131

Comments (0)

Please log in or register to comment.
Log in