Document Detail


A non-homogeneous hidden-state model on first order differences for automatic detection of nucleosome positions.
MedLine Citation:
PMID:  19572828     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Pei Fen Kuan; Dana Huebert; Audrey Gasch; Sunduz Keles
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Review     Date:  2009-06-19
Journal Detail:
Title:  Statistical applications in genetics and molecular biology     Volume:  8     ISSN:  1544-6115     ISO Abbreviation:  Stat Appl Genet Mol Biol     Publication Date:  2009  
Date Detail:
Created Date:  2009-07-03     Completed Date:  2009-09-17     Revised Date:  2014-09-17    
Medline Journal Info:
Nlm Unique ID:  101176023     Medline TA:  Stat Appl Genet Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  Article29     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Genome, Human*
Humans
Models, Genetic*
Nucleosomes / genetics*
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated*
Grant Support
ID/Acronym/Agency:
HG003747/HG/NHGRI NIH HHS; R01 HG003747/HG/NHGRI NIH HHS; R01 HG003747-01A2/HG/NHGRI NIH HHS; T32GM007215/GM/NIGMS NIH HHS
Chemical
Reg. No./Substance:
0/Nucleosomes
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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