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NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model
Motivation: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371838/ https://www.ncbi.nlm.nih.gov/pubmed/22689767 http://dx.doi.org/10.1093/bioinformatics/bts206 |
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author | Polishko, Anton Ponts, Nadia Le Roch, Karine G. Lonardi, Stefano |
author_facet | Polishko, Anton Ponts, Nadia Le Roch, Karine G. Lonardi, Stefano |
author_sort | Polishko, Anton |
collection | PubMed |
description | Motivation: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or ‘fuzzy’ in the sequenced cell sample. Results: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes. Availability: Visit http://www.cs.ucr.edu/~polishka Contact: stelo@cs.ucr.edu or polishka@cs.ucr.edu |
format | Online Article Text |
id | pubmed-3371838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33718382012-06-11 NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model Polishko, Anton Ponts, Nadia Le Roch, Karine G. Lonardi, Stefano Bioinformatics Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa Motivation: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or ‘fuzzy’ in the sequenced cell sample. Results: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes. Availability: Visit http://www.cs.ucr.edu/~polishka Contact: stelo@cs.ucr.edu or polishka@cs.ucr.edu Oxford University Press 2012-06-15 2012-06-09 /pmc/articles/PMC3371838/ /pubmed/22689767 http://dx.doi.org/10.1093/bioinformatics/bts206 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa Polishko, Anton Ponts, Nadia Le Roch, Karine G. Lonardi, Stefano NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title | NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title_full | NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title_fullStr | NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title_full_unstemmed | NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title_short | NOrMAL: accurate nucleosome positioning using a modified Gaussian mixture model |
title_sort | normal: accurate nucleosome positioning using a modified gaussian mixture model |
topic | Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371838/ https://www.ncbi.nlm.nih.gov/pubmed/22689767 http://dx.doi.org/10.1093/bioinformatics/bts206 |
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