The Gibbs Centroid Sampler

The Gibbs Centroid Sampler

The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distributio...

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Main Authors: Thompson, William A., Newberg, Lee A., Conlan, Sean, McCue, Lee Ann, Lawrence, Charles E.
Format: Online
Language:English
Published: Oxford University Press 2007
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933196/
id pubmed-1933196
recordtype oai_dc
spelling pubmed-19331962007-07-31 The Gibbs Centroid Sampler Thompson, William A. Newberg, Lee A. Conlan, Sean McCue, Lee Ann Lawrence, Charles E. Articles The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments. In so doing, it garners information from the full ensemble of solutions, rather than only the single most probable point that is the target of many motif-finding algorithms, including its predecessor, the Gibbs Recursive Sampler. Centroid estimators have been shown to yield substantial improvements, in both sensitivity and positive predictive values, to the prediction of RNA secondary structure and motif finding. The Gibbs Centroid Sampler, along with interactive tutorials, an online user manual, and information on downloading the software, is available at: http://bayesweb.wadsworth.org/gibbs/gibbs.html. Oxford University Press 2007-07 2007-05-05 /pmc/articles/PMC1933196/ /pubmed/17483517 http://dx.doi.org/10.1093/nar/gkm265 Text en © 2007 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Thompson, William A.
Newberg, Lee A.
Conlan, Sean
McCue, Lee Ann
Lawrence, Charles E.
spellingShingle Thompson, William A.
Newberg, Lee A.
Conlan, Sean
McCue, Lee Ann
Lawrence, Charles E.
The Gibbs Centroid Sampler
author_facet Thompson, William A.
Newberg, Lee A.
Conlan, Sean
McCue, Lee Ann
Lawrence, Charles E.
author_sort Thompson, William A.
title The Gibbs Centroid Sampler
title_short The Gibbs Centroid Sampler
title_full The Gibbs Centroid Sampler
title_fullStr The Gibbs Centroid Sampler
title_full_unstemmed The Gibbs Centroid Sampler
title_sort gibbs centroid sampler
description The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments. In so doing, it garners information from the full ensemble of solutions, rather than only the single most probable point that is the target of many motif-finding algorithms, including its predecessor, the Gibbs Recursive Sampler. Centroid estimators have been shown to yield substantial improvements, in both sensitivity and positive predictive values, to the prediction of RNA secondary structure and motif finding. The Gibbs Centroid Sampler, along with interactive tutorials, an online user manual, and information on downloading the software, is available at: http://bayesweb.wadsworth.org/gibbs/gibbs.html.
publisher Oxford University Press
publishDate 2007
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933196/
_version_ 1611398587571240960

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