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吉布斯质心采样器。

The Gibbs Centroid Sampler.

作者信息

Thompson William A, Newberg Lee A, Conlan Sean, McCue Lee Ann, Lawrence Charles E

机构信息

Center for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.

出版信息

Nucleic Acids Res. 2007 Jul;35(Web Server issue):W232-7. doi: 10.1093/nar/gkm265. Epub 2007 May 5.

DOI:10.1093/nar/gkm265
PMID:17483517
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1933196/
Abstract

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.

摘要

吉布斯质心采样器是一个软件包,旨在定位生物聚合物序列中的保守元件。吉布斯质心采样器报告一个质心比对,即与从转录因子结合位点比对的后验概率分布中选择的样本集具有最小总距离的比对。通过这样做,它从整个解决方案集合中获取信息,而不是像许多基序查找算法(包括其前身吉布斯递归采样器)那样只针对单个最可能的点。质心估计器已被证明在RNA二级结构预测和基序查找的灵敏度和阳性预测值方面都有显著提高。可通过以下网址获取吉布斯质心采样器以及交互式教程、在线用户手册和软件下载信息:http://bayesweb.wadsworth.org/gibbs/gibbs.html。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/73e67e405868/gkm265f3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/1e98e3a878a5/gkm265f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/c49c98c4d779/gkm265f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/73e67e405868/gkm265f3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/1e98e3a878a5/gkm265f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/c49c98c4d779/gkm265f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c219/1933196/73e67e405868/gkm265f3a.jpg

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Bioinformatics. 2007 Jul 15;23(14):1718-27. doi: 10.1093/bioinformatics/btm241. Epub 2007 May 8.
3
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PLoS One. 2017 Jun 15;12(6):e0179761. doi: 10.1371/journal.pone.0179761. eCollection 2017.
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