Finding Protein Binding Sites Using Volunteer Computing Grids
Finding Protein Binding Sites Using Volunteer Computing Grids
复制标题
使用志愿者计算网格寻找蛋白质结合位点
DOI：
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复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
W. Thompson
中科院分区：
文献类型：
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作者：
Travis Desell;L. Newberg;M. Magdon;B. Szymański;W. Thompson
本文介绍了DNA@Home志愿者计算项目的发展，其目的是使用吉布斯采样的DNA控制信号的识别和定位全基因组规模的数据集的初步工作。目前大多数涉及这些控制信号的序列分析的研究涉及的数据集要小得多，然而志愿者计算可以提供必要的计算能力，使全基因组分析可行。一个容错和异步实施吉布斯采样使用伯克利开放式网络计算基础设施（BOINC），这是目前被用来分析结核分枝杆菌基因组的基因间区域。在仅三个月的有限运作中，该项目已有1 800多名自愿计算主机参与，并获得了分析所需的一些样本，其速度比结核分枝杆菌数据集的平均计算主机快400倍以上。我们认为该项目的初步结果为此类生物信息学志愿者计算项目的可行性和公众利益提供了强有力的论据。
This paper describes initial work in the development of the DNA@Home volunteer computing project, which aims to use Gibbs sampling for the identification and location of DNA control signals on full genome scale data sets. Most current research involving sequence analysis for these control signals involve significantly smaller data sets, however volunteer computing can provide the necessary computational power to make full genome analysis feasible. A fault tolerant and asynchronous implementation of Gibbs sampling using the Berkeley Open Infrastructure for Network Computing (BOINC) is presented, which is currently being used to analyze the intergenic regions of the Mycobacterium tuberculosis genome. In only three months of limited operation, the project has had over 1,800 volunteered computing hosts participate and obtains a number of samples required for analysis over 400 times faster than an average computing host for the Mycobacterium tuberculosis dataset. We feel that the preliminary results for this project provide a strong argument for the feasibility and public interest of a volunteer computing project for this type of bioinformatics.
影响因子：
56.9
作者：
LAWRENCE, CE;ALTSCHUL, SF;WOOTTON, JC
通讯作者：
WOOTTON, JC