Moitf GibbsGA: Sampling Transcription Factor Binding Sites Coupled with PSFM Optimization by Genetic Algorithm
Moitf GibbsGA: Sampling Transcription Factor Binding Sites Coupled with PSFM Optimization by Genetic Algorithm
复制标题
Moitf GibbsGA：转录因子结合位点采样与遗传算法 PSFM 优化相结合
DOI：
10.4156/jcit.vol5.issue10.18
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Licheng Jiao
中科院分区：
文献类型：
--
作者：
Lifang Liu;Licheng Jiao
转录因子结合位点 (TFBS) 或基序的识别在破译基因调控机制中发挥着重要作用。尽管已经开发了许多实验和计算方法，但寻找 TFBS 仍然是一个具有挑战性的问题。我们提出并开发了一种新颖的基于采样的主题查找方法，并结合遗传算法的 PSFM 优化，我们将其称为 Motif GibbsGA。 Motif GibbsGA 的一个显着特征是吉布斯采样方法和遗传算法的 PSFM 优化的结合。基于位置特定频率矩阵（PSFM）模体模型，采用贪婪策略来选择PSFM的初始参数。然后针对PSFM模型构建了Gibbs采样器。在采样过程中，PSFM 通过遗传算法进行改进。具有自适应添加和删除的后处理用于处理每个序列具有任意数量实例的一般情况。因此 Motif GibbsGA 能够在单个数据集中发现多个出现次数不同的不同主题。我们在 Tompa 等人编译的基准数据集上测试我们的方法。 (2005) 评估预测 TFBS 的计算工具。 Motif GibbsGA 在该数据集上的性能与现有工具的性能相当，并且在许多情况下超过了现有工具的性能。这部分归因于改进 PSFM 的遗传算法所发挥的重要作用。
Identification of transcription factor binding sites (TFBSs) or motifs plays an important role in deciphering the mechanisms of gene regulation. Although many experimental and computational methods have been developed, finding TFBSs remains a challenging problem. We propose and develop a novel sampling based motif finding method coupled with PSFM optimization by genetic algorithm, which we call Motif GibbsGA. One significant feature of Motif GibbsGA is the combination of a Gibbs sampling method and a PSFM optimization by genetic algorithm. Based on position-specific frequency matrix (PSFM) motif model, a greedy strategy for choosing the initial parameters of PSFM is employed. Then a Gibbs sampler is build with respect to PSFM model. During the sampling process, PSFM is improved via a genetic algorithm. A post-processing with adaptive adding and removing is used to handle general cases with arbitrary numbers of instances per sequence. So Motif GibbsGA is capable of discovering several different motifs with differing numbers of occurrences in a single dataset. We test our method on the benchmark dataset compiled by Tompa et al. (2005) for assessing computational tools that predict TFBSs. The performance of Motif GibbsGA on this data set compares well to, and in many cases exceeds, the performance of existing tools. This is in part attributed to the significant role played by the genetic algorithm that improved PSFM.
影响因子：
14.9
作者：
Frith, MC;Hansen, U;Weng, ZP
通讯作者：
Weng, ZP
影响因子：
56.9
作者：
LAWRENCE, CE;ALTSCHUL, SF;WOOTTON, JC
通讯作者：
WOOTTON, JC
影响因子：
14.9
作者：
Frith, MC;Fu, YT;Weng, ZP
通讯作者：
Weng, ZP