BioOptimizer: a Bayesian scoring function approach to motif discovery
BioOptimizer: a Bayesian scoring function approach to motif discovery
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DOI：
10.1093/bioinformatics/bth127
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发表时间：
2004-07-01
期刊：
影响因子：
5.8
通讯作者：
Liu, JS
中科院分区：
文献类型：
--
作者：
Jensen, ST;Liu, JS
动机：转录因子(TF)直接与基因组上的短片段结合，通常在基因转录起始点上游数百至数千个碱基对内，以调节基因表达。TFS结合位点的实验测定既昂贵又耗时。已经开发了许多寻找基序的程序，但没有一个程序在所有情况下都是明显优越的。实践者经常发现很难判断这些算法预测的基序中哪些更有可能是生物相关的。结果：我们基于完整的贝叶斯模型推导出一个综合评分函数，该函数可以处理未知位点丰度、未知基序宽度和具有可变长度间隙的两块基序。提出了一种名为BioOptimizer的算法来优化该评分函数，以降低任何基序发现程序所发现的基序信号中的噪声。BioOptimizer可以与几个现有的程序结合使用，其准确性被证明优于单独使用这些基序寻找程序中的任何一个，当通过模拟研究和应用于细菌中的一组共调控基因时。此外，这种评分函数公式使我们能够客观地比较不同的预测主题并选择最优的主题，有效地结合了现有程序的优势。
Motivation: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant.Results: We derive a comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accuracy of BioOptimizer, which can be used in conjunction with several existing programs, is shown to be superior to using any of these motif-finding programs alone when evaluated by both simulation studies and application to sets of co-regulated genes in bacteria. In addition, this scoring function formulation enables us to compare objectively different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs.