PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling
PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling
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DOI：
10.1371/journal.pcbi.1000156
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发表时间：
2008-08-01
影响因子：
4.3
通讯作者：
Siddharthan, Rahul
中科院分区：
文献类型：
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作者：
Siddharthan, Rahul
PhyloGibbs，我们最近的吉布斯采样基序发现者，考虑到在DNA中检测转录因子的结合位点，并分配后验概率，其预测通过采样整个配置空间。在这里，在一个名为PhyloGibbs-MP的扩展中，我们扩大了程序的范围，解决了计算调控基因组学中的两个主要问题。首先，PhyloGibbs-MP可以将预测定位到一个大的输入序列的小的，不确定的区域，从而有效地预测顺式调节模块（CRM）从头开始，同时预测这些模块中的结合位点，通常是由两个单独的程序完成的任务。PhyloGibbs-MP在这种从头CRM预测中的性能与使用先前表征的转录因子的先验知识的专用模块预测软件相当或上级。其次，PhyloGibbs-MP可以预测区分两组（或更多组）不同调控区的基序，即优先出现在一组中的基序。虽然其他的“歧视性基序发现者”已经在文献中发表，PhyloGibbs-MP的实现有一些独特的功能和灵活性。对合成基因组数据和实际基因组数据的基准测试表明，该算法在增强差异位点的预测和抑制共同位点的预测方面是成功的，并且在实际基因组数据上与其他判别式基序查找器相比或优于其他判别式基序查找器。其他增强功能包括显着的性能和速度改进，使用已知转录因子的“信息先验”的能力，以及以可以用通用基因组浏览器可视化的格式输出注释的能力。在独立的基序发现中，PhyloGibbs- MP仍然具有竞争力，在基准数据上优于PhyloGibbs-1.0和其他程序。
PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. First, PhyloGibbs-MP can localise predictions to small, undetermined regions of a large input sequence, thus effectively predicting cis-regulatory modules (CRMs) ab initio while simultaneously predicting binding sites in those modules-tasks that are usually done by two separate programs. PhyloGibbs-MP's performance at such ab initio CRM prediction is comparable with or superior to dedicated module-prediction software that use prior knowledge of previously characterised transcription factors. Second, PhyloGibbs-MP can predict motifs that differentiate between two ( or more) different groups of regulatory regions, that is, motifs that occur preferentially in one group over the others. While other "discriminative motif-finders'' have been published in the literature, PhyloGibbs-MP's implementation has some unique features and flexibility. Benchmarks on synthetic and actual genomic data show that this algorithm is successful at enhancing predictions of differentiating sites and suppressing predictions of common sites and compares with or outperforms other discriminative motif-finders on actual genomic data. Additional enhancements include significant performance and speed improvements, the ability to use "informative priors'' on known transcription factors, and the ability to output annotations in a format that can be visualised with the Generic Genome Browser. In stand-alone motif-finding, PhyloGibbs- MP remains competitive, outperforming PhyloGibbs-1.0 and other programs on benchmark data.