NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence.
NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence.
复制标题
嵌套：核酸序列中代表性过多的基序的敏感推断。
DOI：
10.1093/nar/gki282
复制
发表时间：
2005
影响因子：
14.9
通讯作者：
Hubbard, TJP
中科院分区：
文献类型：
--
作者：
Down, TA;Hubbard, TJP
NestedMICA是一个新的、可扩展的模式发现系统，用于在生物序列中发现转录因子结合位点和相似的基序。像以前的几种方法一样，NestedMICA通过优化概率混合模型来拟合一组序列来解决这个问题。然而，使用一种新开发的称为嵌套采样的推理策略意味着NestedMICA能够找到最佳解决方案，而无需有问题的初始化或播种步骤。我们调查的性能NestedMICA在一个范围内的情况下，合成数据和一组特征良好的肌肉调控区，并比较它与流行的MEME程序。我们表明，新方法是显着更敏感的MEME：在一种情况下，它成功地从背景序列中提取的目标基序比现有的程序可以处理的时间长四倍。它还对包含多个重要基序的合成序列进行了稳健的处理。当在一组真实的调控序列上进行测试时，NestedMICA产生的基序是所有五种丰富的注释结合位点的良好预测因子。
NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites.
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