Performance comparison of algorithms for finding transcription factor binding sites

S. Sinha, M. Tompa
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引用次数: 32

Abstract

We compare the accuracy of three motif-finding algorithms for the discovery of novel transcription factor binding sites among co-regulated genes. One of the algorithms (YMF) uses a motif model tailored for binding sites and an enumerative search of the motif space, while the other two (MEME and AlignACE) use a more general motif model and local search techniques. The comparison is done on synthetic data with planted motifs, as well as on real data sets of co-regulated genes from the yeast S. cerevisiae. More often than not, the enumerative algorithm is found to be more accurate than the other two on the yeast data sets, though there is a noticeable exclusivity in the accuracy of the different algorithms. The experiments on synthetic data reveal, not surprisingly, that each algorithm outperforms the others when motifs are planted according to its motif model.
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寻找转录因子结合位点的算法性能比较
我们比较了三种基序查找算法在共调节基因中发现新的转录因子结合位点的准确性。其中一种算法(YMF)使用针对结合位点定制的基序模型和基序空间的枚举搜索,而另外两种算法(MEME和AlignACE)使用更通用的基序模型和局部搜索技术。比较是在与种植基序的合成数据上进行的,以及在酵母酿酒酵母共调节基因的真实数据集上进行的。通常情况下,发现枚举算法比酵母数据集上的其他两种算法更准确,尽管不同算法的准确性存在明显的排他性。在合成数据上的实验表明,毫不奇怪,当根据其基序模型植入基序时,每种算法都优于其他算法。
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