Stochastic EM-based TFBS motif discovery with MITSU.
Stochastic EM-based TFBS motif discovery with MITSU.
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DOI：
10.1093/bioinformatics/btu286
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发表时间：
2014-06-15
期刊：
影响因子：
--
通讯作者：
Aitken S
中科院分区：
文献类型：
--
作者：
Kilpatrick AM;Ward B;Aitken S
动机：期望最大化（EM）算法已经成功地应用于转录因子结合位点（TFBS）基序发现问题，是最广泛使用的基序发现算法的基础。在更广泛的概率建模领域，随机EM （sEM）算法已被用来克服EM算法的一些局限性；然而，扫描电镜在基序发现中的应用尚未得到充分的探索。结果：我们提出了MITSU (Motif discovery by ITerative Sampling and Updating)，这是一种新的Motif发现算法，它将sEM与改进的似然函数近似值相结合，该算法对输入数据集中Motif出现的分布不受约束。该算法在实际合成数据和几个特征的原核生物TFBS基序集合上进行了定量评估，结果显示优于EM和另一种基于sem的算法，特别是在位点水平的阳性预测值方面。可用性和实现：Java可执行文件可从http://www.sourceforge.net/p/mitsu-motif/下载，支持Linux/OS x，联系：a.m.kilpatrick@sms.ed.ac.uk
Motivation: The Expectation–Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored. Results: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value. Availability and implementation: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X. Contact: a.m.kilpatrick@sms.ed.ac.uk
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