Bayesian adaptive sequence alignment algorithms.
Bayesian adaptive sequence alignment algorithms.
复制标题
贝叶斯自适应序列比对算法。
DOI：
10.1093/bioinformatics/14.1.25
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
Lawrence,CE
中科院分区：
文献类型：
--
作者：
Zhu,J;Liu,JS;Lawrence,CE
评分矩阵和空位惩罚参数的选择仍然是序列比对中的一个重要问题。我们在这里描述了一种算法，即“贝叶斯块对齐器”，它绕过了这一要求。该算法不需要一组固定的参数设置，而是返回任何感兴趣系列中的间隙数量和评分矩阵的贝叶斯后验概率。此外，该算法不是返回所选参数设置的单个最佳对齐，而是考虑所选间隙和评分矩阵的全部范围，返回所有对齐的后验分布，并根据数据按比例权衡每个矩阵。我们将贝叶斯对齐器与流行的史密斯-沃特曼算法进行比较，并使用文献中的参数设置，该算法已针对结构邻居的识别进行了优化，发现贝叶斯对齐器正确地识别了更多的结构邻居。在对一对激酶和一对 GTP 序列的比对进行详细检查时，我们说明了该算法识别不同程度保守的子序列的潜力。此外，此示例显示贝叶斯对齐器返回一对序列之间距离的无对齐评估。
The selection of a scoring matrix and gap penalty parameters continues to be an important problem in sequence alignment. We describe here an algorithm, the 'Bayes block aligner, which bypasses this requirement. Instead of requiring a fixed set of parameter settings, this algorithm returns the Bayesian posterior probability for the number of gaps and for the scoring matrices in any series of interest. Furthermore, instead of returning the single best alignment for the chosen parameter settings, this algorithm returns the posterior distribution of all alignments considering the full range of gapping and scoring matrices selected, weighing each in proportion to its probability based on the data. We compared the Bayes aligner with the popular Smith-Waterman algorithm with parameter settings from the literature which had been optimized for the identification of structural neighbors, and found that the Bayes aligner correctly identified more structural neighbors. In a detailed examination of the alignment of a pair of kinase and a pair of GTPase sequences, we illustrate the algorithm's potential to identify subsequences that are conserved to different degrees. In addition, this example shows that the Bayes aligner returns an alignment-free assessment of the distance between a pair of sequences.