Liang Kuo-Ching, Wang Xiaodong, Anastassiou Dimitris
Columbia University, Department of Electrical Engineering, New York, NY 10025, USA.
Bioinformatics. 2008 Jan 1;24(1):46-55. doi: 10.1093/bioinformatics/btm543. Epub 2007 Nov 17.
Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of non-coding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery.
We propose a deterministic sequential Monte Carlo (DSMC) motif discovery technique based on the position weight matrix (PWM) model to locate conserved motifs in a given set of nucleotide sequences, and extend our model to search for instances of the motif with insertions/deletions. We show that the proposed method can be used to align the motif where there are insertions and deletions found in different instances of the motif, which cannot be satisfactorily done using other multiple alignment and motif discovery algorithms.
MATLAB code is available at http://www.ee.columbia.edu/~kcliang
保守基序通常具有生物学意义，能为基因转录调控、生物分子二级结构、非编码RNA的存在及进化历史等生物学方面提供见解。随着测序基因组数据数量的增加，需要更快、更准确的工具来自动化基序发现过程。
我们提出了一种基于位置权重矩阵（PWM）模型的确定性序贯蒙特卡罗（DSMC）基序发现技术，用于在给定的核苷酸序列集中定位保守基序，并扩展我们的模型以搜索带有插入/缺失的基序实例。我们表明，所提出的方法可用于比对在基序的不同实例中发现有插入和缺失的基序，而使用其他多重比对和基序发现算法无法令人满意地完成此操作。
MATLAB代码可在http://www.ee.columbia.edu/~kcliang获取
