基于隐马氏模型对编码序列缺失与插入的检测(英)

在基因组测序工作完成后，利用计算工具进行基因识别以及基因结构预测受到了越来越多人的重视.人们开发了大量的相关应用软件，如GenScan, Genemark, GRAIL等，这些软件在寻找新基因方面提供了很重要的线索.但基因的识别和预测问题仍未得到完全解决，当目标基因的编码序列有缺失和插入时，其预测结果和基因的实际结构相差很大.为了消除测序错误对预测结果的影响，希望能找出编码序列区的测序错误.基于这种想法，尝试根据DNA序列的一些统计特性，利用隐马尔科夫模型（Hidden Markov Model），引入缺失和插入状态，然后用Viterbi算法，从中找出含有缺失和插入的外显子序列片段.在常用的Burset/Guigo检测集进行检测，得到的结果在外显子水平上，Sn(sensitivity)和Sp(specificity)均达到84%以上.

Detection of Exons with Deletions and Insertions by Hidden Markov Models

After more and more genome sequencing projects, like the "Human Genome Project", the prediction of genes, including their coding region and their regulatory region, has received a lot of attention. Softwares such as GENSCAN and GeneMark are powerful, but still do not meet the requirement of the practical application. The GENSCAN predicts exons accurately, if the sequences predicted does not have insertions and deletions in their coding regions. But if it does have, even only one, the prediction could be disturbed seriously and satisfactory results can not be obtained. A hidden Markov model with states of deletions, insertions and main state is introduced to find the error of deletions and insertions. The result shows that sensitivity and specificity in exon level are both higher than 84% on the Burset/Guigò test data set.