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.     

隐马尔科夫过程在基因识别中的应用

用非数学语言描述了隐马尔科夫过程（hidden mark-ov model,HMM),介绍了HMM用于基因识别的原理及基于HMM开发的,比较常用的基因识别程序。     

一种新的基于两序列比对的ncRNA基因识别模型

利用上下文相关隐Markov模型建立ncRNA基因基本二级结构模型,从两序列比对结果中提取序列相似性信息,并把该信息引入基本的上下文相关Markov模型中,从而构建出新的ncRNA基因识别模型。     

隐半马氏模型在3'剪接位点识别中的应用

新近的基因识别软件比先前的软件有着显著的提高,但是在外显子水平上的敏感性和特异性仍然不十分令人满意.这是因为已有软件对于剪接位点,翻译起始等生物信号位点的识别还不够有效.如果能够分别提高这些生物信号位点的识别效果,就能够提高整体的基因识别效率.隐半马氏模型能够很好地刻画3'剪接位点(acceptor)的结构.据此开发的一套对acceptor进行识别的算法在Burset/Guigo的数据集上经过检验,获得了比已有算法更好的识别率.该模型的成功还使得我们对剪接点上游的分支位点和嘧啶富含区的概貌有了一定的认识,加深了人们对于acceptor的结构和剪接过程的理解.     

隐半马氏模型在 3′剪接位点识别中的应用(英文)

新近的基因识别软件比先前的软件有着显著的提高 ,但是在外显子水平上的敏感性和特异性仍然不十分令人满意 .这是因为已有软件对于剪接位点 ,翻译起始等生物信号位点的识别还不够有效 .如果能够分别提高这些生物信号位点的识别效果 ,就能够提高整体的基因识别效率 .隐半马氏模型能够很好地刻画 3′剪接位点 (acceptor)的结构 .据此开发的一套对acceptor进行识别的算法在Burset/Guigo的数据集上经过检验 ,获得了比已有算法更好的识别率 .该模型的成功还使得我们对剪接点上游的分支位点和嘧啶富含区的概貌有了一定的认识 ,加深了人们对于acceptor的结构和剪接过程的理解     

隐半马氏模型在3′剪接位点识别中的应用(英)

新近的基因识别软件比先前的软件有着显著的提高,但是在外显子水平上的敏感性和特异性仍然不十分令人满意.这是因为已有软件对于剪接位点,翻译起始等生物信号位点的识别还不够有效.如果能够分别提高这些生物信号位点的识别效果,就能够提高整体的基因识别效率.隐半马氏模型能够很好地刻画3′剪接位点(acceptor)的结构.据此开发的一套对acceptor进行识别的算法在Burset/Guigo的数据集上经过检验,获得了比已有算法更好的识别率.该模型的成功还使得我们对剪接点上游的分支位点和嘧啶富含区的概貌有了一定的认识,加深了人们对于acceptor的结构和剪接过程的理解.     

隐马尔可夫模型-改进的预测蛋白质二级结构方法

引入蛋白质二级结构预测的新方法：隐马尔可夫模型,其中将蛋白质的二级结构分成三类：H(指α-螺旋),E(β-折叠)及O(包括转角,卷曲及其结构)．该方法属于统计方法,但考虑了相邻氮基酸之间的相互作用(体现在状态传输概率)．通过模型的改进及参数的确定后,我们编制了程序HMMPS．用它来预测蛋白质二级结构,具有很高的准确度．其中关于H,F和O的准确率分别达到80．1％．72.0％和63．2％这表明．我们的方法是较为可靠的。     

隐马尔科夫过程在生物信息学中的应用

隐马尔科夫过程（hidden markov model,简称HMM）是20世纪70年代提出来的一种统计方法,以前主要用于语音识别。1989年Churchill将其引入计算生物学。目前,HMM是生物信息学中应用比较广泛的一种统计方法,主要用于：线性序列分析、模型分析、基因发现等方面。对HMM进行了简明扼要的描述,并对其在上述几个方面的应用作一概略介绍。     

HumGene:一个基于GHMM的人类基因预测软件

HumGene是一个采用广义隐Markov模型(GHMM)的人类基因预测软件.利用人类基因的结构特点,采用概率模型为基因结构中各个特定区域建立了独立的子模型,能够获得全局统一的评价指数,使得系统整体框架具有一定的扩展性.采用一种新的简化算法,有效地降低了计算的复杂度.介绍了软件的构成,对软件进行了测试,给出了与其它类似软件的结果比较.     

有报酬的Markov模型在尧勒甸子村土地利用结构分析中的应用

有报酬的Ｍａｒｋｏｖ模型在尧勒甸子村土地利用结构分析中的应用孙宏义,黄学文,王周龙,胡梦春,刘新民（中国科学院兰州沙漠研究所,兰州,７３００００）闫顺国（甘肃省草原生态研究所）ＡＰＰＬＩＣＡＴＩＯＮＯＦＭａｒｋｏｖＭＯＤＥＬＷＩＴＨＲＥＷＡＲＤＳＴＯ...     

A Latent Class Model with Hidden Markov Dependence for Array CGH Data

Summary Array CGH is a high‐throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class‐specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.     

MicroRNA target prediction based on second-order Hidden Markov Model

MicroRNAs are one class of small single-stranded RNA of about 22 nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3′ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.     

基于序列拓扑和二阶隐马尔可夫模型的跨膜蛋白亚细胞定位预测

现有蛋白质亚细胞定位方法针对水溶性蛋白质而设计,对跨膜蛋白并不适用。而专门的跨膜拓扑预测器,又不是为亚细胞定位而设计的。文章改进了跨膜拓扑预测器TMPHMMLoc的模型结构,设计了一个新的二阶隐马尔可夫模型;采用推广到二阶模型的Baum-Welch算法估计模型参数,并把将各个亚细胞位置建立的模型整合为一个预测器。数据集上测试结果表明,此方法性能显著优于针对可溶性蛋白设计的支持向量机方法和模糊k最邻近方法,也优于TMPHMMLoc中提出的隐马尔可夫模型方法,是一个有效的跨膜蛋白亚细胞定位预测方法。     

Prediction of Translation Initiation Sites on the Genome of Synechocystis sp. Strain PCC6803 by Hidden Markov Model

We developed a computer program, GeneHackerTL, which predictsthe most probable translation initiation site for a given nucleotidesequence. The program requires that information be extractedfrom the nucleotide sequence data surrounding the translationinitiation sites according to the framework of the Hidden MarkovModel. Since the translation initiation sites of 72 highly abundantproteins have already been assigned on the genome of Synechocystissp. strain PCC6803 by amino-terminal analysis, we extractednecessary information for GeneHackerTL from the nucleotide sequencedata. The prediction rate of the GeneHackerTL for these proteinswas estimated to be 86.1%. We then used GeneHackerTL for predictionof the translation initiation sites of 24 other proteins, ofwhich the initiation sites were not assigned experimentally,because of the lack of a potential initiation codon at the amino-terminalposition. For 20 out of the 24 proteins, the initiation siteswere predicted in the upstream of their amino-terminal positions.According to this assignment, the processed regions representa typical feature of signal peptides. We could also predictmultiple translation initiation sites for a particular genefor which at least two initiation sites were experimentallydetected. This program would be e.ective for the predictionof translation initiationsites of other proteins, not only inthis species but also in other prokaryotes as well.     

Hidden Markov Models and Animal Behaviour

This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time-trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher-order Markov chain models if a first-order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time-trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).     

A Sufficient Condition for Reducing Recursions in Hidden Markov Models

In hidden Markov models, the probability of observing a set of strings can be computed using recursion relations. We construct a sufficient condition for simplifying the recursion relations for a certain class of hidden Markov models. If the condition is satisfied, then one can construct a reduced recursion where the dependence on Markov states completely disappears. We discuss a specific example—namely, statistical multiple alignment based on the TKF-model—in which the sufficient condition is satisfied.