DNA序列进化过程中核苷酸替代的非独立性研究

本文评述了DNA序列间核苷酸替代数的估计方法,并通过对七个物种中组蛋白基因的比较对DNA进化的模型进行了考察。发现H2A基因第三位点上的碱基组成在物种间变异很大,并且跟H2A基因第一位点、H4基因第一、三位点及H2A上游,下游序列中的碱基组成有强正相关,提示DNA序列进化过程中存在着物种特异的区域性约束力。可能的原因是高等真核生物中GC含量升高,或者是染色体重组使这些同源序列位于不同的等质区段,从而受到不同的选择突变压。密码内各位点上核苷酸替代的相关性分析表明不同位点的替代是非独立的,其原因可能是一次替代事件引起多个位点的变化。文中讨论了这些结果对进化树推断的意义。     

DNA重排及体外分子进化

ＤＮＡ重排是目前为止最简便、最有效的体外定向进化技术,可以对单一基因、质粒、代谢途径、部分甚至整个基因组进行改造。本综述了ＤＮＡ重排的基本原理、特点、与其它体外进化技术的不同,着重介绍了其在体外分子进化上的广泛应用,并对应用前景进行了展望。     

真核生物DNA的进化

真核生物ＤＮＡ的进化刘振山（河北省张家口市教委科学研究室０７５０３１）生物在进化过程中的演变都是由ＤＮＡ的自发改变开始的,当生物从简单形式进化到较复杂的形式后,基因组无论是在遗传物质ＤＮＡ的含量上,还是在基因的数目上都有所扩大,遗传物质的这种量和质的...     

雀形目10种鸟类线粒体的DNA变异及分子进化

采用14种限制性内切酶（Apa I、BamHI、Bgl Ⅱ、EcoRI、EcoRV、HindⅢ、HpaI、KpnI、PstI、PuvⅡ、SalI、ScaI、XbaI和XhoI）对雀形目3科10种鸟类（蒙古百灵、喜鹊、小嘴乌雅、白腰朱顶雀、锡嘴雀、朱雀、红腹灰雀、灰腹灰雀、红交嘴雀和黄喉Wu）进行限制性片段长度多态分析（RFLP分析）。结果表明：雀形目鸟类基因组大小存在遗传多态性,不同类群在酶切类型上表现出各自的特点,雀形目鸟类与非雀形目鸟类在线粒体DNA的进化速率有着相同的特点,化石记录的地质年代与线粒体DNA分子时钟记录的年代有着惊人的吻合,这两个互为独立事件的统一,提示线粒体DNA作为分子进化的良好工具。     

DNA似近距离及进化时间的估算

在似近分析和Ｎｅｉ氏遗传距离的基础上,给出了ＤＮＡ似近距离计算公式,并以ＤＮＡ似近距离估算类群间的分歧时间（进化时间）,应用１０种限制内切酶对猕猴属（ｇｅｎｕｓ Ｍａｃａｃａ）内５个种ｍｔＤＮＡ的切点数据计算了这５个种的ＤＮＡ似近距离和进化时间,比较由ＤＮＡ似近距,遗传距离构建的歧化树和Ｆｏｏｄｅｎ及Ｄｅｌｓｏｎ的形态歧化树表明,除遗传距离的歧化树外,其它三种歧化树都有一个共同点,就是熊猴（Ｍ．ａ     

四种犬科（Canidae）动物线粒体DNA分子进化

以９种限制性内切酶分析家犬,狼,赤狐,貉共７只动物的一粒体ＤＮＡ限制性片段长度多态性（ｍｔＤＮＡＲＦＬＰ）,通过双酶解法构建各种动物的限制性内切酶图谱,用ＵＰＧ法,ＮＪ法和简略法分析这些动物之间的遗传关系。结果表明,１．犬的个体差异小,而赤弧的个体间遗传分化程度非常高;２．犬和狼的亲缘关系很近,应是同一种动物;３．在属间关系中,犬属与狐属的关系较接近,貉属是一个较早分化的独立分支。     

脊椎动物线粒体DNA的进化遗传学

近年来,在分子进化遗传学研究中又产生出一个新的生长点,这就是线粒体DNA(mtDNA)的进化遗传学研究。因为mtDNA结构简单,与拥有4×10~8到4×10~(11)个碱基对的多细胞动物的核基因组相比,比其最小者小25000倍;在不同物种间,mtDNA上的基因成分相对稳定,很少受到序列重排的影响;另一方面,mtDNA又具有广泛的种内和种间多态性,且为母性遗传,在亲缘关系相近的物种间其进化速度比核基因快,因而它为从分子水平上研究种群遗传学和进化遗传学提供了理想的研究对象。     

隼形目鹰科11种鸟类线粒体DNA分子进化的研究

采用ＡｐａⅠ,ＢａｍＨⅠ,ＢａｌⅡ,ＥｃｏＲⅠ,ＥｃｏＲ Ｖ,ＨｉｎｄⅢ,ＨｐａⅠ,ＫｐｎⅠ,ＰｓｔⅠ,ＰｖｕⅡ,ＳａｌⅠ,ＳｃａⅠ,ＸｂａⅠ和ＸｈｏⅠ等１４种限制性内切酶,对隼形目鹰科１１种鸟类（金雕、乌雕、草原雕、普通狂、大狂、鹊、白尾鹞、乌灰鹞、黑翅鸢、高山兀鹫）线粒体ＤＮＡ限制性片段长度多态性分析。结果表明：是遗传距离最小的是金雕和草原雕（Ｐ＝０．９６０）,最大的是金雕和兀鹫（Ｐ＝１９．１     

DNA拓扑异构酶Ⅱ与ⅠA的进化分析

DNA拓扑异构酶与断开DNA链和改变其拓扑结构有关。先前的研究认为,拓扑异构酶TypeⅡB是由TypeⅡA通过基因的复制、重组和缺失所造成。然而,本研究结果则显示,TypeⅠA与TypeⅡB的进化关系较近,而TypeⅡA与TypeⅡB的关系较远。因此,TypeⅡB可能是由TypeⅠA演化而来,或者说,TypeⅡB是TypeⅠA在古细菌中的特化形式;TypeⅡB可能是由TypeⅡA通过形成TypeⅠA后演化而成的,而不是由TypeⅡA直接通过基因的复制、重组和缺失所造成。在由TypeⅡA演化成为TypeⅠA,最后演化为TypeⅡB的过程中,DNA拓扑异构酶的催化机制也发生了变化从需要金属离子的协助,演化到了不需要镁离子的存在协助其催化。     

鱼类线粒体DNA的遗传与进化

{{@ convertAbstractHtml(article.abstractinfoCn, "cn")}}       

Markovian and non-Markovian protein sequence evolution: aggregated Markov process models

Over the years, there have been claims that evolution proceeds according to systematically different processes over different timescales and that protein evolution behaves in a non-Markovian manner. On the other hand, Markov models are fundamental to many applications in evolutionary studies. Apparent non-Markovian or time-dependent behavior has been attributed to influence of the genetic code at short timescales and dominance of physicochemical properties of the amino acids at long timescales. However, any long time period is simply the accumulation of many short time periods, and it remains unclear why evolution should appear to act systematically differently across the range of timescales studied. We show that the observed time-dependent behavior can be explained qualitatively by modeling protein sequence evolution as an aggregated Markov process (AMP): a time-homogeneous Markovian substitution model observed only at the level of the amino acids encoded by the protein-coding DNA sequence. The study of AMPs sheds new light on the relationship between amino acid-level and codon-level models of sequence evolution, and our results suggest that protein evolution should be modeled at the codon level rather than using amino acid substitution models.     

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。     

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).     

Evaluation and Estimation of Various Markov Models with Applications to Membrane Channel Kinetics

Hidden Markov modelling is a powerful and efficient digital signal processing strategy for extracting the maximum likelihood model from a finite length sample of noisy data. Assuming the number of states in the model is known, then the state levels, transition probabilities, initial state distribution and the noise variance can be estimated. We investigate the applicability of this technique in membrane channel kinetics not only as a parameter estimator, but also as an aid to discriminating between various model types according to their statistical likelihood. We survey three representative classes of channel dynamics, namely: aggregated Markov models, semi-Markov models (with asymptotically convergent transition probabilities), and coupled Markov models; reformulating each within a discrete-time hidden Markov model framework. We then provide numerical evidence of the effectiveness of the procedure using simulated channel data and hence show that the correct model, as well as the model parameters, can be discerned. We also demonstrate that the model likelihood can be used to indicate the approximate number of states in the model.     

An Inhomogeneous Markov Model to Fit the Migration Process

In this paper we study the migration process considering an inhomogeneous Markov model. This is a certain condition to investigate age-dependent population distributions, where the transition probabilities are not constant. We consider also a death process for a population alive in a region at age t and, as a result of this, combined transition probabilities between the states of the concerning Markov chain. The model has non-stationary distribution for t →∞, because the condition of ergodicity does not hold.     

Sequence alignments and pair hidden Markov models using evolutionary history

This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.     

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.     

Goodman et al.'s method for augmenting the number of nucleotide substitutions

Summary Statistical properties of Goodman et al.'s (1974) method of compensating for undetected nucleotide substitutions in evolution are investigated by using computer simulation. It is found that the method tends to overcompensate when the stochastic error of the number of nucleotide substitutions is large. Furthermore, the estimate of the number of nucleotide substitutions obtained by this method has a large variance. However, in order to see whether this method gives overcompensation when applied together with the maximum parsimony method, a much larger scale of simulation seems to be necessary.     

An expectation maximization algorithm for training hidden substitution models

We derive an expectation maximization algorithm for maximum-likelihood training of substitution rate matrices from multiple sequence alignments. The algorithm can be used to train hidden substitution models, where the structural context of a residue is treated as a hidden variable that can evolve over time. We used the algorithm to train hidden substitution matrices on protein alignments in the Pfam database. Measuring the accuracy of multiple alignment algorithms with reference to BAliBASE (a database of structural reference alignments) our substitution matrices consistently outperform the PAM series, with the improvement steadily increasing as up to four hidden site classes are added. We discuss several applications of this algorithm in bioinformatics.