湖泊生态系统动力学模型研究进展

从系统分析在湖泊生态系统动力学研究中的作用出发,对湖泊生态系统的动力学建模过程、方法和软件等进行了总结.在此基础上,综述了国内外湖泊生态系统动力学模型的发展.从1960年代至今,湖泊生态系统动力学模型从简单的零维模型发展到复杂的水质水动力学生态综合模型和生态结构动力学模型,如LakeWeb模型.中国的湖泊生态系统动力学模型研究始于20世纪80年代,主要集中在滇池、太湖、东湖和巢湖等富营养化严重的湖泊以及其他水体.目前,已经开发一些软件用于湖泊生态系统动力学模拟,主要有CEQUALICM、WASP、AQUATOX、PAMOLARE、CAEDYM等,以及用来模拟湖泊能流的软件ECOPATH.湖泊生态系统动力学模型还在监测、数据共享和模型结构、参数选取和不确定性分析等方面存在不足,需在今后的研究中加以改进.     

抗HPV11 E2核酶的计算机设计

 借助计算机软件分析 ,设计出能特异性切割HPV11型 6 4 4ntE2mRNA的核酶 (ribozyme) .遵循Symon′s锤头状核酶结构和GUX剪切位点原则 ,靶序列存在 32个这样的剪切位点 .通过计算机软件分析出核酶的最佳剪切位点 ,并对底物及核酶的二级结构进行预测及进行相应基因生物学功能和基因同源性分析 ,筛选出 2个锤头结构核酶 .针对这两位点设计的核酶分别命名为RZ2 777和RZ32 81.计算机分析显示 ,两核酶与底物切点两翼碱基形成锤头状结构 ,切点所在基因序列具有相对松弛的二级结构 ,位于该基因重要生物功能区内 ,是核酶的理想攻击区域 .通过基因库检索 ,在已知人类基因排除了与上述两核酶切点两翼碱基有基因同源性序列的可能性 .将两核酶用于体外剪切实验取得了良好的实验结果 ,认为借助计算机分析可帮助尽快从多个剪切位点选择出最适核酶     

基准剂量软件BMDS中基准剂量下限的计算研究

基准剂量下限作为风险评估的参考点,是剂量评估的重要参数.对美国环境保护暑开发的基准剂量软件BMDS的2.2版中采用的似然比法计算基准剂量下限进行了研究,以二分Logistic模型为例,详细剖析了这种计算方法的运行机制,并将计算结果和BMDS软件的计算结果进行比较,验证了本文介绍方法的正确性.采用这种计算方法设计开发了我国的二分型基准剂量模型评估软件.     

KL距离的变长马尔可夫模型识别人类剪接位点

针对传统基因剪接位点识别方法具有所用到的序列长,且参数多的问题,论文提出了一种基于KL距离的变长马尔可夫模型(Kullback Leibler divergence-variable length Markovmodel,KL-VLMM)。该模型在变长马尔可夫模型的基础上进行改进,由KL距离代替原来的概率比值来判断序列扩展的方向,有效地提高了特征序列的识别能力,且模型阶数由二阶降为一阶,降低了算法的空间复杂度。利用人类剪接位点数据库N269,对该模型和其他传统方法的识别性能进行了比较。实验结果表明,采用KL-VLMM方法预测人类基因剪接位点的预测效果更好。     

基于线性混合模型的红松人工林一级枝条大小预测模拟

基于黑龙江省孟家岗林场60株人工红松955个标准枝数据,采用线性混合效应模型理论和方法,考虑树木效应,利用SAS软件中的MIXED模块拟合红松人工林一级枝条各因子(基径、枝长、着枝角度)的预测模型.结果表明： 通过选择合适的随机参数和方差协方差结构能够提高模型的拟合精度;把相关性结构包括复合对称结构CS、一阶自回归结构AR(1)及一阶自回归与滑动平均结构ARMA(1,1)加入到一级枝条大小最优混合模型中,AR(1)可显著提高枝条基径和角度混合模型的拟合精度,但3种结构均不能提高枝条角度混合模型的精度.为了描述混合模型构建过程中产生的异方差现象,把CF₁和CF₂函数加入到枝条混合模型中,CF₁函数显著提高了枝条角度混合模型的拟合效果,CF₂函数显著提高了枝条基径和长度混合模型拟合效果.模型检验结果表明：对于红松人工林一级枝条大小预测模型,混合效应模型的估计精度比传统回归模型估计精度明显提高.
     

秀丽隐杆线虫2型糖尿病模型研究进展

摘要：秀丽隐杆线虫是一种结构简单且与人类基因在功能上具有高度保守性的模式生物,因其特点鲜明,所以广泛应用于人类疾病研究中,并在2型糖尿病研究中备受关注。目前,2型糖尿病发病机制尚未完全明确,现有的治疗手段会对人体带来许多副作用。利用秀丽隐杆线虫建立2型糖尿病研究模型,与其他2型糖尿病细胞模型和动物模型相比会带来不同的研究策略。本文综述了近年国内外秀丽隐杆线虫模型在2型糖尿病中相关研究进展,为后续研究提供理论参考。     

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.     

应用统计方法确定真核基因外显子

研究了人类基因外显子、内含子的编码结构及D值,发现外显子、内含子间有显著不同,应用“剪接法”进行外显子定位,成功率达到74.6％,表明这一方法可在实际研究中应用。     

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.     

日本建成世界最大的人类基因库

《日本经济新闻》2 0 0 4年4月16日报道:日本经济产业省15日宣布,日本建成了世界上最大的人类基因数据库。该库收录了3至4万个人类基因中的约2 .1万个。该库数据精确,将有助于研究人类疾病,开发新药,进行生命科学的基础研究。从16日开始,该数据库将在因特网上无偿公开,与全世界     

Efficient decoding algorithms for generalized hidden Markov model gene finders

Background

The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity.

Results

As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN.

Conclusions

In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.     

Finding prokaryotic genes by the 'frame-by-frame' algorithm: targeting gene starts and overlapping genes

MOTIVATION: Tightly packed prokaryotic genes frequently overlap with each other. This feature, rarely seen in eukaryotic DNA, makes detection of translation initiation sites and, therefore, exact predictions of prokaryotic genes notoriously difficult. Improving the accuracy of precise gene prediction in prokaryotic genomic DNA remains an important open problem. RESULTS: A software program implementing a new algorithm utilizing a uniform Hidden Markov Model for prokaryotic gene prediction was developed. The algorithm analyzes a given DNA sequence in each of six possible global reading frames independently. Twelve complete prokaryotic genomes were analyzed using the new tool. The accuracy of gene finding, predicting locations of protein-coding ORFs, as well as the accuracy of precise gene prediction, and detecting the whole gene including translation initiation codon were assessed by comparison with existing annotation. It was shown that in terms of gene finding, the program performs at least as well as the previously developed tools, such as GeneMark and GLIMMER. In terms of precise gene prediction the new program was shown to be more accurate, by several percentage points, than earlier developed tools, such as GeneMark.hmm, ECOPARSE and ORPHEUS. The results of testing the program indicated the possibility of systematic bias in start codon annotation in several early sequenced prokaryotic genomes. AVAILABILITY: The new gene-finding program can be accessed through the Web site: http:@dixie.biology.gatech.edu/GeneMark/fbf.cgi CONTACT: mark@amber.gatech.edu.     

广义隐Markov模型在基因识别中的应用

广义隐Markov模型(GHMM)是基因识别的一种重要模型,但是其计算量比传统的隐Markov模型大得多,以至于不能直 接在基因识别中使用。根据原核生物基因的结构特点,提出了一种高效的简化算法,其计算量是序列长度的线性函数。在此 基础上,构建了针对原核生物基因的识别程序GeneMiner,对实际数据的测试表明,此算法是有效的。     

An empirical analysis of training protocols for probabilistic gene finders

Background  

Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters.     

GlimmerM,Exonomy and Unveil: three ab initio eukaryotic genefinders

We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.     

GeneMark.hmm: new solutions for gene finding.

The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark. hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.     

Identifying the 3'-terminal exon in human DNA.

MOTIVATION: We present JTEF, a new program for finding 3' terminal exons in human DNA sequences. This program is based on quadratic discriminant analysis, a standard non-linear statistical pattern recognition method. The quadratic discriminant functions used for building the algorithm were trained on a set of 3' terminal exons of type 3tuexon (those containing the true STOP codon). RESULTS: We showed that the average predictive accuracy of JTEF is higher than the presently available best programs (GenScan and Genemark.hmm) based on a test set of 65 human DNA sequences with 121 genes. In particular JTEF performs well on larger genomic contigs containing multiple genes and significant amounts of intergenic DNA. It will become a valuable tool for genome annotation and gene functional studies. AVAILABILITY: JTEF is available free for academic users on request from ftp://cshl.org/pub/science/mzhanglab/JTEF and will be made available through the World Wide Web (http://argon.cshl.org/).