基于SNP 互作识别潜在冠心病致病基因

目的:基于全基因组关联分析(Genomewideassociationstudy,GWAS)数据与生物信息学方法,识别冠心病潜在致病基因。方法:利用生物信息学方法和GWAS数据,对单核苷酸多态性(SingleNucleotidePolymorphisms,SNP)进行疾病风险打分,依据特定距离阈值内的SNP-SNP互作关系,筛选出疾病相关SNP显著风险模块,识别潜在致病基因。结果:设定阈值20kb,经筛选获得279个SNP显著风险模块,映射到79个基因,文献验证率为71.01%。结论:基于SNP互作识别的潜在致病基因,能更加准确的分析冠心病的发生发展过程。     

基于蛋白质互作评价差异表达基因的重复性

利用高通量基因表达谱数据可以识别在肿瘤与正常组织中差异表达的基因,为研究癌机理提供重要的线索。目前,在研究同种癌型的不同实验中发现的差异表达基因的交叠比例很低。这种高通量基因表达谱数据低重复性的现象严重制约了基因芯片在癌症研究中的应用。然而,已有研究表明从研究同种癌型的不同实验数据中得到的不交叠的差异表达基因倾向于扰动相同的功能。因此,在评价差异表达基因重复性时,应考虑其在生物学功能上的一致性。本文结合基因共表达和蛋白质互作关系,设计了功能重复性指标来评价差异表达基因列表的可重复性。通过分析两套卵巢癌数据,发现对同种癌型得到的差异表达基因具有很高的功能一致性(p<0.0001)。结果表明,在功能水平上评估差异表达基因的一致性具有合理性。     

R-Avr基因互作介导的植物抗病性

Ｒ－Ａｖｒ基因互作介导的植物抗病性李汝刚范云六伍宁丰李茂林（中国农业科学院生物技术研究中心,北京１０００８１）植物在整个生长发育周期中面临着种类繁多的病原微生物袭击,但真正能引起植物病害的病原并不多,这表明植物能抵抗大多数病原微生物袭击,表现非亲合性...     

基因互作与基因型判定的代数方法

如何根据子代的表型判断基因互作的形式以及确定亲代的基因型，是遗传学上重要的也是困难的问题．本文在完全显性及不考虑非遗传因素的条件下，利用文[1]给出了解决上述问题的代数方法．在应用上，要比以往的方法更为优越．     

连锁条件下基因互作形式的判定

如何根据子代的表型判断基因互作形式,是遗传学上重要也是困难的问题.文[l～2］在自由组合条件下给出了判定方法.本文得到了基因连锁时互作形式的判定法则,它是文［l～2］的推广．     

Avr／Cf互作诱发番茄Pto互作因子Pti编码基因的表达

植物抗病基因Pto和Cf产物具完全不同的结构域,其细胞定位也不同。二决定的抗病性产生机制的异同令人关注。采用两种过敏性反应（hypersensitive response,HR）产生系统研究了Pro互作蛋白Pti4,Pti5和Pti6编码基因在Avr/Cf互作中的时序表达：（1）通过杂交方法获取同含互补基因对Avr4/Cf-4和Avr9/Cf-9的番茄（Lycoper-sicon esculentum Mill.）种子,常温下这些种子发芽后形成的苗产生HR坏死斑。（2）先将Avr/Cf苗置于33℃下培养,此时番茄苗生长正常,不形成HR坏死斑。然后将温度降至25℃,数小时内这些苗即形成HR坏死斑。不同方法研究结果均表明,随Avr/Cf苗中HR坏死斑的形成,Pti4、Pti5和Pti6均受显诱导表达。但它们的表达水平和动态不同,这些结果表明,这些Pti在功能上互补,可能同时涉及Pto和Cf决定性抗性的调节作用。     

有利基因和有利的基因互作能够提高籼粳杂种育性

利用一套籼粳交ＤＨ系与两份具有广亲和性的水稻光（温）敏核不育系测交,构建两个测交Ｆ１群体,同时用９２个多型性的ＲＦＬＰ标记分析了杂交亲本和另外３０个不同类型的籼、粳品种（对照组）的基因型。利用对照组籼或粳表型与分子标记的关联性,检测出４１个与籼粳分化高度相关的ＲＦＬＰ标记。结果表明,８７．８％的这种标记参与了对杂种优势显著或极显著的影响,表明在水稻的演化过程中,参与适应性进化的基因与控制经济产量的     

影响微生物互作的重要基因研究进展：以大肠杆菌为例

微生物在自然界中广泛存在,微生物间的相互作用对群落结构和功能有重要影响。目前已经对微生物相互作用的机制给予了很大的关注,通过高通量测序技术和统计学分析方法的结合可以定位获得影响菌株互作的重要基因。为了深入研究微生物相互作用的遗传机制,本文以大肠杆菌(Escherichia coli)为例,综述了与大肠杆菌运动性、耐药性、营养物质吸收和代谢调节相关的基因在互作条件下发挥的作用,并从这几个方面分别阐述了大肠杆菌互作遗传机制。总之,这些基因在大肠杆菌与其他微生物互作中发挥重要作用,同时增强了对细菌互作机制的理解,为今后研究更复杂的微生物群落互作遗传机制奠定了理论基础。     

植物基因互作型显性核不育材料的新假说

植物基因互作型显性核不育材料的新假说刘秉华（中国农业科学院作物育种栽培研究所北京１０００８１）植物雄性不育是一种常见的自然现象,有的受遗传基因控制,有的则由于外界环境条件影响所致。显性基因控制的雄性不育称为显性核不育。在通常情况下,显性核不育材料的异...     

影响水稻花药培养力的数量性状基因座位间的互作

控制数量性状的多个基因间不仅存在加性效应,还存在非等位基因间的互作。对一个籼粳交后代的加倍单倍体群体花药培养,通过Ｅｐｉｓｔａｔ软件分析影响水稻花药 数量性状基因座位间的互作。结果表明,愈伤组织诱导率主要受两个单基因的影响,不存在双基因条件型互作,但有２对互适型互作与其有关。     

PCR-CTPP:一种基于错配技术的SNP分型方法的改良

为探讨两对交叉引物PCR(PCR-CTPP)技术的原理并提高SNP分型的准确性,以人类MTHFR基因1298位点突变为例,通过设计合理的引物、优化引物终浓度及复性温度等重建PCR-CTPP检测系统,对比常规PCR-CTPP和改良的PCR-CTPP检测系统的可靠性。结果表明,改良的PCR-CTPP检测系统更加准确,支持了常规方法存在理论缺陷的观点;批量鉴定结果进一步验证了改良方法的可靠性。该技术有望在医学和分子生物学等领域广泛应用。     

Graphical exploration of gene expression data: a comparative study of three multivariate methods

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.     

MOLE: a data management application based on a protein production data model

MOLE (mining, organizing, and logging experiments) has been developed to meet the growing data management and target tracking needs of molecular biologists and protein crystallographers. The prototype reported here will become a Laboratory Information Management System (LIMS) to help protein scientists manage the large amounts of laboratory data being generated due to the acceleration in proteome research and will furthermore facilitate collaborations between groups based at different sites. To achieve this, MOLE is based on the data model for protein production devised at the European Bioinformatics Institute (Pajon A, et al., Proteins in press).     

Phred/Phrap/Consed/Polyphred:类Unix平台的测序数据管理和SNP识别软件包

尽管二代基因组测序技术日渐流行,Sanger测序依旧是SNP识别和分析的金标准。传统对于Sanger测序结果的分析多依赖Seq Man等软件进行。然而这类软件大多依靠人工操作来识别和记录测序结果中的SNP位点,效率低下且容易发生错误。此外,当对多个个体进行序列测定时,这类软件无法完成对群体数据的管理和输出,给研究人员造成了一定的不便。Phred/Phrap/Consed/Polyphred是华盛顿大学开发的基于类Unix平台的软件包,在大规模测序数据的管理和SNP自动识别、标记与输出方面具有强大的功能。然而,由于其安装和使用较为复杂,在国内较少使用。本研究对该软件包的功能、使用流程、特点等进行了介绍,并将其安装于Ubuntu12.04操作系统并置于VMware虚拟机中,方便遗传学者的下载和使用。     

Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false‐positive and false‐negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co‐complex relationship, and (3) pathway co‐membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity‐based k‐Nearest‐Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co‐complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top‐ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast‐2‐hybrid system were not among the top‐ranking features under any condition. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions

Knowledge discovery from the exponentially growing body of structurally characterised protein-ligand complexes as a source of information in structure-based drug design is a major challenge in contemporary drug research. Given the need for powerful data retrieval, integration and analysis tools, Relibase was developed as a database system particularly designed to handle protein-ligand related problems and tasks. Here, we describe the design and functionality of the Relibase core database system. Features of Relibase include, e.g. the detailed analysis of superimposed ligand binding sites, ligand similarity and substructure searches, and 3D searches for protein-ligand and protein-protein interaction patterns. The broad range of functions provided in Relibase and its high level of data integration, along with its flexible and intuitive interface, makes Relibase an invaluable data mining tool which can significantly enhance the drug development process. An example, illustrating a 3D query for quarternary ligand nitrogen atoms interacting with aromatic ring systems in proteins, a pattern found in pharmaceutically relevant target proteins such as, e.g. acetylcholine-esterase, is discussed.     

Analysing censored data in agricultural research: A review with examples and software tips

Metric data are usually assessed on a continuous scale with good precision, but sometimes agricultural researchers cannot obtain precise measurements of a variable. Values of such a variable cannot then be expressed as real numbers (e.g., 1.51 or 2.56), but often can be represented by intervals into which the values fall (e.g., from 1 to 2 or from 2 to 3). In this situation, statisticians talk about censoring and censored data, as opposed to missing data, where no information is available at all. Traditionally, in agriculture and biology, three methods have been used to analyse such data: (a) when intervals are narrow, some form of imputation (e.g., mid‐point imputation) is used to replace the interval and traditional methods for continuous data are employed (such as analyses of variance [ANOVA] and regression); (b) for time‐to‐event data, the cumulative proportions of individuals that experienced the event of interest are analysed, instead of the individual observed times‐to‐event; (c) when intervals are wide and many individuals are collected, non‐parametric methods of data analysis are favoured, where counts are considered instead of the individual observed value for each sample element. In this paper, we show that these methods may be suboptimal: The first one does not respect the process of data collection, the second leads to unreliable standard errors (SEs), while the third does not make full use of all the available information. As an alternative, methods of survival analysis for censored data can be useful, leading to reliable inferences and sound hypotheses testing. These methods are illustrated using three examples from plant and crop sciences.     

Incorporating data received after a sequential trial has stopped into the final analysis: implementation and comparison of methods

In a sequential clinical trial, accrual of data on patients often continues after the stopping criterion for the study has been met. This is termed "overrunning." Overrunning occurs mainly when the primary response from each patient is measured after some extended observation period. The objective of this article is to compare two methods of allowing for overrunning. In particular, simulation studies are reported that assess the two procedures in terms of how well they maintain the intended type I error rate. The effect on power resulting from the incorporation of "overrunning data" using the two procedures is evaluated.