基于质谱的定量蛋白质组学策略和方法研究进展

定量蛋白质组学已经成为组学领域研究的热点之一.相关实验技术和计算方法的不断创新极大地促进了定量蛋白质组学的飞速发展.常用的定量蛋白质组学策略按照是否需要稳定同位素标记可以分为无标定量和有标定量两大类.每类策略又产生了众多定量方法和工具,它们一方面推动了定量蛋白质组学的深入发展;另一方面,也在实验策略与技术的发展过程中不断更新.因此对这些定量实验策略和方法进行系统总结和归纳将有助于定量蛋白质组学的研究.本文主要从方法学角度全面归纳了目前定量蛋白质组学研究的相关策略和算法,详述了无标定量和有标定量的具体算法流程并比较了各自特点,还对以研究蛋白质绝对丰度为目标的绝对定量算法进行了总结,列举了常用的定量软件和工具,最后概述了定量结果的质量控制方法,对定量蛋白质组学方法发展的前景进行了展望.     

Improved techniques for sampling complex pedigrees with the Gibbs sampler

Markov chain Monte Carlo (MCMC) methods have been widely used to overcome computational problems in linkage and segregation analyses. Many variants of this approach exist and are practiced; among the most popular is the Gibbs sampler. The Gibbs sampler is simple to implement but has (in its simplest form) mixing and reducibility problems; furthermore in order to initiate a Gibbs sampling chain we need a starting genotypic or allelic configuration which is consistent with the marker data in the pedigree and which has suitable weight in the joint distribution. We outline a procedure for finding such a configuration in pedigrees which have too many loci to allow for exact peeling. We also explain how this technique could be used to implement a blocking Gibbs sampler.     

基于EM算法的遗传重组率估计方法

EM算法是在不完全信息资料下实现参数极大似然估计的一种通用方法．本文导出了双位点不同标记类型,包括共显性-共显性,共显性-显性和显性-显性3种模式下,估计遗传重组率的EM算法,以及获得重组率抽样方差的Bootstrap方法;并将之推广到部分个体缺失标记基因型(未检测到电泳谱带)下的重组率估计．通过大量Monte Carlo模拟研究发现: (1)连锁紧密时,样本容量对重组率的估计影响不大;连锁松散时,需要较大样本容量才可检测到连锁以及实现重组率的较精确估计．(2)用包含缺失标记的所有个体估计重组率比仅用其中的非缺失标记个体估计更准确,且可显著提高连锁检测的统计功效．     

The role of economic incentives and social norms in forest resource management

Forests are experiencing increasing pressure from human activities, which is leading to rapid deforestation and loss of ecosystem services. Although deforestation occurs for a wide range of reasons, one important reason is that landowners lack a long-term management view. I suggest that both economic incentives and social norms will work differently to reduce landowners’ motivation to deforest and to help landowners identify the long-term benefits that will be obtained by sacrificing short-term gain.     

The science of research: The principles underlying the discovery of cognitive and other biological mechanisms

Studies of cognitive function include a wide spectrum of disciplines, with very diverse theoretical and practical frameworks. For example, in Behavioral Neuroscience cognitive mechanisms are mostly inferred from loss of function (lesion) experiments while in Cognitive Neuroscience these mechanisms are commonly deduced from brain activation patterns. Although neuroscientists acknowledge the limitations of deriving conclusions using a limited scope of approaches, there are no systematically studied, objective and explicit criteria for what is required to test a given hypothesis of cognitive function. This problem plagues every discipline in science: scientific research lacks objective, systematic studies that validate the principles underlying even its most elemental practices. For example, scientists decide what experiments are best suited to test key ideas in their field, which hypotheses have sufficient supporting evidence and which require further investigation, which studies are important and which are not, based on intuitions derived from experience, implicit principles learned from mentors and colleagues, traditions in their fields, etc. Philosophers have made numerous attempts to articulate and frame the principles that guide research and innovation, but these speculative ideas have remained untested and have had a minimal impact on the work of scientists. Here, I propose the development of methods for systematically and objectively studying and improving the modus operandi of research and development. This effort (the science of scientific research or S2) will benefit all aspects of science, from education of young scientists to research, publishing and funding, since it will provide explicit and systematically tested frameworks for practices in science. To illustrate its goals, I will introduce a hypothesis (the Convergent Four) derived from experimental practices common in molecular and cellular biology. This S2 hypothesis proposes that there are at least four fundamentally distinct strategies that scientists can use to test the connection between two phenomena of interest (A and B), and that to establish a compelling connection between A and B it is crucial to develop independently confirmed lines of convergent evidence in each of these four categories. The four categories include negative alteration (decrease probability of A or p(A) and determine p(B)), positive alteration (increase p(A) and determine p(B)), non-intervention (examine whether A precedes B) and integration (develop ideas about how to get from A to B and integrate those ideas with other available information about A and B). I will discuss both strategies to test this hypothesis and its implications for studies of cognitive function.     

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments

Summary. A newly synthesized secretory protein in cells bears a special sequence, called signal peptide or sequence, which plays the role of “address tag” in guiding the protein to wherever it is needed. Such a unique function of signal sequences has stimulated novel strategies for drug design or reprogramming cells for gene therapy. To realize these new ideas and plans, however, it is important to develop an automated method for fast and accurately identifying the signal sequences or their cleavage sites. In this paper, a new method is developed for predicting the signal sequence of a query secretory protein by fusing the results from a series of global alignments through a voting system. The very high success rates thus obtained suggest that the novel approach is very promising, and that the new method may become a useful vehicle in identifying signal sequence, or at least serve as a complementary tool to the existing algorithms of this field.     

Differences in Characteristics of Reserve Network Selection Using Population Data Versus Habitat Surrogates

The use of species data versus environmental surrogates used in lieu of species data in systematic reserve site selection is still highly debated. We analyse in a case study whether and how the results of reserve network selection are affected by the use of species data versus habitat surrogates (habitat models) for qualitative (presence/absence) and quantitative (population size/habitat quality) information. In a model region, the post-mining landscape south of Leipzig/Germany, we used iterative algorithms to select a network for 29 animal target species from a basic set of 127 sites. The network results differ markedly for the two information types: depending on the representation goal, 18–45% of the selected sites chosen in response to one information type do not appear in the results for the other type. Given the availability of quantitative and hence deeper information, evaluation rules can be used to filter out the best habitats and the largest populations. In our model study, 0–40% less suitable areas were selected when instead of quantitative details only qualitative data were used. In view of various advantages and limitations of the two information types, we propose improving the methodological approach to the selection of networks for animal species by combining different information types.     

A de novo designed protein protein interface

As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid beta1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest ( approximately 300 microM), 2D-[(1)H,(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation.     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.