BIOINFORMATICS ORIGINAL PAPER Systems biology A model

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博士后选择的10个基本原则

1.选择自己感兴趣的方向2.选责适合你工作和生活方式的实验室3.选择能够学到新技能的研究组4.有备份的计划，考虑至少做一主一副两个项目5.选择有明显成果的项目6.开始之前，就跟未来的老板确定第一作者的归属问题7.时间的考虑。

Postdoc是一个过度期，长短随人，随job market的变化而变，去一个有funding保证的组可以令你有更多回旋的余地8.考虑个人发展前景9.努力争取你自己的研究资金。

10.学会发现机遇Ten Simple Rules for Selecting a Postdoctoral Positionwodehongqi 发表于: 2007-1-04 17:00 来源: 考博网Philip E. Bourne*, Iddo FriedbergCitation: Bourne PE, Friedberg I (2006) Ten Simple Rules for Selecting a Postdoctoral Position. PLoS Comput Biol 2(11): e121 DOI:10.1371/journal.pcbi.0020121Published: November 24, 2006Copyright: © 2006 Bourne and Friedberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Philip E. Bourne is a professor in the Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America, and is Editor-in-Chief of PLoS Computational Biology. Iddo Friedberg is a research assistant in the Bioinformatics and Systems Biology program at the Burnham Institute for Medical Research, La Jolla, California, United States of America.* To whom correspondence should be addressed. E-mail: bourne@____________________________________________________________________________________You are a PhD candidate and your thesis defense is already in sight. You have decided you would like to continue with a postdoctoral position rather than moving into industry as the next step in your career (that decision should be the subject of another “Ten Simple Rules”).Further, you already have ideas for the type of research you wish to pursue and perhaps some ideas for specific projects. Here are ten simple rules to help you make the best decisions on a research project and the laboratory in which to carry it out.Rule 1: Select a Position that Excites YouIf you find the position boring, you will not do your best work—believe us, the salary will not be what motivates you, it will be the science.Discuss the position fully with your proposed mentor, review the literature on the proposed project, and discuss it with others to get a balanced view. Try and evaluate what will be published during the process of your research. Being scooped during a postdoc can be a big setback. Just because the mentor is excited about the project does not mean you that will be six months into it.Rule 2: Select a Laboratory That Suits Your Work and LifestyleIf at all possible, visit the laboratory before making a decision. Laboratories vary widely in scope and size. Think about how you like to work—as part of a team, individually, with little supervision, with significant supervision (remembering that this is part of your training where you are supposed to be becoming independent), etc. Talk to other graduate students and postdoctoral fellows in the laboratory and determine the work style of the laboratory. Also, your best work is going to be done when you are happiest with the rest of your life. Does the location of the laboratory and the surrounding environment satisfy your nonwork interests?Rule 3: Select a Laboratory and a Project That Develop New SkillsMaximizing your versatility increases your marketability. Balance this against the need to ultimately be recognized for a particular set of contributions. Avoid strictly continuing the work you did in graduate school. A postdoctoral position is an extension of your graduate training; maximize your gain in knowledge and experience. Think very carefully before extending your graduate work into a postdoc in the same laboratory where you are now—to some professionals this raises a red flag when they look at your resume. Almost never does it maximize your gain of knowledge and experience, but that can be offset by rapid and important publications.Rule 4: Have a Backup PlanDo not be afraid to take risks, although keep in mind that pursuing a risky project does not mean it should be unrealistic: carefully research and plan your project. Even then, the most researched, well-thought-out, and well-planned project may fizzle; research is like that. Then what? Do you have a backup plan? Consider working on at least two projects. One to which you devote most of your time and energy and the second as a fallback. The second project should be more of the “bread and butter” type, guaranteed to generate good (if n ot exciting) results no matter what happens. This contradicts Rule 1, but that is allowed for a backup plan. For as we see in Rule 5, you need tangible outcomes.Rule 5: Choose a Project with Tangible OutcomesThat Match Your Career GoalsFor a future in academia, the most tangible outcomes are publications, followed by more publications. Does the laboratory you are entering have a track record in producing high-quality publications? Is your future mentor well-respected and recognized by the community? Talk to postdocs who have left the laboratory and find out. If the mentor is young, does s/he have the promise of providing those outcomes? Strive to have at least one quality publication per year.Rule 6: Negotiate First Authorship before You StartThe average number of authors on a paper has continued to rise over the years: a sign that science continues to become more collaborative. This is good for science, but how does it impact your career prospects? Think of it this way. If you are not the first author on a paper, your contribution is viewed as 1/n where n is the number of authors. Journals such as this one try to document each author's contributions; this is a relatively new concept, and few people pay any attention to it. Have an understanding with your mentor on your likelihood of first authorship before you start a project. It is best to tackle this problem early during the interview process and to achieve an understanding; this prevents conflicts and disappointments later on. Don't be shy about speaking frankly on this issue. This is particularly important when you are joining an ongoing study.Rule 7: The Time in a Postdoctoral Fellowship Should Be FiniteMentors favor postdocs second only to students. Why? Postdocs are second only to students in providing a talented labor pool for the leastpossible cost. If you are good, your mentor may want you to postdoc for a long period. Three years in any postdoc is probably enough. Three years often corresponds to the length of a grant that pays the postdoctoral fellowship, so the grant may define the duration. Definitely find out about the source and duration of funding before accepting a position. Be very wary about accepting one-year appointments. Be aware that the length of a postdoc will likely be governed by the prevailing job market. When the job market is good, assistant professorships and suitable positions in industry will mean you can transition early to the next stage of your career. Since the job market even a year out is unpredictable, having at least the option of a three-year postdoc fellowship is desirable.Rule 8: Evaluate the Growth PathMany independent researchers continue the research they started during their postdoc well into their first years as assistant professors, and they may continue the same line of work in industry, too. When researching the field you are about to enter, consider how much has been done already, how much you can contribute in your postdoc, and whether you could take it with you after your postdoc. This should be discussed with your mentor as part of an ongoing open dialog, since in the future you may be competing against your mentor. A good mentor will understand, as should you, that your horizon is independence—your own future lab, as a group leader, etc.Rule 9: Strive to Get Your Own MoneyThe ease of getting a postdoc is correlated with the amount of independent research monies available. When grants are hard to get, so are postdocs. Entering a position with your own financing gives you a level of independence and an important extra line on your resume. This requires forward thinking, since most sources of funding come from a joint application with the person who will mentor you as a postdoc. Few graduate students think about applying for postdoctoral fellowships in a timely way. Even if you do not apply for funding early, it remains an attractive option, even after your postdoc has started with a different funding source. Choosing one to two potential mentors and writing a grant at least a year before you will graduate is recommended.Rule 10: Learn to Recognize OpportunitiesNew areas of science emerge and become hot very quickly. Getting involved in an area early on has advantages, since you will be more easily recognized. Consider a laboratory and mentor that have a track record in pioneering new areas or at least the promise to do so.AcknowledgmentsThe authors would like to thank Mickey Kosloff for helpful discussions.申请博士后的过程和申请博士有所不同，一般博士后的申请不需要toefl或gre成绩。

生物信息分析经常使用名词说明

生物信息分析经常使用名词说明生物信息学（bioinformatics）：综合运算机科学、信息技术和数学的理论和方式来研究生物信息的交叉学科。

包括生物学数据的研究、存档、显示、处置和模拟，基因遗传和物理图谱的处置，核苷酸和氨基酸序列分析，新基因的发觉和蛋白质结构的预测等。

基因组（genome)：是指一个物种的单倍体的染色体数量，又称染色体组。

它包括了该物种自身的所有基因。

基因（gene）：是遗传信息的物理和功能单位，包括产生一条多肽链或功能RNA所必需的全数核苷酸序列。

基因组学：（genomics)是指对所有基因进行基因组作图（包括遗传图谱、物理图谱、转录图谱）、核酸序列测定、基因定位和基因功能分析的科学。

基因组学包括结构基因组学（structural genomics)、功能基因组学（functional genomics)、比较基因组学(Comparative genomics)宏基因组学：宏基因组是基因组学一个新兴的科学研究方向。

宏基因组学（又称元基因组学，环境基因组学，生态基因组学等），是研究直接从环境样本中提取的基因组遗传物质的学科。

传统的微生物研究依托于实验室培育，元基因组的兴起填补了无法在传统实验室中培育的微生物研究的空白。

蛋白质组学（proteomics）：说明生物体各类生物基因组在细胞中表达的全数蛋白质的表达模式及功能模式的学科。

包括鉴定蛋白质的表达、存在方式(修饰形式)、结构、功能和彼此作用等。

遗传图谱：指通过遗传重组所取得的基因线性排列图。

物理图谱：是利用限制性内切酶将染色体切成片段，再依照重叠序列把片段连接称染色体，确信遗传标记之间的物理距离的图谱。

转录图谱：是利用EST作为标记所构建的分子遗传图谱。

基因文库：用重组DNA技术将某种生物细胞的总DNA 或染色体DNA的所有片断随机地连接到基因载体上,然后转移到适当的宿主细胞中，通过细胞增殖而组成各个片段的无性繁衍系（克隆），在制备的克隆数量多到能够把某种生物的全数基因都包括在内的情形下，这一组克隆的整体就被称为某种生物的基因文库。

【生物信息学】第三章 Internet网上生物信息学资源

ExPASy分子生物学服务器 : /
结构生物信息学研究联合实验室（RCSB）
结构生物信息学研究联合实验室（The Research Collaboratory for Structural Bioinformatics, RCSB）（ : /index.html）是一个非盈利性研究机构，主要致力于通过对生物大分子三维结构的研究进一步探索生物系统的功能。
NDB核酸数据库（Nucleic Acid Database）( : /NDB/ndb.html) NDB 数据库主要收集与发布核酸的结构信息。
此外，RCSB还在其网站上提供了其开发的结构分析工具、标准和教学服务信息等。
日本国立遗传学研究所
日本国立遗传学研究所（National Institute of Genetics， NIG）（ : nig.ac.jp/）创建于1949年，是日本遗传学各方面研究的中心研究机构及生命科学所有领域的研究基地。该研究所的著名数据库是日本DNA数据库（DNA Data Bank of Japan）即DDBJ（ : ddbj.nig.ac.jp/）。
蛋白质序列数据库：SWISS-PROT（蛋白质序列注释性数据库）、TrEMBL（计算机注释的蛋白质序列数据库）及InterPro等。
全部基因组数据库：Completed Genomics at the EBI。从人类基因组项目一开始，国际人类基因组序列联盟即向国际核酸序列数据库（DDBJ/EMBL/GenBank）提供人类序列草图数据。通过EBI服务器及时地向研究者提供大量的人类基因组序列信息，这些数据最终合并到EMBL 数据库中。
网上生物信息学资源
网上生物信息学资源
Internet不仅向其用户提供了全球范围的信息交流与快速通讯手段，其本身也具有极其丰富的信息资源，包括新闻、书刊杂志、数据库、计算机软件、多媒体资料等，也包括大量的生物信息学

01-Introduction to Bioinformatics(生物信息学国外教程2010版) PPT课件

Textbook
The course textbook has no required textbook. I wrote Bioinformatics and Functional Genomics (Wiley-Blackwell, 2nd edition 2009). The lectures in this course correspond closely to chapters.
The textbook website is: This has powerpoints, URLs, etc. organized by chapter. This is most useful to find “web documents” corresponding to each chapter.
I will make pdfs of the chapters available to everyone.
You can also purchase a copy at the bookstore, at (now $60), or at Wiley with a 20% discount through the book’s website .
Literature references
You are encouraged to read original source articles (posted on moodle). They will enhance your understanding of the material. Readings are optional but recommended.
Web sites
The course website is reached via moodle: /moodle (or Google “moodle bioinformatics”) --This site contains the powerpoints for each lecture, including black & white versions for printing --The weekly quizzes are here --You can ask questions via the forum --Audio files of each lecture will be posted here

Bioinformatics-2009-Yang-2236-43

BIOINFORMATICS ORIGINAL PAPER Vol.25no.172009,pages2236–2243doi:10.1093/bioinformatics/btp376 Systems biologyReconstruct modular phenotype-speciﬁc gene networks by knowledge-driven matrix factorizationXuerui Yang1,2,Yang Zhou3,Rong Jin3and Christina Chan1,2,3,∗1Department of Chemical Engineering and Materials Science,2Department of Biochemistry and Molecular Biology and3Department of Computer Science and Engineering,Michigan State University,East Lansing,MI48824,USA Received on March9,2009;revised on May19,2009;accepted on June9,2009Advance Access publication June19,2009Associate Editor:Trey IdekerABSTRACTMotivation:Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes.A popular structure learning method,Bayesian network inference,has been used to determine network topology despite its shortcomings,i.e. the high-computational cost when analyzing a large number of genes and the inefﬁciency in exploiting prior knowledge,such as the co-regulation information of the genes.To address these limitations, we are introducing an alternative method,knowledge-driven matrix factorization(KMF)framework,to reconstruct phenotype-speciﬁc modular gene networks.Results:Considering the reconstruction of gene network as a matrix factorization problem,weﬁrst use the gene expression data to estimate a correlation matrix,and then factorize the correlation matrix to recover the gene modules and the interactions between them.Prior knowledge from Gene Ontology is integrated into the matrix factorization.We applied this KMF algorithm to hepatocellular carcinoma(HepG2)cells treated with free fatty acids(FFAs).By comparing the module networks for the different conditions,we identiﬁed the speciﬁc modules that are involved in conferring the cytotoxic phenotype induced by palmitate.Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary,KMF can efﬁciently integrate gene expression data with prior knowledge,thereby providing a powerful method of reconstructing phenotype-speciﬁc gene networks and valuable insights into the mechanisms that govern the phenotype. Contact:krischan@Supplementary information:Supplementary data are available at Bioinformatics online.1INTRODUCTIONCellular activities are believed to be coordinately regulated by genes and proteins that function in complex networks.Disease states ensue upon abnormal regulation of cellular activities.Reconstructing the gene networks that give rise to the different phenotypes may provide insights into the cellular mechanisms involved(Said et al.,2004; Srivastava et al.,2007).Biological networks of protein–protein interaction(Han,2008),metabolic pathways(Ravasz et al.,2002) and transcriptional regulation(Ihmels et al.,2002)are modular in ∗To whom correspondence should be addressed.structure,enabling mutations to be isolated to speciﬁc modules without affecting the overall viability of the system(Jeong et al., 2000;Thieffry and Romero,1999;Yook et al.,2004).Since organized modularity is ubiquitous in biological systems,identifying the gene modules and their interplay in a modular network should help to provide insights into the differential mechanisms involved in normal versus disease states.Previously we identiﬁed that saturated free fatty acid(FFA), e.g.palmitate,induced cytotoxicity in liver cells,while unsaturated FFAs,e.g.oleate and linoleate,were not signiﬁcantly cytotoxic(Li et al.,2007a,b;Srivastava and Chan,2007;Yang and Chan,2009). Palmitate-induced cytotoxicity of liver cells has been implicated in the pathogenesis of many obesity-related metabolic disorders, such as fatty liver disease,non-alcoholic steatohepatitis(NASH) and non-alcoholic fatty liver disease(NAFLD)(Farrell and Larter, 2006;Scheen and Luyckx,2002).Tumor necrosis factor(TNF)-α, a proinﬂammatory cytokine often is involved,along with elevated FFA,in these diseases(Bruce and Dyck,2004),and further potentiates the cytotoxicity induced by palmitate(Li et al.,2007b; Srivastava and Chan,2007;Srivastava et al.,2007).To study the multi-faceted effects of palmitate and provide insights into potential mechanism of saturated FFA-induced alterations,we obtained gene expression proﬁles of hepatocellular carcinoma(HepG2)cells upon exposure to different FFAs and TNF-α,and applied a module-based gene network reconstruction method that integrates prior knowledge and phenotypic information.The proposed methodology consists of two phases.Theﬁrst phase,the‘gene selection phase’,selects a subset of genes that are relevant to the phenotype,palmitate-induced cytotoxicity,using a mixture regression model.The second phase,the‘network reconstruction phase’,clusters the selected genes into modules,and reconstructs a module network based upon the interactions between the modules.Selecting the genes that are potentially relevant to the desired metabolic/phenotypic response of the cells can be viewed as a feature selection problem(Ressom et al.,2008;Saeys et al.,2007), which is extensively studied in machine learning(Bhaskar et al., 2006;Inza et al.,2004).Most feature selection methods,such as the Wilcoxon’s rank sum test(Troyanskaya et al.,2002)and Fisher’s Discriminant Analysis(FDR;Chan et al.,2003),are data driven,and thus susceptible to the noise level of the microarray data.One strategy to ameliorate this problem is to incorporate domain knowledge and functional information of the genes(Phillip et al.,2004).Typically these knowledge-based methods qualitatively2236©The Author2009.Published by Oxford University Press.All rights reserved.For Permissions,please email:journals.permissions@ at Tsinghua University on January 25, 2015 /Downloaded fromModular gene network reconstructionincorporate the prior knowledge in post-processing the genes that are selected by the data-driven approaches.In the present work,we address this limitation with a Bayesian mixture regression model that quantitatively incorporates the prior knowledge of the gene functions,upfront,in the gene-selection phase(see Supplementary Methods for details).By this process250genes are selected. Clustering methods,such as Self Organizing Map(Toronen et al.,1999;Yin et al.,2006),hierarchical clustering(Eisen et al., 1998)and K-means(Ma et al.,2005),commonly used to identify gene modules,cannot uncover the interactions among the modules or clusters.To address this limitation,several studies integrated clustering methods with structure learning algorithms,such as graphical Gaussian modeling and Bayesian network learning(Li and Chan,2004;Segal et al.,2003;Toh and Horimoto,2002).These approaches are predominantly data-driven and thus susceptible to noise in the expression data,and suffer from the sparse data problem associated with limited number of experimental conditions(Husmeier,2003;Yu et al.,2004).Previous studies recognized the importance of exploiting prior knowledge in reconstructing networks with sparse and noisy expression data(Bar-Joseph et al.,2003;Berman et al.,2002; Hartemink et al.,2002;Ideker et al.,2001;Ihmels et al.,2002; Li and Yang,2004;Pilpel et al.,2001).Similarly,we developed a framework based on knowledge-driven matrix factorization, termed KMF,to exploit the domain knowledge and reconstruct modular gene networks.This framework views the gene network reconstruction as a matrix factorization problem.In brief,the pairwise correlation coefﬁcients between any two genes are computed from their expression data and used to construct a correlation matrix.This correlation matrix is decomposed into a product of three matrices,from which the gene modules and module interaction information are extracted.During this process, the Gene Ontology(GO)information is introduced as regularization in matrix factorization,which affects the decomposed matrices and eventually the derived gene modules and interaction among pared with the existing approaches for gene network reconstruction,the key features of the proposed KMF framework are:(i)derives both the gene modules and their interactions from a combination of expression data and GO information;(ii) incorporates the prior knowledge of co-regulation relationships into the network reconstruction using a regularization scheme;and (iii)presents an efﬁcient learning algorithm based on non-negative matrix factorization and semi-deﬁnite programming.Finally,although a number of algorithms have been developed for matrix factorization(Ding and He,2005;Lee and Seung,1999),this study distinguishes from the prior studies in that it incorporates the prior knowledge of the gene functions into the matrix factorization. In addition,unlike most matrix factorization methods that only identify gene clusters,the current framework derives both the gene modules and their interactions simultaneously.2METHODSKMF is a technique based on matrix factorization.Itﬁrst computes pairwise correlation between two genes based on their expression levels across different experimental conditions.The matrix of pairwise gene correlation, denoted by W,is approximated by the product of three matrices,M×C×M.A gene modular network,including gene modules and their interaction,is derived from the decomposed matrices M and C.We denote the gene expression data by X=(x1,x2,...,x n)where n isthe number of genes,and each x i=(x i,1,x i,2,...,x i,m)∈R m is the expressionlevels of the i-th gene measured under m conditions.We can compute thepairwise correlation between any two genes using statistical correlationmetrics such as Pearson correlation,mutual information andχ2-statistics.In our experiment,we use RBF kernel function.This computation resultsin a symmetric matrix W=[w i,j]n×n where w i,j measures the correlationbetween gene x i and x j.This estimated correlation matrix W providesvaluable information about the structure of the gene network since a high correlation w i,j between two genes x i and x j could suggest that:(i)genes x iand x j belong to the same module,or(ii)gene x i regulates the expressionlevels of gene x j or vice versa.To derive these two types of interactions simultaneously,we follow the framework of weighted non-negative matrix factorization(WNMF)of Ding and He(2005)and factorize W as follows:W≈M×C×Mwhere M is a matrix of size n×r and C is a matrix of size r×r,wherer n is the number of modules that can be determined empirically as wewill discuss later.Matrix M=[m i,j]n×r represents the memberships of the ngenes in r modules where m i,j≥0indicates the conﬁdence of assigning thei-th gene to the j-th module.Matrix C=[c i,j]r×r represents the relationshipsamong r modules where c i,j≥0indicates the conﬁdence of the two genemodules to interact(regulate)with each other.Note that in this study,wefocus on the undirected network since the gene module regulation matrix Cis symmetric.To determine the appropriate factorization of matrix W,weﬁrst deﬁne aloss function l d(W,MCM )that measures the difference between W and the factorized matrices M and C as follows:l d(W,MCM T)=||W−MCM ||2F=ni,j=1(W i,j−[MCM T]i,j)2Second,we regularize the solution of M using the prior knowledge from GO information.We encode the information within GO by a similarity matrix S,where S i,j≥0represents the similarity between two genes in their biological functions.The discussion of gene similarity by GO can be found in Jin et al. (2006).To ensure the modules to be consistent with the prior knowledgewithin the GO,we introduce another loss function l m(M,S)that measuresthe inconsistency between M and S as follows:l m(M,S)=rk=1m k L(S)m k=tr(M L(S)M)where m k is the k-th column of M matrix.L(S)is the combinatorial Laplacianof matrix S.The deﬁnition of combinatorial Laplacian and its application to regularize numerical solutions can be found in Chung(1997).Furthermore,we regularize the solution for C by the regularizer l c(C)=||C||2F.Regularizer,as already shown in statistical machine learningtheory(Scholkopf and Smola,2001),is important for improving the stabilityof solutions as well as the generalization error of statistical models.This regularizer enforces sparse regulation among the gene modules,and as pointout in Andrade et al.(2005),will result in a scale-free structure of the genemodule network.By combining the above factors together,we obtain thefollowing optimization problem:argminM∈R n×r,C∈R r×rl d(W,Z)+αl m(M,S)+βl c(C)s.t.C 0,C i,i=1,i=1,2,...,n,C i,j≥0,i,j=1,2,...,rM i,j≥0,i,j=1,2,...,n,Z=MCMWe solve the above optimization problem through alternating optimization.It alters the process of optimizing M withﬁxed C and theprocess of optimizing C withﬁxed M iteratively till the solution convergesto the local optimum(see Supplementary Methods).Furthermore,the key2237at Tsinghua University on January 25, 2015/Downloaded fromX.Yang et al.parameters that determine the outcome of the algorithm,i.e.α,βand the number of modules,are tuned automatically.In particular,bothαandβare determined by a supervised learning method;the number of modules is decided by a stability analysis.Further details can be found in the Supplementary Methods.The KMF algorithm was applied to toxic and non-toxic conditions, separately.It was also applied to the combination of both conditions.We denoted by C t and C n the interaction matrices of toxic and non-toxic conditions,respectively,and by C all the interaction matrix derived from all the conditions.In order to ensure that matrices C t and C n are comparable,we align C t and C n with C all.The alignment is achieved by linearly transforming C t(and C n)to minimize|C t−C all|2F(and|C n−C all|2F).Finally,we emphasize that although this framework follows the work of WNMF,it is different from WNMF in that it incorporates the prior knowledge of the gene functions by introducing regularizer l m(S,M),which not only results in a different objective function to be optimized but also a different method of optimization.3RESULTS AND DISCUSSIONWe applied the proposed KMF framework to HepG2cells cultured in different FFAs,with or without TNF-αfor24h(see Supplementary Methods for details).To reconstruct the network,250genes selected in the gene selection phase(see Supplementary Methods)were used. Prior knowledge can be incorporated to help reconstruct networks with sparse and noisy expression data(Bar-Joseph et al.,2003; Berman et al.,2002;Hartemink et al.,2002;Ideker et al.,2001; Ihmels et al.,2002;Li and Yang,2004;Pilpel et al.,2001).Typically, the prior knowledge of the gene interaction is encoded in a Bayesian prior,in which a high probability is given for each gene relationship derived from prior knowledge.By incorporating a Bayesian prior, Bayesian network(BN)analysis penalizes any gene relationship (i.e.gives a low score)when it violates the prior knowledge of the gene relationships,thus improving both the accuracy and efﬁciency of BN analysis.In this study,the prior knowledge of the genes is taken from the GO database.Although GO information does not directly reveal the gene relationships,nevertheless it does provide co-regulation relationships and functional information of the genes, both of which are still potentially useful for reconstructing gene networks.Unlike existing methods that apply the GO information to generate predeﬁned sets of genes based on supervised feature selection(Subramanian et al.,2005),our KMF algorithm applies an alternative unsupervised feature selection,which allows us to identify the feature genes when the classiﬁcation of the experimental conditions is unknown.In addition,KMF tunes the impact of the GO information on the model selection to obtain optimal results(see Section2).This is in contrast to the other methods where the GO information takes precedence over the subsequent analysis(Srivastava et al.,2008).The KMF algorithm yields two matrices,M and C.M is the module matrix.Each element M i,j in matrix M represents the conﬁdence of assigning the i-th gene to the j-th module.We can derive the member genes for each module by assigning each gene i to the module j∗with the highest weight,i.e.j∗=argmax1≤j≤m M i,j. These member genes will furthermore allow us to infer the overall biological functions of each module.C is the network structure matrix that indicates the connectivity between gene modules.In particular,each element C i,j in matrix C represents the strength of the interaction between modules i and j.The interaction information revealed by the C matrix may shed light onto how biological Table1.Gene modules identiﬁed by KMFModule Function1Lipid metabolism and lipid processing.2Signaling proteins,intracellular and membraneprotein-mediated:GPCR signaling,chemokine/TNF-αreceptor signaling and ion channel-related signaling.3Glucose metabolism:glycolysis and pentose phosphatepathway.4Post-translational modiﬁcation:ubiquitin-proteasomepathway,protein folding,transportation,phosphorylation. 5Reactive oxygen species(ROS)homeostasis,redox systemregulation and the TCA cycle.6Energy:ATP and GTP metabolism.7Protein synthesis:translation initiation and transcription.8Amino acid metabolism and urea cycle.9Apoptosis:executors and regulators.information is processed and passed between different cellular activities.Furthermore,comparing the C matrices for the different conditions suggests structural changes in the module network in response to the toxic conditions,and these changes may confer the cytotoxic phenotype.3.1Application of KMF to identify gene modules andthe interactions between the modulesNine gene modules are identiﬁed by the proposed.We observe that the identiﬁed modules are highly enriched with genes involved in speciﬁc cellular functions or activities(Table1).A full list of the genes in each module is available online at /groups/chan/GO_KMF_genecluster.xls. Next,KMF identiﬁed the interactions between the modules,namely the connections between different cellular functions,in the form of the C matrix(Table2),and thereby recovered a module network (Fig.1).The bottom row(‘sum’)of Table2sums the correlation coefﬁcients(C i,j)between a module with the other eight modules, thereby capturing an overall snapshot of the module connections.A higher‘sum’value indicates that the module is more highly correlated with the other modules and thereby takes a more central position in the overall gene module network.A map of the module network is provided in Figure1,where the strengths of the interactions between the gene modules are indicated by both darkness and thickness of the edges.From the C matrix(Table2)and the module interaction network (Fig.1),module6(ATP and GTP metabolism)has the highest‘sum’value among the nine modules,and is presented as the largest node in the module interaction map.Indeed,as the molecular currency of intracellular energy transfer,ATP(as well as GTP)is either produced or consumed by most cellular activities,e.g.metabolism (catabolism and anabolism)and signaling pathways.Module6has the highest interaction values with modules3,5and8in the C matrix,reﬂecting that glucose metabolism(module3)and TCA cycle(module5)are the major metabolic pathways that produce ATP,the electron transport chain(ETC)(module5)produces the proton gradient across the mitochondria membrane to provide the driving force for ATP production(Lehninger et al.,2005),and amino acid metabolism(module8)is highly dependent on the ATP2238 at Tsinghua University on January 25, 2015 /Downloaded fromModular gene network reconstructionTable 2.C matrix of the modules Module 12345678910.1520.2340.1950.1910.2750.1010.2360.17620.1520.1770.1550.1520.2140.0920.1830.14030.2340.1770.2360.2150.3050.1070.2840.20940.1950.1550.2360.2040.2950.1200.2490.18850.1910.1520.2150.2040.3020.1220.2530.18660.2750.2140.3050.2950.3020.1700.3600.26770.1010.0920.1070.1200.1220.1700.1380.10880.2360.1830.2840.2490.2530.3600.1380.22790.1760.1400.2090.1880.1860.2670.1080.227Sum 1.560 1.265 1.767 1.642 1.625 2.1880.958 1.930 1.501Elements in rows 1–9represent the interaction strength between modules.The bottom row (sum)is the summation of eachcolumn.Fig.1.Gene module interaction network.Interactions among the nine gene modules are visualized according to the C matrix.The nodes represent modules and the edges indicating the strength of the interaction between modules.A higher C i ,j value in the C matrix,suggesting stronger interaction,is indicated by a thicker and darker edge line,whereas a higher ‘sum’valuein the C matrix,suggesting more relevant module,is indicated by a larger and darker node.levels.Therefore,from the example of module 6,KMF recovered a high connectivity between ATP (and GTP)synthesis and the major cellular activities that are known to be related to energy production and consumption.3.2Application of KMF to identify the interactions involved in palmitate-induced cytotoxicity KMF,if applied to the different conditions separately,yields different C matrices speciﬁcally for the toxic (saturated FFAs and TNF-α,see Supplementary Table 1)and non-toxic (control,unsaturated FFAs and TNF-α,see Supplementary Table 2)conditions.This is in contrast to the average C matrix obtained using all the conditions discussed above (Table 2).Similarly,these condition-speciﬁc C matrices indicate module networks composed of interactions between cellular activities for their corresponding condition.The C matrix in the toxic conditions differs signiﬁcantly from the non-toxic conditions,suggesting that the interactions between the gene modules in the toxic (saturated FFAs and TNF-α)case are altered signiﬁcantly,and these changes potentially may help to explain the phenotype,palmitate-induced cytotoxicity.To quantitatively assess these changes,we subtracted the C matrix for the non-toxic conditions from the C matrix for the toxicconditions,and obtained a matrix we denoted as the ‘difference C matrix’(Table 3).This matrix indicates the differences in theinteractions between the gene modules for the toxic versus the non-toxic conditions.Positive values indicate stronger interactions between the modules under the toxic than the non-toxic conditions,and vice versa.The summation of each column in the difference C matrix (the row denoted as ‘sum’in Table 3)indicates the difference between the toxic and the non-toxic conditions in the interactions of a module with the other modules.As shown in the difference C matrix (Table 3),modules 2,3,4and 5are more highly connected to the other modules,while modules 6and 9are less connected to the other modules in the toxic than in the non-toxicconditions.Since modules 4and 6have the largest positive andnegative ‘sum’values,0.144and −0.294,respectively,we focused on these two modules in the discussion of their potential involvementin palmitate-induced cytotoxicity (Supplementary Discussion).Inbrief,the ubiquitin-proteasome pathway and post-translationalmodiﬁcations (folding/unfolding,transportation and degradation)of proteins,module 4,was identiﬁed to be important in saturatedFFA-induced cytotoxicity,which is supported by the literature (Dinget al.,2007;Guo et al.,2007;Lai et al.,2008;Zhang et al.,2006).In contrast,module 6,ATP metabolism,was suggested to be lesscorrelated with the other cellular processes in the toxic than non-toxic conditions.Indeed long-term exposure of saturated FFAs canactivate uncoupling proteins (UCP)(Lameloise et al.,2001),whichuncouple mitochondrial oxidative phosphorylation and produce heatinstead of ATP (Breen et al.,2006).As a result,with this additionalregulation through UCPs,the level of ATP should be less connectedwith the cellular activities in the toxic than the non-toxic conditions.The proposed KMF algorithm identiﬁed the gene modules and their interactions,as well as how they change in the toxic versus non-toxic conditions.The results suggested that post-translational modiﬁcation and uncoupling proteins (UCP)play important roles in mediating the palmitate/TNF-αinduced cellular responses,therebyshedding light on potential mechanisms involved in palmitate-induced cytotoxicity.Thus far,this methodology has focused onthe module network.To further uncover the speciﬁc genes that may be responsible for the palmitate-induced cytotoxicity,we performed further analysis to assess the contribution of each gene in the two gene modules that were deemed important.3.3Identifying potential genes responsible forpalmitate-induced cytotoxicityAs described above,the values in the M matrix (M i ,j )indicate the strength or contribution of gene i to module j .The rank of the genes in a module by their M i ,j values provides a relative index of the importance of a gene to the cellular function that corresponds to that module.Under different conditions,the modules remained relatively stable with respect to their size and gene members,however,the rank of certain genes changed signiﬁcantly in some of the modules.The importance or the weights of these genes in their corresponding modules varied across the different conditions,suggesting that these genes may play important roles in conferring a phenotype.Given the importance of modules 4(post-translational modiﬁcation of proteins)and 6(ATP and GTP metabolism),we ranked the genes in these two modules according to their M i ,j values in the toxic conditions.The top 10out of 33genes in2239 at Tsinghua University on January 25, 2015/Downloaded fromX.Yang et al.Table3.Difference C matrix.Obtained by subtracting the C matrix of the non-toxic conditions(Supplementary Table2)from the C matrix of the toxic conditions(Supplementary Table1)Module123456789 10.0060.0160.0230.008−0.050−0.0160.006−0.007 20.0060.0370.0310.017−0.017−0.0070.023−0.002 30.0160.0370.0380.039−0.053−0.0010.0100.011 40.0230.0310.0380.034−0.0240.0070.0270.008 50.0080.0170.0390.034−0.0210.0110.015−0.005 6−0.050−0.017−0.053−0.024−0.021−0.010−0.062−0.057 7−0.016−0.007−0.0010.0070.011−0.0100.003−0.027 80.0060.0230.0100.0270.015−0.0620.003−0.010 9−0.007−0.0020.0110.008−0.005−0.057−0.027−0.010Sum−0.0140.0880.0970.1440.098−0.294−0.0400.012−0.089 The largest positive(0.144)and negative(−0.294)sum values are marked in bold.Table4.Top10out of33genes in module4ranked according to their contributions to the module under toxic conditionsRankToxic Non-toxic Difference Gene143LCMT253MAP3K12 330PRSS242−2ST13 52520HSP105B 660APOC1 73023RABGGTA 8146UVRAG97−2DPM2 102313MAPKAPK3 The ranking difference was calculated by subtracting the ranking number of the speciﬁc gene under toxic conditions from non-toxic conditions.Positive ranking differences indicate bigger ranking numbers and less contribution in non-toxic conditions.The two genes with the highest Difference ranks are highlighted in grey.module4and all the genes in module6are listed in Table4and Supplementary Table3,respectively.The ranking numbers of these genes in the toxic and non-toxic conditions are listed,as is the difference in the rankings of the genes between these conditions (Table4and Supplementary Table3).In module4,the positions of two genes,Rab geranylgeranyltrans-ferase(RABGGTA)and heat shock105kDa(HSP105B)changed signiﬁcantly in the toxic conditions,as indicated by high positive differences in the ranking,23and20,respectively,suggesting that these two genes may be more involved in the toxic than non-toxic conditions and thereby play a role in conferring the toxic phenotype. RABGGT catalyzes the transfer of a geranyl–geranyl moiety from geranyl–geranyl pyrophosphate to Rab proteins(GTPases)such as RAB1A,RAB3A and RAB5A(Leung et al.,2006).As a member of the Ras superfamily of monomeric G proteins,Rab proteins regulate membrane trafﬁc,which facilitates the trafﬁcking of cell membrane proteins from the Golgi apparatus to the plasma membrane and the recycling of the membrane proteins(Seabra et al., 2002;Stenmark and Olkkonen,2001).RABGGT,by facilitating the prenylation of Rab proteins(Leung et al.,2006),ensures that the Rab proteins are insoluble and correctly anchored in the membrane. The response of RABGGT to saturated FFAs and its potential role,if any,in the saturated FFA-induced cytotoxicity has never been studied.The mRNA level of RABGGT is not affected by oleate and increased by palmitate albeit insigniﬁcantly(Fig.2a, see Supplementary Methods for the details of the experiments). However,further analysis by silencing the gene expression level of RABGGT revealed a very interesting feature of RABGGT in regulating cytotoxicity(Fig.2b).In the non-toxic conditions, i.e.BSA(vehicle of the FFAs)or oleate,the LDH release was increased by the siRNA of RABGGT(Fig.2b),suggesting that RABGGT may help to maintain normal healthy cellular activities under physiological and non-toxic conditions.Indeed,membrane trafﬁc pathways,regulated by RABGGT through Rab GTPases,are important in maintaining normal vesicle formation and movement and membrane protein trafﬁcking and recycling.In contrast,in the toxic condition,i.e.palmitate,the LDH release was decreased by the siRNA of RABGGT(Fig.2b),suggesting that RABGGT may be involved in mediating the cytotoxic effect of palmitate.The potential mechanism of the distinct roles of RABGGT under the different conditions is unclear at this point.Given that RABGGT catalyzes the prenylation and therefore the activation of Rab GTPases, we hypothesize that the toxic conditions(i.e.palmitate)induce disordered trafﬁcking and recycling of the membrane proteins and disrupt the membrane integrity,through RABGGT and Rab proteins, thereby enhancing the cytotoxicity.As discussed in the Supplementary Discussion,as an important chaperone protein involved in processing denatured proteins under stress conditions(Yamagishi et al.,2000,2003),HSP105B may also be involved in the cellular responses induced by the toxic conditions, potentially by regulating the post-translational modiﬁcations,such as denaturation,folding/unfolding,transportation and degradation. Indeed,we found that both the mRNA(Supplementary Fig.1A) and protein(Supplementary Fig.1B)expression levels of HSP105B were signiﬁcantly increased by palmitate but not by oleate, suggesting that this gene potentially plays a role in the cytotoxicity induced by saturated FFA.HSP105B usually exists as a complex associated with Hsp70and Hsc70(a constitutive member of the HSP70family)in mammalian cells and functions as a negative regulator of Hsp70/Hsc70by suppressing the Hsp70/Hsc70 chaperone activity(Yamagishi et al.,2000).More detailed2240 at Tsinghua University on January 25, 2015 /Downloaded from。

Bioinformatics信息

Bioinformatics生物信息学From Wikipedia, the free encyclopedia维基百科，自由的百科全书Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data.生物信息学是开发用于了解生物数据的方法和软件工具一个跨学科领域。

随着科学的跨学科领域，生物信息学结合计算机科学，统计学，数学和工程学的研究和处理生物数据。

Bioinformatics is both an umbrella term for the body of biological studies that use computer programming as part of their methodology, as well as a reference to specific analysis "pipelines" that are repeatedly used, particularly in the fields of genetics and genomics. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences.生物信息学无论是对于使用计算机编程作为方法的一部分，以及对重复使用的，特别是在遗传学和基因组学领域的具体分析“管道”的引用生物学研究的主体的统称。

几种常用生物分析软件的特点及其使用简介

几种常用生物分析软件的特点及其使用简介韦荣编1　邱高峰1　张源2(1上海水产大学渔业学院,上海200090;2上海水产大学工程学院,上海200090)摘　要:　基于代表性、实用性的原则选择了RAPDistance ,PHY L IP ,MEG A ,TREECON ,AMOVA ,DIPLO 2MO 和MAPMA KER 等7种系统发育及遗传图谱构建方面的免费共享生物分析软件,简要介绍了它们各自的特点、功能、使用及获得的方法。

以期这些软件对于从事生物技术研究的人员具有一定的指导性,可操作性。

关键词:　软件　生物信息学　使用简介Features and B rief Introduction to the Applicationof Some Common Used Biosoft w aresWei Rongbian 1　Qiu G aofeng 1　Zhang Yuan 2(1Fisheries College ,S hanghai Fisheries U niversity ,S hanghai 200090;2Engi neeri ng College ,S hanghai Fisheries U niversity ,S hanghai 200090)Abstract :　In this article ,some common used free biosoftwares about phylogeny and genetic map constructing ,i.e.RAPDistance ,PHY L IP ,MEG A ,TREECON ,AMOVA ,DIPLOMO and MAPMA KER are selected based on the prin 2ciple of representativity and utility to briefly introduce their features ,function ,application and means of getting them.The methods in the article are expected to be instructive and operational for the researchers on biotechnology.K ey words :　S oftware Bioinformatics Application introduction 随着IT 技术的不断发展,它已经越来越深刻地渗透到各门学科中,包括生命科学。

生物信息学复习的总结

生物信息期末总结1.生物信息学〔Bioinformatics〕定义：〔第一章〕★生物信息学是一门交叉科学，它包含了生物信息的获取、加工、存储、分配、分析、解释等在内的所有方面，它综合运用数学、计算机科学和生物学的各种工具来说明和理解大量数据所包含的生物学意义。

〔或：〕生物信息学是运用计算机技术和信息技术开发新的算法和统计方法，对生物实验数据进展分析，确定数据所含的生物学意义，并开发新的数据分析工具以实现对各种信息的获取和管理的学科。

〔NSFC〕2. 科研机构与网络资源中心：NCBI：美国国立卫生研究院NIH下属国立生物技术信息中心；EMBnet：欧洲分子生物学网络；EMBL-EBI：欧洲分子生物学实验室下属欧洲生物信息学研究所；ExPASy:瑞士生物信息研究所SIB下属的蛋白质分析专家系统；(Expert Protein Analysis System)Bioinformatics Links Directory；PDB (Protein Data Bank)；UniProt 数据库3. 生物信息学的主要应用:1．生物信息学数据库；2．序列分析；3．比拟基因组学；4．表达分析；5．蛋白质结构预测；6．系统生物学；7．计算进化生物学与生物多样性。

4.什么是数据库：★1、定义：数据库是存储与管理数据的计算机文档、结构化记录形式的数据集合。

〔记录record、字段field、值value〕2、生物信息数据库应满足5个方面的主要需求：〔1〕时间性；〔2〕注释；〔3〕支撑数据；〔4〕数据质量；〔5〕集成性。

3、生物学数据库的类型：一级数据库和二级数据库。

〔国际著名的一级核酸数据库有Genbank数据库、EMBL核酸库和DDBJ库等；蛋白质序列数据库有SWISS-PROT等；蛋白质结构库有PDB等。

〕4、一级数据库与二级数据库的区别：★1〕一级数据库：包括：a.基因组数据库----来自基因组作图；b.核酸和蛋白质一级结构序列数据库；c.生物大分子(主要是蛋白质)的三维空间结构数据库，(来自X-衍射和核磁共振结构测定)；2〕二级数据库：是对原始生物分子数据进展整理、分类的结果，是在一级数据库、实验数据和理论分析的根底上针对特定的应用目标而建立的。

生物信息学期刊及影响因子汇总

Nature Communications 10.742，综合：一区，12.124Nucleic Acid Research 8.808，生物：一区；10.162Scientific Reports 5.078，综合类：二区，4.259Methods 3.221，生物，二区，3.802BMC Systems Biology 2.853，生物：3区，2.003Journal of Biomedical informatics 2.482，医学：3区；2.753Journal of Computational Biology 1.670，生物：四区；1.0320.931，生物：四区；0.800 Journal of Bioinformatics and Computational Biology 0.931Journal of Theoretical Biology1.833 生物、数学与计算生物学三区Journal of Mathematical Biology 1.786 生物4区数学与计算生物学3区Bioinformatics (IF: 4.328)BMC Bioinformatics (IF: 3.781)，生物：三区；2.448BMC Biology (IF: 4.734)，生物：一区；6.779Briefings in Bioinformatics (IF: 4.627)，生物：一区；5.134Journal of Computational Biology (IF: 1.563)，生物：四区；1.032PLoS Computational Biology (IF: 5.895)，生物：二区，2.542 Mathematical Biosciences (IF: 1.148)，生物：四区；1.246Journal of Biomedical Informatics (IF: 1.924)，医学：三区；2.753，生物：三区；2.781Molecular Biosystems ( IF: 4.236)，生物：三区；Frontiers in Genetics 4.151 生物二区生物：二区，微生物3区Microbial Ecology 3.614 生物：二区，微生物Frontiers in Microbiology4.019生物、微生物2区Frontiers in Cellular and Infection Microbiology3.520医学、微生物2区Research in Microbiology 2.372 生物、微生物3区。

生物信息学主要英文术语及释义

生物信息学主要英文术语及释义Abstract Syntax Notation (ASN.l)（NCBI发展的许多程序，如显示蛋白质三维结构的Cn3D 等所使用的内部格式）A language that is used to describe structured data types formally, Within bioinformatits,it has been used by the National Center for Biotechnology Information to encode sequences, maps, taxonomic information, molecular structures, and biographical information in such a way that it can be easily accessed and exchanged by computer software.Accession number（记录号）A unique identifier that is assigned to a single database entry for a DNA or protein sequence. Affine gap penalty（一种设置空位罚分策略）A gap penalty score that is a linear function of gap length, consisting of a gap opening penalty and a gap extension penalty multiplied by the length of the gap. Using this penalty scheme greatly enhances the performance of dynamic programming methods for sequence alignment. See also Gap penalty.Algorithm（算法）A systematic procedure for solving a problem in a finite number of steps, typically involving a repetition of operations. Once specified, an algorithm can be written in a computer language and run as a program.Alignment（联配/比对/联配）Refers to the procedure of comparing two or more sequences by looking for a series of individual characters or character patterns that are in the same order in the sequences. Of the two types of alignment, local and global, a local alignment is generally the most useful. See also Local and Global alignments.Alignment score（联配/比对/联配值）An algorithmically computed score based on the number of matches, substitutions, insertions, and deletions (gaps) within an alignment. Scores for matches and substitutions Are derived from a scoring matrix such as the BLOSUM and PAM matrices for proteins, and aftine gap penalties suitable for the matrix are chosen. Alignment scores are in log odds units, often bit units (log to the base 2). Higher scores denote better alignments. See also Similarity score, Distance in sequence analysis.Alphabet（字母表）The total number of symbols in a sequence-4 for DNA sequences and 20 for protein sequences. Annotation（注释）The prediction of genes in a genome, including the location of protein-encoding genes, the sequence of the encoded proteins, anysignificantmatches to other Proteins of known function, and the location of RNA-encoding genes. Predictions are based on gene models; e.g., hidden Markov models of introns and exons in proteins encoding genes, and models of secondary structure in RNA.Anonymous FTP（匿名FTP）When a FTP service allows anyone to log in, it is said to provide anonymous FTP ser-vice. A user can log in to an anonymous FTP server by typing anonymous as the user name and his E-mail address as a password. Most Web browsers now negotiate anonymous FTP logon without asking the user for a user name and password. See also FTP.ASCIIThe American Standard Code for Information Interchange (ASCII) encodes unaccented letters a-z, A-Z, the numbers O-9, most punctuation marks, space, and a set of control characters such as carriage return and tab. ASCII specifies 128 characters that are mapped to the values O-127. ASCII tiles are commonly called plain text, meaning that they only encode text without extra markup.BAC clone（细菌人工染色体克隆）Bacterial artificial chromosome vector carrying a genomic DNA insert, typically 100–200 kb. Most of the large-insert clones sequenced in the project were BAC clones.Back-propagation（反向传输）When training feed-forward neural networks, a back-propagation algorithm can be used to modify the network weights. After each training input pattern is fed through the network, the network’s output is compared with the desired output and the amount of error is calculated. This error is back-propagated through the network by using an error function to correct the network weights. See also Feed-forward neural network.Baum-Welch algorithm（Baum-Welch算法）An expectation maximization algorithm that is used to train hidden Markov models.Baye’s rule（贝叶斯法则）Forms the basis of conditional probability by calculating the likelihood of an event occurring based on the history of the event and relevant background information. In terms of two parameters A and B, the theorem is stated in an equation: The condition-al probability of A, given B, P(AIB), is equal to the probability of A, P(A), times the conditional probability of B, given A, P(BIA), divided by the probability of B, P(B). P(A) is the historical or prior distribution value of A, P(BIA) is a new prediction for B for a particular value of A, and P(B) is the sum of the newly predicted values for B. P(AIB) is a posterior probability, representing a new prediction for A given the prior knowledge of A and the newly discovered relationships between A and B.Bayesian analysis（贝叶斯分析）A statistical procedure used to estimate parameters of an underlyingdistribution based on an observed distribution. See a lso Baye’s rule.Biochips（生物芯片）Miniaturized arrays of large numbers of molecular substrates, often oligonucleotides, in a defined pattern. They are also called DNA microarrays and microchips.Bioinformatics （生物信息学）The merger of biotechnology and information technology with the goal of revealing new insights and principles in biology. /The discipline of obtaining information about genomic or protein sequence data. This may involve similarity searches of databases, comparing your unidentified sequence to the sequences in a database, or making predictions about the sequence based on current knowledge of similar sequences. Databases are frequently made publically available through the Internet, or locally at your institution.Bit score （二进制值/ Bit值）The value S' is derived from the raw alignment score S in which the statistical properties of the scoring system used have been taken into account. Because bit scores have been normalized with respect to the scoring system, they can be used to compare alignment scores from different searches.Bit unitsFrom information theory, a bit denotes the amount of information required to distinguish between two equally likely possibilities. The number of bits of information, AJ, required to convey a message that has A4 possibilities is log2 M = N bits.BLAST （基本局部联配搜索工具，一种主要数据库搜索程序）Basic Local Alignment Search Tool. A set of programs, used to perform fast similarity searches. Nucleotide sequences can be compared with nucleotide sequences in a database using BLASTN, for example. Complex statistics are applied to judge the significance of each match. Reported sequences may be homologous to, or related to the query sequence. The BLASTP program is used to search a protein database for a match against a query protein sequence. There are several other flavours of BLAST. BLAST2 is a newer release of BLAST. Allows for insertions or deletions in the sequences being aligned. Gapped alignments may be more biologically significant.Block（蛋白质家族中保守区域的组块）Conserved ungapped patterns approximately 3-60 amino acids in length in a set of related proteins.BLOSUM matrices（模块替换矩阵，一种主要替换矩阵）An alternative to PAM tables, BLOSUM tables were derived using local multiple alignments of more distantly related sequences than were used for the PAM matrix. These are used to assess the similarity of sequences when performing alignments.Boltzmann distribution（Boltzmann 分布）Describes the number of molecules that have energies above a certain level, based on the Boltzmann gas constant and the absolute temperature.Boltzmann probability function(Boltzmann 概率函数)See Boltzmann distribution.Bootstrap analysisA method for testing how well a particular data set fits a model. For example, the validity of the branch arrangement in a predicted phylogenetic tree can be tested by resampling columns in a multiple sequence alignment to create many new alignments. The appearance of a particular branch in trees generated from these resampled sequences can then be measured. Alternatively, a sequence may be left out of an analysis to deter-mine how much the sequence influences the results of an analysis.Branch length（分支长度）In sequence analysis, the number of sequence changes along a particular branch of a phylogenetic tree.CDS or cds （编码序列）Coding sequence.Chebyshe, d inequalityThe probability that a random variable exceeds its mean is less than or equal to the square of 1 over the number of standard deviations from the mean.Clone （克隆）Population of identical cells or molecules (e.g. DNA), derived from a single ancestor.Cloning Vector （克隆载体）A molecule that carries a foreign gene into a host, and allows/facilitates the multiplication of that gene in a host. When sequencing a gene that has been cloned using a cloning vector (rather than by PCR), care should be taken not to include the cloning vector sequence when performingsimilarity searches. Plasmids, cosmids, phagemids, Y ACs and PACs are example types of cloning vectors.Cluster analysis（聚类分析）A method for grouping together a set of objects that are most similar from a larger group of related objects. The relationships are based on some criterion of similarity or difference. For sequences, a similarity or distance score or a statistical evaluation of those scores is used.CobblerA single sequence that represents the most conserved regions in a multiple sequence alignment. The BLOCKS server uses the cobbler sequence to perform a database similarity search as a way to reach sequences that are more divergent than would be found using the single sequences in the alignment for searches.Coding system (neural networks)Regarding neural networks, a coding system needs to be designed for representing input and output. The level of success found when training the model will be partially dependent on the quality of the coding system chosen.Codon usageAnalysis of the codons used in a particular gene or organism.COG（直系同源簇）Clusters of orthologous groups in a set of groups of related sequences in microorganism and yeast (S. cerevisiae). These groups are found by whole proteome comparisons and include orthologs and paralogs. See also Orthologs and Paralogs.Comparative genomics（比较基因组学）A comparison of gene numbers, gene locations, and biological functions of genes in the genomes of diverse organisms, one objective being to identify groups of genes that play a unique biological role in a particular organism.Complexity (of an algorithm)（算法的复杂性）Describes the number of steps required by the algorithm to solve a problem as a function of the amount of data; for example, the length of sequences to be aligned.Conditional probability（条件概率）The probability of a particular result (or of a particular value of a variable) given one or more events or conditions (or values of other variables).Conservation （保守）Changes at a specific position of an amino acid or (less commonly, DNA) sequence that preserve the physico-chemical properties of the original residue.Consensus（一致序列）A single sequence that represents, at each subsequent position, the variation found within corresponding columns of a multiple sequence alignment.Context-free grammarsA recursive set of production rules for generating patterns of strings. These consist of a set of terminal characters that are used to create strings, a set of nonterminal symbols that correspond to rules and act as placeholders for patterns that can be generated using terminal characters, a set of rules for replacing nonterminal symbols with terminal characters, and a start symbol.Contig （序列重叠群/拼接序列）A set of clones that can be assembled into a linear order. A DNA sequence that overlaps with another contig. The full set of overlapping sequences (contigs) can be put together to obtain thesequence for a long region of DNA that cannot be sequenced in one run in a sequencing assay. Important in genetic mapping at the molecular level.CORBA（国际对象管理协作组制定的使OOP对象与网络接口统一起来的一套跨计算机、操作系统、程序语言和网络的共同标准）The Common Object Request Broker Architecture (CORBA) is an open industry standard for working with distributed objects, developed by the Object Management Group. CORBA allows the interconnection of objects and applications regardless of computer language, machine architecture, or geographic location of the computers.Correlation coefficient（相关系数）A numerical measure, falling between - 1 and 1, of the degree of the linear relationship between two variables. A positive value indicates a direct relationship, a negative value indicates an inverse relationship, and the distance of the value away from zero indicates the strength of the relationship. A value near zero indicates no relationship between the variables.Covariation (in sequences)（共变）Coincident change at two or more sequence positions in related sequences that may influence the secondary structures of RNA or protein molecules.Coverage (or depth) （覆盖率/厚度）The average number of times a nucleotide is represented by a high-quality base in a collection of random raw sequence. Operationally, a 'high-quality base' is defined as one with an accuracy of at least 99% (corresponding to a PHRED score of at least 20).Database（数据库）A computerized storehouse of data that provides a standardized way for locating, adding, removing, and changing data. See also Object-oriented database, Relational database. DendogramA form of a tree that lists the compared objects (e.g., sequences or genes in a microarray analysis) in a vertical order and joins related ones by levels of branches extending to one side of the list. Depth （厚度）See coverageDirichlet mixturesDefined as the conjugational prior of a multinomial distribution. One use is for predicting the expected pattern of amino acid variation found in the match state of a hid-den Markov model (representing one column of a multiple sequence alignment of proteins), based on prior distributions found in conserved protein domains (blocks).Distance in sequence analysis（序列距离）The number of observed changes in an optimal alignment of two sequences, usually not counting gaps.DNA Sequencing （DNA测序）The experimental process of determining the nucleotide sequence of a region of DNA. This is done by labelling each nucleotide (A, C, G or T) with either a radioactive or fluorescent marker which identifies it. There are several methods of applying this technology, each with their advantages and disadvantages. For more information, refer to a current text book. High throughput laboratories frequently use automated sequencers, which are capable of rapidly reading large numbers of templates. Sometimes, the sequences may be generated more quickly than they can becharacterised.Domain （功能域）A discrete portion of a protein assumed to fold independently of the rest of the protein and possessing its own function.Dot matrix（点标矩阵图）Dot matrix diagrams provide a graphical method for comparing two sequences. One sequence is written horizontally across the top of the graph and the other along the left-hand side. Dots are placed within the graph at the intersection of the same letter appearing in both sequences. A series of diagonal lines in the graph indicate regions of alignment. The matrix may be filtered to reveal the most-alike regions by scoring a minimal threshold number of matches within a sequence window.Draft genome sequence （基因组序列草图）The sequence produced by combining the information from the individual sequenced clones (by creating merged sequence contigs and then employing linking information to create scaffolds) and positioning the sequence along the physical map of the chromosomes.DUST （一种低复杂性区段过滤程序）A program for filtering low complexity regions from nucleic acid sequences.Dynamic programming（动态规划法）A dynamic programming algorithm solves a problem by combining solutions to sub-problems that are computed once and saved in a table or matrix. Dynamic programming is typically used when a problem has many possible solutions and an optimal one needs to be found. This algorithm is used for producing sequence alignments, given a scoring system for sequence comparisons.EMBL （欧洲分子生物学实验室，EMBL数据库是主要公共核酸序列数据库之一）European Molecular Biology Laboratories. Maintain the EMBL database, one of the major public sequence databases.EMBnet （欧洲分子生物学网络）European Molecular Biology Network: / was established in 1988, and provides services including local molecular databases and software for molecular biologists in Europe. There are several large outposts of EMBnet, including EXPASY.Entropy（熵）From information theory, a measure of the unpredictable nature of a set of possible elements. The higher the level of variation within the set, the higher the entropy.Erdos and Renyi lawIn a toss of a “fair” coin, the number of heads in a row that can be expected is the logarithm of the number of tosses to the base 2. The law may be generalized for more than two possible outcomes by changing the base of the logarithm to the number of out-comes. This law was used to analyze the number of matches and mismatches that can be expected between random sequences as a basis for scoring the statistical significance of a sequence alignment.EST （表达序列标签的缩写）See Expressed Sequence TagExpect value (E)（E值）E value. The number of different alignents with scores equivalent to or better than S that are expected to occur in a database search by chance. The lower the E value, the more significant the score. In a database similarity search, the probability that an alignment score as good as the one found between a query sequence and a database sequence would be found in as many comparisonsbetween random sequences as was done to find the matching sequence. In other types of sequence analysis, E has a similar meaning.Expectation maximization (sequence analysis)An algorithm for locating similar sequence patterns in a set of sequences. A guessed alignment of the sequences is first used to generate an expected scoring matrix representing the distribution of sequence characters in each column of the alignment, this pattern is matched to each sequence, and the scoring matrix values are then updated to maximize the alignment of the matrix to the sequences. The procedure is repeated until there is no further improvement.Exon （外显子）。

bioinformatics analysis is a technique

bioinformatics analysis is atechniqueBioinformatics Analysis: A Technique Shaping Modern Biomedical ResearchBioinformatics analysis is an intricate technique that revolutionizes the field of biomedical research. It involves the application of computational methods to biological data, enabling scientists to extract meaningful information from vast amounts of genetic, proteomic, and other biological datasets. This technique has become crucial in the post-genomic era, where the amount of biological data generated is exploding at an unprecedented rate.The core of bioinformatics analysis lies in the integration of multiple disciplines, including computer science, statistics, mathematics, and biology. This interdisciplinary approach allows researchers to tackle complex biological problems using advanced computational tools and algorithms. For instance, bioinformatics techniques are used to annotate and interpret genome sequences, predict protein function and interactions, analyze gene expression patterns, and identify biomarkers for various diseases.One of the most significant applications of bioinformatics analysis is in personalized medicine. By analyzing individual genetic variations, bioinformatics can help predict a person's risk for certain diseases and their response to different medications. This information can then be used to develop personalized treatment plans tailored to the unique genetic profile of each patient.Moreover, bioinformatics analysis plays a crucial role in drug discovery and development. By analyzing the interactions between drugs and their targets at the molecular level, bioinformatics can help identify potential drug candidates and predict their efficacy and safety profiles. This information can significantly shorten the drug discovery process and reduce the costs associated with clinical trials.In addition to its applications in personalized medicine and drug discovery, bioinformatics analysis also has numerous other uses. It can be used to study the evolution of species, the mechanisms of gene regulation, and the interactions between different biological systems. Bioinformatics analysis is also essential in the field of epidemiology, where it helps track the spread of diseases and identify potential outbreaks.In conclusion, bioinformatics analysis is a technique that has revolutionized biomedical research. Its interdisciplinary nature and the use of advanced computational methods have enabled researchers to extract meaningful information from vast amounts of biological data. This information has led to breakthroughs in personalized medicine, drug discovery, and other areas of biomedical research, promising better health outcomes and improved quality of life for millions of people.。

Bioinformatics生信案例

精子生成障碍病例及其对照人群
基因组DNA甲基化谱分析
Roche-NimbleGen HG18 Meth 3×720K CpG plus Promoter
DNA甲基化模式异常基因的筛选人群验证
异常DNA甲基化模式基因与EDCs 交互作用分析
Genetics
Biochemistry
Bioinformatics
Molecular biology
Computer science
molecular biology genetics biochemistry other biological science computer science mathematics statistics ……
Bioinformatics
What is Bioinformatics?
Bioinformatics is a new subject of genetic data collection, analysis and dissemination to the research community Dr. Hwa A. Lim，1987
Application in bioinformatics
or Case Control
Case
Control
Thousands of genes, Thousands of problems, while Thousands of papers
1
2 Gene annotation
2 Microarray presentation
A very brief history of bioinformatics:
1965 1970 1974 ~1979 Margaret Dayhoff and collaborators at National Biomedical Research Foundation develop first protein sequence database Saul Needleman and Christian Wunsch develop first sequence alignment algorithm Chou and Fasman develop first protein structure prediction algorithm GenBank established as a nucleic acid sequence database at Los Alamos National Laboratory

安徽医科大学2016级研究生《医学信息检索与利用》

安徽医科大学2016级研究生《医学信息检索与利用》专题检索报告研究生姓名代凤学号 1645011158学科专业空总护理成绩1检索项目标题（2分）（中文）风湿性心脏病进行瓣膜置换术（英文） Valve replacement for rheumatic heart disease关键词 (3分)中文：风湿性心脏病；瓣膜置换术；风心病英文：Rheumatic heart disease ；Valve replacement ； rheumatic heart disease主题词（6分）中文：阿司匹林；阿尔兹海默病；乙酰水杨酸英文： aspirin ； Alzheimer's disease ； Acetylsalicylic Acid中图分类号（3分）： R971.1 ； R473.74 ； R971.12检索项目标题（2分）（中文）风湿性心脏病进行瓣膜置换术（英文） Valve replacement for rheumatic heart disease关键词 (3分)中文：风湿性心脏病；瓣膜置换术；风心病英文：Rheumatic heart disease ；Valve replacement ； rheumatic heart disease主题词（6分）中文：阿司匹林；阿尔兹海默病；乙酰水杨酸英文： aspirin ； Alzheimer's disease ； Acetylsalicylic Acid中图分类号（3分）： R971.1 ； R473.74 ； R971.13检索项目标题（2分）（中文）风湿性心脏病进行瓣膜置换术（英文） Valve replacement for rheumatic heart disease关键词 (3分)中文：风湿性心脏病；瓣膜置换术；风心病英文：Rheumatic heart disease ；Valve replacement ； rheumatic heart disease主题词（6分）中文：阿司匹林；阿尔兹海默病；乙酰水杨酸英文： aspirin ； Alzheimer's disease ； Acetylsalicylic Acid中图分类号（3分）： R971.1 ； R473.74 ； R971.1说明1．根据上述课题，检索不同数据库及网络资源，制定检索策略，写出检索式，分析检索结果。

callforpapers

CALL FOR PAPERS： CFP BIODEVICES 2010BIODEVICES is organized by INSTICC (Institute for Systems and Technologies of Information, Control and Communication).SCOPEThe purpose of the 3rd International Conference on Biomedical Electronics and Devices is to bring together researchers and practitioners from electronics and mechanical engineering, interested in studying and using models, equipments and materials inspired from biological systems and/or addressing biological requirements. Monitoring devices, instrumentation sensors and systems, biorobotics, micro-nanotechnologies and biomaterials are some of the technologies addressed at this conference.BIODEVICES encourages authors to submit papers to one of the main topics indicated below, describing original work, including methods, techniques, advanced prototypes, applications, systems, tools or general survey papers, reporting research results and/or indicating future directions. Accepted papers will be presented at the conference by one of the authors and published in the proceedings. Acceptance will be based on quality, relevance and originality. There will be both oral and poster sessions.The proceedings will be indexed by several major international indexers.Special sessions are also welcome. Please contact the secretariat for further information on how to propose a special session. Additional information can be found at .CONFERENCE TOPICS- Biomedical Instrumentation- Biomedical Equipment- Biomedical Sensors- Biomedical Metrology- Microelectronics- Health Monitoring Devices- Embedded Signal Processing- Low-Power Design- Electrical Bio-Impedance- Bio-Electromagnetism- Biorobotics- Biocomputing and Biochips- Implantable Electronics- Emerging Technologies- Biotelemetry- Wireless Systems- Biomaterials- Power Sources- MEMS- Nanotechnologies- Biomechanical Devices- Artificial Limbs- Technologies Evaluation- BioprintingKEYNOTE SPEAKERSPeter D. Karp, Director Bioinformatics Research Group, Artificial Intelligence Center, United States(List not yet complete)PAPER SUBMISSIONAuthors should submit an original paper in English, carefully checked for correct grammar and spelling, using the on-line submission procedure. Please check the paper formats so you may be aware of the accepted paper page limits.The guidelines for paper formatting provided at the conference web site must be strictly used for all submitted papers. The submission format is the same as the camera-ready format. Please check and carefully follow the instructions and templates provided.Each paper should clearly indicate the nature of itstechnical/scientific contribution, and the problems, domains or environments to which it is applicable.Papers that are out of the conference scope or contain any form of plagiarism will be rejected without reviews.Remarks about the on-line submission procedure:1. A "double-blind" paper evaluation method will be used. Tofacilitate that, the authors are kindly requested to produce andprovide the paper, WITHOUT any reference to any of the authors. This means that is necessary to remove the authors personal details, the acknowledgements section and any reference that may disclose the authors identity.LaTeX/PS/PDF/DOC/DOCX/RTF format are accepted.2. The web submission procedure automatically sends an acknowledgement, by e-mail, to the contact author.Paper submission types:Regular Paper SubmissionA regular paper presents a work where the research is completed or almost finished. It does not necessary means that the acceptance is as a full paper. It may be accepted as a 揻ull paper?(30 min. oral presentation) , a 搒hort paper? (20 min. oral presentation) or a損oster?Position Paper SubmissionA position paper presents an arguable opinion about an issue. The goal of a position paper is to convince the audience that your opinion is valid and worth listening to, without the need to present completed research work and/or validated results. It is, nevertheless, important to support your argument with evidence to ensure the validity of your claims. A position paper may be a short report and discussion of ideas, facts, situations, methods, procedures or results of scientific research (bibliographic, experimental, theoretical, or other) focused on one of the conference topic areas. The acceptance of a position paper is restricted to the categories of "short paper" or "poster", i.e. a position paper is not a candidate to acceptance as "full paper".Camera-ready:After the reviewing process is completed, the contact author (the author who submits the paper) of each paper will be notified of the result, by e-mail. The authors are required to follow the reviews in order to improve their paper before the camera-ready submission.All accepted papers will be published in the proceedings, under an ISBN reference, on paper and on CD-ROM support.PUBLICATIONSAll accepted papers will be published in the conference proceedings, under an ISBN reference, on paper and on CD-ROM support.A book including a selection of the best conference papers will be edited and published by Springer.IMPORTANT DATESConference date: 20 - 23 January, 2010Regular Paper Submission: July 21, 2009Authors Notification (regular papers): October 7, 2009Final Regular Paper Submission and Registration: October 21, 2009SECRETARIATBIODEVICES SecretariatAddress: Av. D.Manuel I, 27A 2ºesq.2910-595 Set鷅al - PortugalTel.: +351 265 520 185Fax: + 44 203 014 5436Email: **********************************Web: VENUEValencia was founded by the ancient Romans in 137 BC and has been pillaged, burned and besieged numerous times by various conquerors over the centuries since, but the vivacious Spanish city has sailed into the second millennium as Europe's quintessential sophisticated modern holiday city, a favoured location for the America's Cup yacht race. Situated on the Mediterranean coast about four hours south of Barcelona, Valencia is spread out around its busy port and backed by the hills which give way to the plains of Aragon.Valencia oozes traditional character, particularly in its old town (El Carmen), and has retained its cultural heritage not only in the form of medieval architecture but also in its quirky, exuberant festivals (like the Battle of the Flowers, the fireworks of Fallas and one dedicated to tomato-hurling). The Valencians even have their own language. Amid the old, Valencia has very much that is new, including its major attraction, the ultra-modern City of Arts and Sciences, which draws around four million appreciative visitors each year.Outdoors it is hard to beat the golden beaches which fan out from theport along the coast, and the sprawling city offers plenty of green parks for strolling, cycling or simply lolling on a bench to get your breath back after indulging in the vibrancy of the city. Football is also a local passion, Valencia's team being at the top of the game, and fans should not miss the atmosphere at one of the carnival-like matches.When night falls, dine on paella, which originated here, and then hit the high spots, because Valencia is renowned for its livelycollection of bars and clubs. It may sound clich閐, but Valencia does indeed fit the bill as the holiday city, which 'has it all'.CONFERENCE CO-CHAIRSAna Fred, IST - Technical University of Lisbon, PortugalJoaquim Filipe, Polytechnic Institute of Set鷅al / INSTICC, Portugal Hugo Gamboa, Instituto de Telecomunica珲es, PortugalPROGRAM COMMITTEEAvailable soon.。

生物信息学bioinformatics(近完整版) Microsoft Word 文档 (2)1

一．什么是生物信息学？Genome informatics is a scientific discipline that encompasses all aspects of genome information acquisition, processing, storage, distribution, analysis, and interpretation. （它是一个学科领域，包含着基因组信息的获取、处理、存储、分配、分析和解释的所有方面。

）(The U.S. Human Genome Project: The First Five Y ears FY 1991-1995, by NIH and DOE)生物信息学是把基因组DNA序列信息分析作为源头，破译隐藏在DNA序列中的遗传语言，特别是非编码区的实质；同时在发现了新基因信息之后进行蛋白质空间结构模拟和预测。

生物信息学的研究目标是揭示“基因组信息结构的复杂性及遗传语言的根本规律”。

它是本世纪自然科学和技术科学领域中“基因组、“信息结构”和“复杂性”这三个重大科学问题的有机结合。

How to find the coding regions in rude DNA sequence?By signals or By contentsAmong the types of functional sites in genomic DNA that researchers have sought to recognize are splice sites, start and stop codons, branch points, promoters and terminators of transcription, polyadenylation sites, ribosomal binding sites, topoisomerase II binding sites, topoisomerase I cleavage sites, and various transcription factor binding sites. Local sites such as these are called signals and methods for detecting them may be called signal sensors.二．新基因和新SNPs的发现与鉴定大部分新基因是靠理论方法预测出来的。

生物信息学课件英文原版课件

Original English version of bioinformatics courseware
• Introduction to Bioinformatics • Genomics • Proteomics • The Application of Bioinformatics in
Medicine • The Future Development of
The research field of bioinformatics
Summary: Research Field of Bioinformatics
Detailed description: The research fields of bioinformatics are very extensive, including genomics, proteomics, systems biology, evolutionary biology, epigenetics, etc. These fields of research all involve the acquisition, processing, analysis, and interpretation of biological data, as well as the role of these data in understanding biological processes and disease mechanisms.
pharmaceuticals. For example, in the field of medicine, genomics can be used to diagnose genetic diseases, predict drug responses, and personalize healthcare. In the field of agriculture, genomics can be used to improve crop and livestock varieties, increase yield and resistance.

一代测序的操作流程

一代测序的操作流程Next-generation sequencing (NGS), also known as next-generation sequencing, is a popular method for DNA sequencing.下一代测序（NGS），也称为下一代测序，是一种常用的DNA测序方法。

NGS has revolutionized the field of genomics with its high-throughput capability, cost-effectiveness, and rapid turnaround time.NGS以其高通量能力，成本效益和快速周转时间，彻底改变了基因组学领域。

The NGS workflow typically includes library preparation, cluster generation, sequencing, and data analysis.NGS工作流程通常包括文库准备，簇生成，测序和数据分析。

First, the DNA or RNA sample is processed to create a library of fragments that can be sequenced.首先，对DNA或RNA样本进行处理，以创建可以进行测序的片段文库。

This library is then amplified and attached to a solid surface to create clusters, where each cluster contains multiple copies of the same DNA fragment.然后，将这个文库进行扩增，并将其附着在固体表面上形成簇，其中每个簇包含同一DNA片段的多个复制。

During sequencing, a sequencer reads each cluster and identifies the nucleotides in the DNA fragment.在测序过程中，测序仪读取每个簇，并确定DNA片段中的核苷酸。

生物信息学应用及主要算法模板

• 原核细胞
Prokaryotic Cells
THE CHEMICAL BASIS OF LIFE
Types of Biological Molecules (1)
• 单糖—二糖—寡糖—多糖
Types of Biological Molecules (2)
• 脂类lipid
Types of Biological Molecules (4)
开发和应用数据分析、理论方法、数学模型和计算机仿真技术，用于生物学、行为学和社会群体系统的研究。
Bioinformatics
Computational Biology
Two aspect of Bioinformatics
Data analysis
Theoretical
Studies
Algorithms
1 GenBank中DNA序列格式 2 EMBL序列格式 3 SwissProt序列格式 4 FASTA序列格式 5 NBRF序列格式 6 Intelligenetics序列格式 7 GCG序列格式 8 PIR/CODATA序列格式 9 Plain/ASCII.Staden序列格式 10 ASN.1序列格式 11 GDE格式
• 中心法则Central Dogma of Genetics • 基因表达Gene Expression
原核细胞的基因结构 Gene Structure of Prokaryote
原核生物
Transcription initiation site Transcription termination site
b-turns are four amino acids big and are stabilized by i-i+4 H-bonds.

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Vol.22no.152006,pages1879–1885doi:10.1093/bioinformatics/btl195 BIOINFORMATICS ORIGINAL PAPERSystems biologyA model diagram layout extension for SBMLRalph GaugesÃ,Ursula Rost,Sven Sahle and Katja WegnerBioinformatics and Computational Biochemistry,EML Research,Schloss-Wolfsbrunnen Weg33,D-69118Heidelberg,GermanyReceived on March29,2006;revised on May10,2006;accepted on May15,2006Advance Access publication May18,2006Associate Editor:Martin BishopABSTRACTMotivation:Since the knowledge about processes in living cells is increasing,modelling and simulation techniques are used to get new insights into these complex processes.During the last few years,the SBML file format has gained in popularity and support as a means of exchanging model data between the different modelling and simulation tools.In addition to specifying the model as a set of equations,many modern modelling tools allow the user to create and to interact with the model in the form of a reaction graph.Unfortunately,the SBML file format does not provide for the storage of this graph data along with the mathematical description of the model.Results:Therefore,we developed an extension to the SBML file format that makes it possible to store such layout information which describes position and size of objects in the graphical representation. Availability:The complete specification can be found on(http:// projects.villa-bosch.de/bcb/sbml/(SBML Layout Extension documen-tation,2005).Additionally,a complete implementation exists as part of libSBML(2006,/software/libsbml/).Contact:Ralph.Gauges@eml-r.villa-bosch.deSupplementary information:Supplementary data are available on Bioinformatics online.1INTRODUCTIONThe availability of fast computers together with computer programs that provide novel analysis and simulation methods has drawn more and more scientists towards investigating reaction networks on the computer as a complementary research method to experiments in the laboratory. One of the advantages computer simulations have over laboratory experiments is the possibility to study the behaviour of every part of even very large reaction systems,whereas in laboratory experiments, due to various reasons,scientists can only observe a limited subset of available species.Another reason is the ease with which reaction conditions and parameters can be changed in a computer model of a reaction network as opposed to in vitro or in vivo experiments.A biochemical model in this context consists of a set of chemical species and a set of reactions that consume and produce some of these species.Often also a mathematical description of the reaction kinetics is given.If this is the case,the behaviour of the model,i.e. the change of concentrations of the various species,can be cal-culated on a computer.This is called a simulation of the model. There are several programs that simulate a biochemical reaction network if they are provided with a mathematical description of the model,e.g.a set of ordinary differential equations.Unfortunately,until recently each of those programs had its own way of reading in these descriptions which was not compatible with those of other ers that wanted to exchange models between different programs had to manually convert the model from one format to the other.Therefore,SBML(System Biology Markup Language) (Hucka et al.,2005,/sbml/docs/papers/ sbml-level-1/html/sbml-level-1.html;Hucka et al.,2003)was developedﬁve years ago to allow programs to exchange biochem-ical models.Nowﬁve years later,close to a hundred programs support the SBMLﬁle format(/,2006). Many of these modern modelling and simulation tools provide the user with a graphical representation of the model.Especially with large models,this representation provides a greatly enhanced over-view of the model and some of the analysis results can be displayed in the context of the whole network.In many cases,this is more intuitive than for example differential equations or a set of chemical equations.Elementaryﬂux mode(Schilling et al.,1999)analysis results would be a good example for this.Some programs assist the users in creating those reaction graphs and a few even go as far as providing completely automated layout routines(Karp and Paley,1994;Schreiber,2002;Wegner and Kummer,2005).On the other hand there are still many programs where the user has to create the graphs for the reaction network manually which often requires considerable amounts of time. Although with SBML there is now a format to exchange the mathematical description of a model,SBML does not provide ways to save the layout of the reaction graphs created with the help of those programs.Each time,the user uses the SBMLﬁle with another program,the layout has to be recreated,either auto-matically by the other program or manually by the user.In order to overcome this limit,we created an extension to the SBMLﬁle format that allows programs to store information on the graphical layout of reaction networks within SBMLﬁles.One possible usage scenario would be a webservice that takes a plain SBMLﬁle and automatically creates a layout and uses our layout extension to store the layout in the SBMLﬁle.Afterwards, the user can load and use this model that now includes a layout in a tool that does not provide automatic layout facilities.Figure1shows two images of the TCA cycle.The layout was created with an automatic layout tool(Wegner and Kummer,2005) developed by one of the authors and stored in an SBMLﬁle using the proposed layout extension.The SBMLﬁle with the layout was then rendered twice.Theﬁrst image was created byﬁrst generating an SVG graphicsﬁle via an XSL transformation.The SVGﬁle was then rendered to a PNG bitmap image with the Batik toolkit(Batik, 2005,/batik/).The second image wasÃTo whom correspondence should be addressed.ÓThe Author2006.Published by Oxford University Press.All rights reserved.For Permissions,please email:journals.permissions@1879rendered by a webservic (SBML Layout Viewer,/Layout/)which can also do automatic layout of reaction networks from SBML ﬁles.Although both programs use different styles to render the layout,the information about position and size of the individual layout components is conserved.There already are several formats to store graphical reaction layout information in model ﬁles,but all of those are tailored towards the needs of one program and are therefore part of this program’s proprietary ﬁle format.So far there has been no method to store this kind of information in a way that it can be exchanged between different programs.With our extension we tried to ﬁnd the least common denominator of those approaches to provide a common and general format for the exchange of layouts.The information speciﬁc to the individual applications was left out.It must also be mentioned that this extension does not compete with speciﬁc ways of drawing reaction networks as speciﬁed by Kohn’s molecular interaction maps or Kitano’s process diagrams (Kohn et al.,2005;Kitano et al .,2005).Our extension describes a way to preserve the layout of such a graph,but it does not specify how the individual elements of the layout are to be rendered.Our extension was created with the intention to provide programs with a way to complement the mathematical model description in an SBML ﬁle with a graphical representation of this information.The extension does not add any semantic information beyond what is already speciﬁed in the SBML model.In order to exchange graphs of metabolic reaction networks,usually no extra information is needed.In the case of more elaborate graphs like interaction maps or process diagrams,where extra information about reactions is coded in the graph that is not part of the SBML model,the programs will have to store this additional information in the form of annotations to the layout.Another intention was to keep the extension as general as possible so that can not only be used for SBML ﬁles,but also for other ﬁles with similar structure.This way,program authors could proﬁt from having a common layout format which would make supporting different ﬁle formats a lot easier.This extension was presented and discussed on several SBML workshops and on the SBML mailing list (SBML Forum,2006,/forums/).It has been accepted by the SBMLcommunity and will be included in the upcoming speciﬁcation for SBML Level 3.2DESIGN PRINCIPLESThe scope of this extension to SBML is to store the layout of a reaction graph.That means,we will describe a language that spe-ciﬁes the size and the position of graphical representations of bio-chemical entities like species and reactions and its connections.How the layout elements are actually to be drawn is not speciﬁed and is up to the program that reads the layout.As an illustration,let us consider Figure 2.All three diagrams are valid renderings of the same layout information and would be described by identical SBML ﬁles because no information about colours,line styles,fonts,etc.is present in our diagram layout extension at the moment.The design of the extension is based on some underlying design decisions.First of all,the layout of the reaction network should be described in a language that is structurally similar to the language that describes the biochemical model itself.That rules out the use of existing XML languages that can describe general 2D drawings [e.g.SVG,2006(/Graphics/SVG)]or general graphs [e.g.GraphML,2006,(/)].Since we want to allow for several different graph layouts for the same model,the layout is not stored as annotations to the individual model elements but as a separate Layout element several of which can be put inside a ListOfLayouts structure.The SBML spe-ciﬁcation already provides some means of extending the standard via annotation elements,so we decided to store the layout information as an annotation to the model element.The decision to store it there,rather than storing it as an annotation to the sbml element,is based on the assumption that later versions of the SBML speciﬁcation may allow the storage of several models per ﬁle.The top element for the layout extension is the listOfLayouts which contains one or more layouts.As stated above,this element will be located in the annotation tag of the model element for SBML Level 2,but for SBML Level 3we expect it to be an additional subelement of the modelelement.Fig.1.Two different renderings of the same TCA cycle layout by two different implementations of the layout extension.A colour version of this figure is available as Supplementary data.R.Gauges et al.1880Because we primarily describe the layout of elements within an SBML model,the structure of the layout extensions is very similar to the structure of the SBML model.Most elements in the layout have a direct correspondence in the SBML speciﬁcation.Consider as example,to store the layout information for a compartment we introduced the compartmentGlyph or to store the layout information for a species we introduced the speciesGlyph. Also,the connection between the layout and the model has to be described.In many cases this will be a simple one-to-one relation, e.g.a given species in the model will be graphically represented by a speciﬁc speciesGlyph element in the layout.There are some exceptions however.Sometimes it is advantageous to describe the same model element by several elements in the layout.For example,species that participate in many reactions will end up with many edges going in and out of the species representation. In order to avoid edge crossings those species can have several speciesGlyphs in the layout.There are also cases where a layout element does not correspond to a speciﬁc element in the model.This could occur if the layout shows a simpliﬁed version of the model where one element in the diagram corresponds to several reactions and intermediate species in the yout elements may also have no counterpart in the SBML model at all.This can occur if the layout includes,for example,a legend or some representation of connecting pathways which are not explicitly considered in the model.In our extension the layout elements connect to model ele-ments via references to their ids.The connection is optional.The layout extension uses a right-handed Cartesian coordinate system.The origin of the coordinate system is located in the upper left corner of the screen.The positive x-axis runs from left to right, the positive y-axis runs from top to bottom and the positive z-axis points into the screen.This seems to be the way most graphical tools do it.For printing purposes a point in this coordinate system is presumed to be1/72of an inch( 0.353mm)as in postscript. Even though we expect most implementations to use2D space for layout in the near future,the extension is alreadyﬁt to handle 3D layout data as well.The extension was designed to provide a high level ofﬂexibility to the user while making potential implementations as straightfor-ward and simple as possible.With the current layout extension,it is possible to describe any given layout with a very limited set of objects.Since the whole design is very general,the layout extension can not only be used for SBMLﬁles but also can in principle be applied to any modelﬁle format that has a similar structure.Pro-grams that want to include layout information into their ownﬁle formats should therefore be able to use this extension for this pur-pose and save a lot of implementation work in the process since the same code can be used to handle the layout in SBMLﬁles.We could even envision a scenario where many programmers would choose to use this extension with theirﬁle formats and authors of other pro-grams could just use their already existing implementation of the layout extension.This would make the task of supporting different proprietaryﬁle formats a lot easier.3LAYOUT ELEMENTSFigure3shows the relations between the different elements in the layout extension.Inheritance relations are shown as well as which element contains which other elements.All layout elements are derived from the SBML element SBase which is the super element of all elements in SBML according to the SBML Level2schema speciﬁcation(/sbml/level2/version1/,2006; Hucka and Finney,2003).The names of the layout elements mostly consist of the corresponding SBML element name and the postﬁx Glyph.The layout elements have the following attributes in common. First,the layout elements have an attribute id.All glyph elements are derived from graphicalObject and must have an id which is unique throughout the model and the layout(with the notable exception of local parameters in reactions).All id attributes are deﬁned to be of type SId as deﬁned in SBML Level2(http:// /sbml/level2/version1/,2006).Second,each glyph element that can have a corresponding element in the model has a second attribute that holds the id of this corresponding element in the model.This attribute is optional, so it is possible to create objects that are not connected to elements in the model.Finally,each glyph element contains a bounding box that deﬁnes the size and position of this element without giving additional information about its contents.Each boundingBox element has a subelement called position of type Point and a subelement called dimensions of type Dimensions.The position element has three attributes, called x,y and z,which specify the location of the point in the coordinate system.The z attribute is optional and defaults to0.0if not speciﬁed.The dimensions element also has three attributes,Fig.2.Illustration of different renderings of the same layout information.Acolour version of this figure is available as Supplementary data.A model diagram layout extension for SBML1881called width ,height and depth ,which specify the size along the positive x-,y-and z -axis,respectively.Again,the depth attrib-ute is optional and defaults to 0.0if not speciﬁed.Both elements of type Point and type Dimensions have an attribute called id which is optional and can be used to reference these elements.All size values for the dimensions element must be positive.In the following sections,we will describe the various elements in detail by giving a short example.3.1LayoutAll layout information is stored in an element called listOfLay-outs .This list can hold one or more layout elements which in turn hold layout information for some or all elements of the SBML model,plus additional objects that are not part of the model.The layout element has an attribute id that uniquely identiﬁes it and a subelement dimensions that speciﬁes its size.The actual layout elements are contained in several list elements,namely a listOfCompartmentGlyphs ,a listOfSpeciesGlyphs ,a listOfReactionGlyphs ,a listOfTextGlyphs and a listOfAdditionalGraphicalObjects ,see the following skeleton:<?xml version ¼"1.0"encoding ¼"UTF-8"?><sbml xmlns ¼"/sbml/level2"level ¼"2"version ¼"1"><model id ¼"TestModel with modiﬁers"><annotation ><listOfLayouts xmlns ¼"/bcb/sbml/level2"xmlns:xsi ¼\"/2001/XMLSchema-instance"><layout id ¼"Layout_1"><dimensions width ¼"400"height ¼"230"/><listOfCompartmentGlyphs >...</listOfCompartmentGlyphs ><listOfSpeciesGlyph >...</listOfSpeciesGlyph ><listOfReactionGlyphs >...</listOfReactionGlyphs ><listOfTextGlyphs >...</listOfTextGlyphs ><listOfAdditionalGraphicalObjects >...</listOfAdditionalGraphicalObjects ></layout ></listOfLayouts ></annotation >...</model ></sbml >3.2GraphicalObjectThe graphicalObject element contains a bounding box and an id.All more speciﬁc layout elements (compartmentGlyph,speciesGlyph,reactionGlyph,textGlyph and spe-ciesReferenceGlyph )are derived from GraphicalObject.Some software tools may want to store information about graphical objects that cannot adequately be described by one of the derived elements.This could e.g.be the legend to the layout or some bitmap graphic.Therefore,generic graphical objects can be put into a listOfAdditionalGraphicalObjects .Note that there is no default interpretation for these generic graphical objects.However,different software tools can use this mechanism to easily extend the layout description to their special needs (using annotations).3.3CompartmentGlyphThe compartmentGlyph element is derived from Graphical-Object and has an optional reference to the id of the corresponding compartment in the model.<listOfCompartmentGlyphs ><compartmentGlyph id ¼"CG_1"compartment ¼"Yeast"><boundingBox ><position x ¼"5.0"y ¼‘5.0"/><dimensions width ¼"390.0"height ¼"220.0"/></boundingBox ></compartmentGlyph >...</listOfCompartmentGlyphs>Fig.3.Containment and inheritance tree for the layout elements.R.Gauges et al.1882Since the compartment id is optional,the user can also specify compartment glyphs that do not have a corresponding compartment in the model.3.4SpeciesGlyphSpecies are represented by speciesGlyph elements which are grouped in a listOfSpeciesGlyphs element.In addition to the attributes from graphicalObject,the speciesGlyph element has a species attribute which can hold the id of the appro-priate species element in the model.Several species glyphs can refer to the same species in the SBML model which can be useful to avoid crossing lines.The species attribute is optional to allow the program to specify species representations that do not have a direct correspondence in the model.One case where this might be useful is when parts of the network have been collapsed to form a simpliﬁed diagram where several model elements are now being represented by a single species glyph.<listOfSpeciesGlyphs><speciesGlyph id¼"SpeciesGlyph_1"species¼"NADH"><boundingBox><position x¼"968.816"y¼"386.91"/><dimensions width¼"88.8"height¼"17.4609"/></boundingBox></speciesGlyph>...</listOfSpeciesGlyphs>3.5ReactionGlyphReactions are represented by reactionGlyph elements in the layout extension.Just like the other glyphs,the reactionGlyph has an optional attribute that speciﬁes the id of the corresponding reaction in the model.Basically,the graphical representation of a reaction consists of a part that stands for the reaction itself and parts describing the connections between the reaction and the species glyphs.The latter is given by a listOfSpeciesReference-Glyphs which is described below in the next paragraph.In many cases reaction glyphs and species reference glyphs are represented by curves.For that reason,a reaction glyph or a species reference glyph can contain the speciﬁcation for a curve object in addition to the bounding box.If both,the bounding box and the curve are speciﬁed in a reaction glyph or species reference glyph, the curve takes precedence over the bounding box.<listOfReactionGlyphs><reactionGlyph id¼"RG_1"reaction¼"reaction_0"><curve><listOfCurveSegments><curveSegmentxsi:type¼"LineSegment"><start x¼"111.364"y¼"158.972"/><end x¼"179.858"y¼"76.3368"/></curveSegment></listOfCurveSegments></curve><listOfSpeciesReferenceGlyphs>...</listOfSpeciesReferenceGlyphs></reactionGlyph></listOfReactionGlyphs>The curve element contains a listOfCurveSegments which contains an arbitrary number of curve segments.For now, we provide the deﬁnitions for two types of curve segments (LineSegment and CubicBezier)but this can easily be extended if needed.Since all types of curves can be represented by a series of cubic bezier segments,we think that limiting the curve segment types to only those two types does not pose a problem but rather helps to keep the layout speciﬁcation simple.CubicBezier is a direct subtype of LineSegment.The type of the curve segment has to be speciﬁed with a xsi:type attribute in the curveSegment element(xsi: type¼LineSegment or xsi:type¼CubicBezier). The LineSegment type contains two elements of type Point. One is called start(the starting point of the line)and the other is called end(the endpoint of the line).As mentioned above,the CubicBezier type is derived from LineSegment,so it has the same two Point elements start and end,which again specify the starting point and the endpoint of the cubic bezier seg-ment.In addition,the two elements basePoint1and base-Point2specify the two additional base points that are needed to describe a CubicBezier curve segment.Both base points are optional.If not speciﬁed,they are assumed to be in the middle between the two endpoints of the cubic bezier segment thus describ-ing a straight line.If only one base point is given,the other is assumed to be identical to the one speciﬁed.3.6SpeciesReferenceGlyphThe graphical connection between a species glyph and a reaction glyph,an arrow or some curve in most cases,is represented by the speciesReferenceGlyph element.Therefore,a listOfSpeciesReferenceGlyphs is contained in a reactionGlyph.The speciesReferenceGlyph has a speciesGlyph attribute that corresponds to the id of a speciesGlyph element that is connected to the reactionGlyph.The speciesReference attribute refers to a species refer-ence(or a modiﬁer species reference)in the model.This id deﬁnes a relation between a species reference glyph and the appropriate species reference or modiﬁer species reference in the model. Since the speciﬁcation for SBML Level2Version1does not include an id attribute for elements of type SpeciesReference or ModiﬁerSpeciesReference,we propose to use the annotation of those elements to store an id.This id is of type SId and has to be unique within the model and the layout.SBML Level2Version 2will probably add the id attribute to these element types.<speciesReference species¼"ATP"><annotation><layoutIdxmlns¼"/bcb/sbml/level2"id¼"ATP_Reference_1"/></annotation></speciesReference>A model diagram layout extension for SBML1883If the speciesReference attribute is set,we can deduce the role a certain species plays in the reaction (whether it is a substrate,a product or a modiﬁer)by checking where in the reaction it has been speciﬁed.Since this connection from the layout to the model is optional,there are cases where the role of the species cannot be derived in that way.For that reason and in the case when the respective information from the model needs to be overridden,we propose an optional role attribute.This can be used to provide the role of a metabolite in the reaction as a string.The following values are allowed:substrate,product,sidesubstrate,sideproduct,modiﬁer,activator,inhibitor and undeﬁned.The values substrate and product are used if the species reference is a main product or substrate in the reaction.Sidesubstrate and sideproduct are used for species like ATP,NAD +,etc.that some renderers might choose to display differently than main substrates and products.Activator and inhibitor are modiﬁers where their inﬂuence on the reaction is known and modiﬁer is a more general term if the inﬂuence is unknown or changes during the course of the simulation.This list is probably not exhaustive and will be updated as needed.We are also aware of a proposal (System Biology Ontologies,2006,/index.php?s ¼SBO)that would allow the inclusion of controlled vocabularies based on information from ontologies.If this was possible,the role of a speciesReference element could be speciﬁed through those means which would make the role attribute in species-ReferenceElements obsolete.So far we have deﬁned which graphical objects should be con-nected to the reaction glyph.This is the minimum information that a render program with biochemical knowledge needs to display the reaction layout.The standard way to render this connection would be a straight line.In most cases,the relation of a species to a reaction will be graphically represented by a curve.In this case the above-explained curve element can be used.The species reference glyph should either contain a bounding box or a curve speciﬁcation.If both are given,the bounding box is to be ignored.<listOfSpeciesReferenceGlyphs ><speciesReferenceGlyph id ¼"SRG_1"speciesReference ¼"SR_1"speciesGlyph ¼"SG_8"role ¼"substrate"><curve ><listOfCurveSegments ><curveSegment xsi:type ¼"LineSegment"><start x ¼"111.364"y ¼"158.972"/><end x ¼"74.2"y ¼"138.183"/></curveSegment ></listOfCurveSegments ></curve ></speciesReferenceGlyph >...</listOfSpeciesReferenceGlyphs >In many cases,the curves will be drawn with an arrowhead (or some other graphical symbol)at one end.Therefore,the direction of the curves matters.We recommend that curves in a species reference glyph are deﬁned so that the arrowhead is at the end of the ually,this means that for substrates and products the curve starts at the reaction glyph and points towards the species glyph.For species reference glyphs that describe modiﬁers the curve should point towards the reaction glyph.3.7TextLabelsA list Of TextGlyphs element in the layout contains an arbit-rary number of textGlyph elements.Each text glyph describes a text label.The textGlyph element has an id attribute of type SId which has to be unique within the model and the layout and a bounding box which speciﬁes the size of the text glyph.<listOfTextGlyphs ><textGlyph id ¼"TextGlyph_1"graphicalObject ¼"SG_8"originOfText ¼"NADH"><boundingBox ><position x ¼"10"y ¼"120.722"/><dimensions width ¼"128.4"height ¼"17.4609"/></boundingBox>Fig.4.This figure shows one possible rendering of a simple yout code is displayed and connected to the corresponding graphical representation via a straight line.A colour version of this figure is available as Supplementary data.R.Gauges et al.1884。