visualization of phylogenetic trees
进化树(Phylogenetictree)
确定运行后就会出现下面这个
采用变通的办法，下载新版Dnapars ver3.61
同样修改参数M
成功运行！
最后Dnapars ver3.61输出二个文件，分别命名为dnapars,outfile和dnapars,outtree
最后运行consense，导入dnapars,outtree
打开consense,outfile
2
To reconstrut phyligenetic tree，构建一个进化树；
3
对进化树进行评估。主要采用Bootst：最大简约法
1
首先用ClustalW比对序列。
2
使用SEQBOOT产生重复随机序列。
3
使用DNAPARS构造进化树。
4
使用CONSENSUS分析一致性。
首先用CLUSTALX对齐序列，输出1.phy，文本 编辑器打开后如下图：
共8个序列，每个序列50个碱基。
然后，打开软件SEQBOOT，如下图
输入刚才生成的1.PHY文件 输入一个4N+1的数字后，比如5。
Bootstraping法就是从整个序列的碱基（氨基酸）中 任意选取一半，剩下的一半序列随机补齐组成一个 新的序列。这样，一个序列就可以变成了许多序列。 一个多序列组也就可以变成许多个多序列组。根据 某种算法（最大简约性法、最大可能性法、除权配 对法或邻位相连法）每个多序列组都可以生成一个 进化树。将生成的许多进化树进行比较，按照多数 规则（majority-rule）我们就会得到一个最“逼真” 的进化树。
如图：
对比两种方法得到的进化树结果
谢谢。
用PHYLIP构建进化树
冯伟，北医三院血管医学研究所 snooppyyy@
16srna菌种鉴定建树流程
16srna菌种鉴定建树流程英文回答：To identify and build a phylogenetic tree for 16S rRNA bacterial species, several steps need to be followed. The16S rRNA gene is commonly used for bacterial identification due to its conserved regions that allow for comparison across different species. Here is a general outline of the process:1. Sample collection and DNA extraction: The first step is to collect bacterial samples from the environment or the host organism. This can be done by swabbing surfaces, collecting soil or water samples, or isolating bacteriafrom clinical specimens. Once the samples are obtained, DNA extraction is performed to isolate the bacterial genomic DNA.2. PCR amplification of the 16S rRNA gene: The 16S rRNA gene is then amplified using polymerase chain reaction (PCR)with specific primers. These primers target conserved regions of the gene that are present in all bacterial species. The PCR amplification results in multiple copies of the 16S rRNA gene.3. DNA sequencing: The amplified 16S rRNA gene is then subjected to DNA sequencing. There are several sequencing technologies available, such as Sanger sequencing or next-generation sequencing (NGS) platforms. The sequencing results in obtaining the nucleotide sequence of the 16S rRNA gene.4. Sequence analysis and alignment: The obtained 16S rRNA gene sequences are then analyzed and aligned using bioinformatics tools. This step involves comparing the sequences to known databases, such as the NCBI's GenBank, to identify similar sequences and assign taxonomic information to the bacterial species.5. Phylogenetic tree construction: Once the sequences are aligned, a phylogenetic tree is constructed. This tree represents the evolutionary relationships between differentbacterial species based on their 16S rRNA gene sequences. Various software programs, such as MEGA or PHYLIP, can be used to build the phylogenetic tree using different algorithms, such as neighbor-joining or maximum likelihood.6. Tree visualization and interpretation: The final step is to visualize and interpret the constructed phylogenetic tree. The tree can be displayed using tree visualization software, such as FigTree or iTOL. The tree can provide insights into the relatedness and evolutionary history of the bacterial species included in the analysis.中文回答：鉴定和构建16S rRNA菌种的系统发育树需要遵循几个步骤。
MEGA 4.0
MEGA4:Molecular Evolutionary Genetics Analysis(MEGA)Software Version4.0Koichiro Tamura,* Joel Dudley,*Masatoshi Nei,àand Sudhir Kumar§**Center for Evolutionary Functional Genomics,The Biodesign Institute,Arizona State University; Department of Biological Sciences,Tokyo Metropolitan University,Tokyo,Japan;àDepartment of Biology and the Institute of Molecular Evolutionary Genetics,The Pennsylvania State University;and§School of Life Sciences,Arizona State University We announce the release of the fourth version of MEGA software,which expands on the existing facilities for editing DNA sequence data from autosequencers,mining Web-databases,performing automatic and manual sequence alignment,analyzing sequence alignments to estimate evolutionary distances,inferring phylogenetic trees,and testing evolutionary hypotheses.Version4includes a unique facility to generate captions,written infigure legend format,in order to provide natural language descriptions of the models and methods used in the analyses.This facility aims to promote a better understanding of the underlying assumptions used in analyses,and of the results generated.Another new feature is the Maximum Composite Likelihood(MCL)method for estimating evolutionary distances between all pairs of sequences simultaneously,with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages.This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree.This new version is a native32-bit Windows application with multi-threading and multi-user supports,and it is also available to run in a Linux desktop environment (via the Wine compatibility layer)and on Intel-based Macintosh computers under the Parallels program.The current version of MEGA is available free of charge at .Since the early1990s,MEGA software functionality has evolved to include the creation and exploration of sequence alignments,the estimation of sequence diver-gence,the reconstruction and visualization of phylogenetic trees,and the testing of molecular evolutionary hypotheses. The three versions of MEGA have been released,and they integrate Web-based sequence data acquisition and align-ment capabilities(fig.1)with the evolutionary analyses (fig.2),making it much easier to conduct comparative anal-yses in a single computing environment(Kumar,Tamura, and Nei2004).Over time,MEGA has come to enhance the classroom learning experience as its use by researchers, educators,and students in diverse disciplines has expanded (Kumar and Dudley2007).The fourth version(MEGA4) contains three distinct newly developed functionalities, which are outlined below.First,we have developed a Caption Expert software module that generates descriptions for every result obtained by MEGA4.This description informs the user of all of the options used in the analysis,including the data subset used(e.g.,codon positions included),the chosen option for the handling of sites with gaps or missing data,the evolu-tionary model of substitution(e.g.,DNA substitution pat-tern,uniformity of evolutionary rates among sites,and homogeneity assumption among lineages),and the methods applied for estimating pairwise distances and for inferring and testing phylogeny.The caption also includes specific citations for any method,algorithm,and software used in the given analysis.Two examples of descriptions generated by the Caption Expert are shown infigure3.The availability of these descriptions is intended to promote a better understanding of the underlying assump-tions used in analyses,and of the results produced.This is needed because MEGA’s intuitive graphical interface makes it easy for both new and expert users to conduct a variety of computational and statistical analyses. However,some users may not immediately realize the underlying assumptions and data-handling options in-volved in each analysis.Even expert molecular and popu-lation geneticists may not be able to discern all of the assumptions implied.In general,we expect a written de-scription of methods and results to be useful for students and researchers when preparing tables andfigures for pre-sentation and publication.Second,we have now added a Maximum Composite Likelihood(MCL)method for estimating evolutionary distances(d ij)between DNA sequences,which MEGA users frequently employ for inferring phylogenetic trees, divergence times,and average sequence divergences between and within groups of sequences.In this approach, the Composite Log Likelihood(CL)obtained as the sum of log likelihood for all sequence pairs in an alignment is maximized byfitting the common parameters for nucle-otide substitution pattern(h)to every sequence pair(i,j): CL5Pi;jln lðh;d ijÞ(Tamura,Nei,and Kumar2004).This approach was previously referred to as the‘‘Simultaneous Estimation’’(SE)method,because all d ij’s are simul-taneously estimated(Tamura,Nei,and Kumar2004). The MCL approach differs from current approaches for evolutionary distance estimation,wherein each distance is estimated independently of others,either by analytical formulas or by likelihood methods(independent estimation [IE]approach).The MCL method has many advantages over the IE approach.To begin with,the IE method for estimating evo-lutionary distance for each pair of sequences will often cause rather large errors unless very long sequences are used.The use of the MCL method reduces these errors con-siderably,as a single set of parameters estimated from all-sequence pairs is applied to each distance estimation.When distances are estimated with lower errors,distance-based methods for inferring phylogenies are expected to be more accurate.This is indeed the case for theKey words:selection,genomics,phylogenetics,software,cross-platform.E-mail:s.kumar@Mol.Biol.Evol.24(8):1596–1599.2007doi:10.1093/molbev/msm092Advance Access publication May7,2007ÓThe Author2007.Published by Oxford University Press on behalf ofthe Society for Molecular Biology and Evolution.All rights reserved.For permissions,please e-mail:journals.permissions@Neighbor-Joining method (Saitou and Nei 1987),as the use of the MCL distances leads to a much higher accuracy (Tamura,Nei,and Kumar 2004).Even when the topologies estimated are the same,the use of the MCL distances often gives higher bootstrap values for the estimated phy-logenetic tree compared to the use of IE distances,which is evident from the example given in figure 4A (MCL:bold,IE:italics).In addition,the IE distances are not always estimable when pairwise distances are calculated between very dis-tantly related sequences,because the arguments of loga-rithms in the analytical formulas may become negative by chance.The probability of occurrence of such inappli-cable cases increases as the number of sequences in the data increases,the evolutionary distances become larger,and the substitution pattern becomes more complex (Tamura,Nei,and Kumar 2004).The use of the MCL method eliminates this problem effectively and allows for the use of sophisticated models in inferring phylogenies from an increasingly larger number of diverse sequences.MEGA4implements the MCL approach for estimat-ing distances between sequence pairs,average distances between and within groups,and average pairs overall with their variances estimated by a bootstrap approach.Our implementation of the MCL method allows for the consid-eration of substitution rate variation from site to site,using an approximation of the gamma distribution of evolutionary rates,and the incorporation of heterogeneity of base com-position in different species/sequences.The user also has the flexibility to estimate the numbers of transition and transversion type substitutions per site separately.Natu-rally,the MCL distances can be used for inferring phylog-enies by the distance-based methods,along with the bootstrap tests of phylogenies.MEGA4implements the MCL approach under the Tamura-Nei (1993)substitution model,in which the rates of two types of transitional substitutions (between purines [a 1]and between pyrimidines [a 2])and the rate of trans-versional substitutions (b )are considered separately by taking into account the unequal frequencies of four nucleo-tides (base composition bias).The MCL estimates of the transition/transversion rate ratio have been found to be close to the true values in previous simulation experiments (Tamura,Nei,and Kumar 2004).We have employed this feature to provide users with a facility to compute the rel-ative rates of substitutions between nucleotides based on the MCL estimates of a 1,a 2,b ,and on the observed frequencies of the four nucleotides under the Tamura-Nei (1993)model (fig.3C).For ease of comparison,we have expressed these substitution rates as relative frequencies ofsubstitutionsF IG .1.—Sequence alignment editor and Web-data mining features in MEGA4.In the Alignment Explorer (A ),the integrated web browser (B )permits downloading sequences from online databases directly into the current alignment,without the need for manual cutting-and-pasting and reformatting.The DNA sequences can be translated to the corresponding protein sequences by a single mouse click (D ),and the protein sequences can be aligned by ClustalW (E )(Thompson,Higgins,and Gibson 1994)and adjusted manually by eye.Returning to the nucleotide view automatically aligns the nucleotide sequences according to the protein alignments,and DNA and protein sequence alignments can be exported in a variety of formats for use with other programs.Alignment Editor also contains facilities for editing and importing of trace data files output from DNA sequencers (C ).MEGA4software 1597between nucleotides such that the sum of all frequencies is 100(see also Gojobori,Li,and Graur 1982).Third,we have now programmed MEGA4to run on some versions of Linux through the Wine software com-patibility layer ().The first advancement alleviates the problem of performance degradation (and the need to purchase Windows emulation software)when using MEGA on Linux.Wine is neither a hardware nor a software emulator,but an open source tool that allows for the native execution of Windows applications on Linux.Our tests of MEGA4running on Linux show the display,stability,and performance to be highly satisfactory and comparable to the native Windows system (fig.4B).Fur-thermore,investigators now report MEGA4runningonF IG .2.—A collection of menus that provide access to many different data analysis options in MEGA4,including exploration of input data set (A ),estimation of evolutionary distances (B ),inferring and testing phylogenetic trees (C ),tests of homogeneity of substitution patterns and its estimation (D ),tests of selection (E ),alignment of DNA and protein sequences (F ),and the dialog box that provides users with options to select model of substitution and data sub-setting options (G).F IG .3.—The Tree-Explorer displaying a Neighbor-Joining tree of mitochondrial 16S rRNA sequences (A ),and the description generated by the Caption Expert (B ).Estimates of the relative probabilities of nucleotide substitutions for 70control-region sequences of human mitochondrial DNA sequences are shown in (C ).The gamma shape parameter (a 50.35)was estimated using the Yang and Kumar (1996)method,and the rest of the analysis details are given in (B ).It is worth noting that the Tree Explorer shown in (A )includes a high-resolution tree drawing facility that includes displaying trees in a variety of formats,with options to display/hide branch lengths as well as clade confidence labels,and re-rooting and rearranging trees,among other functionalities.MEGA4can export the drawings to graphics programs,and can export trees in Newick format for use by other programs.Furthermore,MEGA can import and draw trees from Newick format files that have been estimated by other programs (see fig.2C ).1598Tamura et al.Intel-based Macintosh computers under the Parallels pro-gram as well as it does on Windows-native personal com-puters (see Hall 2007).The Parallels program is a native solution for Macintosh computers that permits them to simultaneously run Windows and Macintosh software.We have also built support for a multi-user environ-ment,which will allow each user of the same computer to keep his/her customized settings,including file locations,window sizes,choice of genetic code table,and previously used analysis options.This feature will facilitate educa-tional and laboratory usage,where a single computer is often shared by multiple users.In conclusion,MEGA4now contains a wide array of functionalities for the molecular evolutionary analysis of data (/features.html).It is useful to note that while we are continuously adding new methods and functions to MEGA,we do not intend to make it a catalog of all evolutionary analysis methods available.Rather,it is anticipated to become a workbench for the exploration of sequence data from evolutionary perspectives.AcknowledgmentsWe thank the colleagues,students,and volunteers who spent countless hours testing the early release versions of MEGA;almost all facets of MEGA’s design and imple-mentation benefited from their comments.We thank Ms.Linwei Wu for assistance with MEGA Web site and for handling bugs,and Ms.Kristi Garboushian for edito-rial support.We thank the two reviewers for suggesting many useful text additions,which have been included in the figure 1legend and in the text.We also thank Drs.Masafumi Nozawa and Barry Hall for comments on an earlier version of this manuscript.The MEGA software project is supported by research grants from NationalInstitutes of Health (S.K.and M.N.)and from Japan Society for Promotion of Sciences (K.T.).Literature CitedGojobori T,Li WH,Graur D.1982.Patterns of nucleotide substitution in pseudogenes and functional genes.J Mol Evol.18:360–369.Hall BG.Phylogenetic trees made easy:A how-to manual.Sunderland (MA):Sinauer Associates.Kumar S,Dudley J.2007.Bioinformatics for biologists in the genomics era.Bioinformatics.10.1093/bioinformatics/btm239.Kumar S,Tamura K,Nei M.2004.MEGA3:an integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment.Brief Bioinform.5:150–163.Saitou N,Nei M.1987.The Neighbor-Joining method—a new method for reconstructing phylogenetic trees.Mol Biol Evol.4:406–425.Tamura K,Nei M.1993.Estimation of the number of nucleo-tide substitutions in the control region of mitochondrial-DNA in humans and chimpanzees.Mol Biol Evol.10:512–526.Tamura K,Nei M,Kumar S.2004.Prospects for inferring very large phylogenies by using the Neighbor-Joining method.Proc Natl Acad Sci USA.101:11030–11035.Thompson JD,Higgins DG,Gibson TJ.1994.ClustalW—improving the sensitivity of progressive multiple sequence alignment through sequence weighting,position-specific gap penalties and weight matrix choice.Nucleic Acids Res.22:4673–4680.Yang Z,Kumar S.1996.Approximate methods for estimating the pattern of nucleotide substitution and the variation of substitution rates among sites.Mol Biol Evol.13:650–659.William Martin,Associate Editor Accepted May 2,2007F IG .4.—(A )Bootstrap support for the branching order of 16Laurasiatheria species reconstructed with MCL approach (bold)and without MCL approach (italics)under the Tamura-Nei (1993)model (see figure 3B for rest of the analysis details).The 16S rRNA sequences used were downloaded from GenBank and were aligned in MEGA4using CLUSTALW (accession numbers:AJ428578,NC004029,X72004,AF303109,NC008093,DQ480502,X97336,X79547,DQ534707,AJ554051,AJ554061,NC000889,NC007704,AB074968,NC005044,and NC001941).(B )Comparison of MEGA4performance benchmarks on Windows and Linux (with Wine application compatibility layer).Identical hardware configuration was used,and example data sets included in the MEGA4installation were employed.The results show that computations executed under Wine are penalized by about 2s,which is attributable to the need for Wine’s initialization.MEGA4software 1599。
生物信息学复习题
⽣物信息学复习题⼀、名词解释1.bioinformatics：⽣物信息学，指从事对基因组研究相关的⽣物信息的获取、加⼯、储存、分配、分析和解释的⼀门科学，是⼀门⽣物学，数学和计算机相互交叉融合⽽产⽣的新兴学科。
2.molecular bioinformatics：指综合应⽤信息科学、数学的理论、⽅法和技术，管理、分析和利⽤⽣物分⼦数据的科学。
3.GenBank：是美国全国卫⽣研究所维护的基因序列数据库，汇集并注释了所有公开的核酸序列，与⽇本的DNA数据库DDBJ以及欧洲分⼦实验室核酸序列数据库EMBL⼀起，都是国际核苷酸序列数据库合作的成员。
4.EMBL：EMBL实验室—欧洲分⼦⽣物学实验室，EMBL数据库—是⾮盈利性学术组织EMBL建⽴的综合性数据库，EMBL核酸数据库是欧洲最重要的核酸序列数据库，它定期地与美国的GenBank、⽇本的DDBJ数据库中的数据进⾏交换，并同步更新。
5.DDBJ：⽇本DNA数据库，主要向研究者收集DNA序列信息并赋予其数据存取号，信息来源主要是⽇本的研究机构，也接受其他国家呈递的序列。
6.BLAST：基本局部⽐对搜索⼯具的缩写，是⼀种序列类似性检索⼯具。
BLAST采⽤统计学⼏分系统，同时采⽤局部⽐对算法， BLAST程序能迅速与公开数据库进⾏相似性序列⽐较。
BLAST结果中的得分是对⼀种对相似性的统计说明。
7.BLASTn：是核酸序列到核酸库中的⼀种查询。
库中存在的每条已知序列都将同所查序列作⼀对⼀地核酸序列⽐对。
8.BLASTp：是蛋⽩序列到蛋⽩库中的⼀种查询。
库中存在的每条已知序列将逐⼀地同每条所查序列作⼀对⼀的序列⽐对。
9.Clustsl X：是CLUSTAL多重序列⽐对程序的Windows版本，是⽤来对核酸与蛋⽩序列进⾏多序列⽐较的程序，也可以对来⾃不同物种的功能或结构相似的序列进⾏⽐对和聚类，通过重建系统发⽣树判断亲缘关系，并对序列在⽣物进化过程中的保守性进⾏估计。
SplitsTree 4.0- Computation of phylogenetic trees and networks
1 SplitsTree4.0-Computation of phylogenetic treesand networksDaniel H.Huson1,Tobias Kloepper2,David Bryant3 Keywords:phylogeny,evolution,trees,networks,graphs.1IntroductionThe goal of phylogenetic analysis is to determine the order and approximate timing of spe-ciation events in the evolution of a given set of species.In the classic theory of phylogenetic analysis the species are assumed to evolve along a(bifucating)X-tree,experiencing point mutations along the way.Recent results from genome wide comparision of gene trees seem to indicate that these models mayfit well for single genes but fail to represent the complex evo-lution of a genome.A natural generalisation of the phylogenetic tree,that seems tofit well with the complex evolution of a genome,is the phylogenetic network.There are two main approches to generate such phylogenetic networks.Thefirst one is the combined analysis approach,and the second one is the individual analysis approach.In thefirst approach the phylogenetic network is generated directly from the given combined information.In the sec-ond approach individual trees are generated for the given information sets and all individual trees are then combined into one network.Bandelt and Dress[BD92]suggested a number of combined analysis methods,such as the split-decomposition method and the parsimony splits.More recently,Bryant and Moulton[BM02]described a new method Neighbor-Net, that brings together both split decomposition and the well-known Neighbor-Joining method [SN87].Holland and Moulton[HM03]presented a method to join individual gene trees for a common set of species.In2004Huson et al.[HDKS04]presented the Z-Super-Network method,which merges individual partial gene trees.2The SplitsTree programThere exist a number of packages for performing phylogenetic analysis,e.g.[Swo00,SvH96, MM02].However,they all use trees as the fundamental data structure.In contrast,the SplitsTree program[HB05]is based on so-called splits and phylogenetic networks.It is aimed at providing a general framework for both tree-and network-oriented phylogenetic analysis.Fundamental data types supported by the program includ unaligned-and aligned sequences,distances,splits,trees,networks and quartets.The package provides commonly used distance-based algorithms.We have also implemented the methods mentioned in the introduction.The package provides numerous visualisation methods for phylogentic trees and networks,examples are split graphs[DH04]and the equal-daylight method[Fel04].We [HKLS05]have recently implemented methods for the reconstruction of reticulation networks and the transformation from split networks to reticulation networks.The main features of the program are:2•it runs on any machine with minimal installation requirements based on Java or Java Web Start.•GUI version for interactive use,command-line version for scripting pipelines.•Flexible frame-work for doing phylogenetic analysis.•De-centralized plug-in concept for adding new methodology.•Fundamental data types include splits and quartets.•Uses Nexusfile format,with one-to-one correspondence between internal data classes and external nexus blocks,and supports most other formats.•Transformations of molecular sequences to distances.•Combined and individual analysis methods.•Visualisation of phylogenetic trees and networks.•Interactive exploration of the visualisation.•Bootstrapping3Availability.The program is freely available from rmatik.uni-tuebingen.de/software References[BD92]H.-J.Bandelt,and A.W.M.Dress,Split Decomposition:A new and useful approach to phylogenetic analysis of distance data.Molecular Phylogenetics and Evolution1(3):242-252, 1992.[BM02] D.Bryant and V.Moulton.NeighborNet:An agglomerative method for the construction of planar phylogenetic networks.In R.Guig´o and D.Gusfield,editors,Algorithms in Bioinfor-matics,WABI2002,volume LNCS2452,pages375,391,2002.[DH04] A.W.M.Dress and D.H.Huson.Constructing splits graphs,IEEE/ACM Transactions in Computational Biology and Bioinformatics,volume1(3),pages109-115,2004.[Fel04]J.Felsenstein.Inferring Phylogenies Sinauer Associates,Inc.,pages582ff,2004.[HM03] B.Holland and V.Moulton.Consensus networks:A method for visualizing incompatibilities in collections of trees.Algorithms in Bioinformatics,WABI2003,volume LNBI2812,pages 165-176,2003[HB05] D.H.Huson and D.Bryant.Estimating phylogenetic trees and networks using SplitsTree4,note=Manuscript in preparation,software available from rmatik.uni-tuebingen.de/software[HDKS04] D.H.Huson,T.Dezulian,T.Kloepper and M.A.Steel.Phylogenetic Super-Networks from Partial Trees.Algorithms in Bioinformatics,WABI2004,in press,2004.[HKLS05] D.H.Huson,T.Kloepper,P.J.Lockhart and M.A.Steel.Reconstruction of Reticulate Networks from Gene Trees accepted for RECOMB2005.[MM02]W.Maddison and D.Maddison.Mesquite-a modular system for evolutionary analysis./mesquite/mesquite.html,2002.[SN87]N.Saitou and M.Nei.The Neighbor-Joining method:a new method for reconstructing phylogenetic trees.Molecular Biology and Evolution,4:406–425,1987.[SvH96]K.Strimmer and A.von Haeseler.Quartet puzzling:a quartet maximum likelihood method for reconstructing tree topologies.Molecular Biology and Evolution,13:964–969,1996. [Swo00] D.L.Swofford.PAUP∗:Phylogenetic analysis using parsimony(∗:and other methods), version4.2,2000.。
环境微生物多样性分析
(((SEQ1:0.02120,(SEQ2:0.09111,SEQ3:0.04491)node1:0.00097)node2:0.0 0194, (SEQ4:0.03160,SEQ5:0.04378)node3:0.00365)node4:0.00188,SEQ6:0.00 881)node5:0.00739；
Muscle
/muscle/
/pynast/#
/fasttree/
Phylogenetic tree
NEWICK format:
NEWICK is a standard format that is recognized by most programs that generate or allow visualization of phylogenetic trees including PHYLIP, TREE-PUZZLE, ARB, and TREEVIEW.
unWeighted unifrac
Qualitative
Weighted unifrac
Quantitative
Unifrac分析软件
Unifrac /unifrac/index.psp
Mothur /wiki/Unifrac.weighted
/unifrac/
Distance Visualization
Nonmetric Multidimensional Scaling (NMDS)
Principal Coordinate Analysis (PCoA)
Hierarchical clustering
H2
Bacteroidetes Cyanobacteria Spirochaetes
进化树(精美自制)PPT
每个分支在不同此取样时出现的频率赋予该分 支一个百分比。 如果严格根据统计学概念，该百分比要大于95 ％才认为该分支可信。在实际应用中该值大于 75％就认为可信。
A.重新取样(100-1000 time).
由于HCV基因1型用干扰素治疗的效果不佳。
病毒基因型分型对预防策略的影响（HEV）
净化环境，保 持水源清洁
给易感者接种 HEV疫苗
免食生肉
给猪接种HEV 疫苗，切断传 染源头。
净化环境，保 持水源清洁
给易感者接种 HEV疫苗
传染的来源
利用构建系统发生树的方法，可揭示时间 和地点相距较远的病毒分离株之间的同源 性，从而发现某一流行事件是过去流行株 复发还是从外界传入，对控制病毒的流行 具有重要意义。
基于特征的建树方法
不计算序列间的距离，而是将序列中有差异的位 点作为单独的特征，并根据这些特征来建树。
ML－最大似然法
选取一个特定的替代模型来分析给定的一 组序列数据，使得获得的每一个拓扑结构 的似然率都为最大值，然后再挑出其中似 然率最大的拓扑结构作为最优树。
最大似然法的建树过程是个很费时的过程 ，因为在分析过程中有很大的计算量，每 个步骤都要考虑内部节点的所有可能性。
指导疾病的预防（HEV genotype Ⅰ Ⅳ）
有助研究病毒的分子流行病学意义
揭示传染的来源
监控和预测
为疫苗的选定提供依据
基因分型对HCV临床治疗的指导意义
HCV（丙型肝炎病毒）基因分型及血清HCV RNA定量测定对于预治疗疗效及决定治疗方案有重 要意义。 非基因1型（2、3型）感染者用干扰素加小剂量 利巴韦林800mg/d治疗24周即可获得较好的疗效。 而基因1型者疗效较差（特别是病毒负荷较高者 ），应给予更长的疗程（48周），并需更大剂量的 利巴韦林（1000~1200mg/d）。
系统发育树的检验
系统发生树的构建
• （1）序列比对与排序； • （2）系统发育树的重建； • （3）结果的检验。
序列比对与排序
• 序列比对与排序是构建系统发育树、进行系统发 育分析的前提和必要条件。在古DNA研究中，序 列比对的目的就是建立起所检测序列与其他序列 的同源关系，提取系统发育分析数据集。 • 序列比对有各种不同的方法，这些方法都是将同 源序列位点上相同或相似残基（称匹配位点）与 不相似残基（称不匹配位点）按一定的记分规则 转化成序列之间相似性或差异性（距离）数值进 行比较。 • ClustalX (ClustalW) 是进行此项工作的经典程序。
最大简约树的构建
• 最大简约法应用于序列数据构建包括以下 几个步骤： ⑴ 确定所有的信息位点， ⑵ 对所有可能的树型，计算每个信息位点 上的发生核苷酸替代的最低次数，并对所 有信息位点的最低替代数目求和， ⑶ 选择核苷酸替代次数总和最小的树作为 最简约谱系树。
一致树consensus tree
• 在简约法中会产生多颗等价的简约树是很常 见的，大量近源序列组成的数据集有时会产 生成百上千棵树，无法得到准确的系统发育 信息。此时最好的办法是将所有的谱系树合 成为一个谱系树，即一致树。 • 一致树可分为： 严格一致树（strict consensus tree） 多数一致树（majority-rule consensus tree）
第十章 古DNA数据分析
主要内容
• • • • 系统发育分析 遗传多维尺度分析 主成分分析 群体遗传学分析
系统发育分析
• 系统发育(phylogeny)是指一群有机体发生或进化 的历史。 • 系统发育树(phylogenetic tree），也称为谱系发 育树、谱系树、系统发生树、系统树)就是描述这 一群有机体发生或进化顺序的拓扑结构 。 • 系统发育分析(phylogenetic analysis)就是指利用 现有生物的形态或分子生物学数据重建 (reconstruction)系统发育树推断系统发生的过程。
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A Web-Based T ree V iew(TV)Programfor the Visualization of Phylogenetic TreesYufeng Zhai,Jason Tchieu,and Milton H.Saier,Jr.*Department of Biology,University of California at San Diego,La Jolla,CA92093-0116,USAAbstractWe designed a web-based program,T ree V iew (TV),which uses a dynamic data structure algo-rithm to draw the phylogenetic tree for a family of homologous proteins.This program has a user-friendly interface and can be easily implemented into other programs for convenient protein sequence analysis.It is available at our web site: ~yzhai/biotools.html. IntroductionSequence alignments and phylogenetic trees can provide information about the evolutionary history of the proteins that comprise a family.In a phylogenetic tree,every terminal branch represents a specific protein,each branch intersection point defines a relationship between two sequence-related proteins, and the lengths of the two branches indicate the evolutionary distance between them.There are cur-rently several tools for drawing phylogenetic trees,for example TREEVIEW(Page,1996),PHYLO_WIN (Galtier et al.,1996),TREECON(Van de Peer and De Wachter,1997),SPECTRUM(Charleston,1998) and PhyloDraw(Choi et al.,2000).These methods allow users to easily and interactively manipulate the shapes of phylogenetic trees.All of these programs have at least one drawback:they are designed to be platform-dependent and have to be installed on a local computer before they can be used.Among these programs,TREEVIEW and SPECTRUM can only be installed on machines running MacOS or Microsoft Windows,TREECON and PhyloDraw can only be run o n m a c h i n e s w i t h M i c r o s o f t W i n d o w s,a n d PHYLO_WIN is a graphic tool only for Unix machines running X-windows.Since completion of the sequencing of several prokaryotic and eukaryotic genomes,an abundance of biological and genomic information has become freely accessible over the internet.One outcome of this fact is the proliferation of web-based bioinformatic tools allowing annotation and organization of the data.These tools allow biologists to diagnose sequences, search databases,assign proteins to families and provide functional annotations.Here we describe a novel Tree View(TV)program and its implementation to two other programs, Clustal W(Thompson et al.,1997)and TC-Blast(see below).The TV program allows phylogenetic trees to be drawn dynamically(on thefly).Thus,from the input data such as a multiple alignment,a phylogenetic tree can be automatically generated.This advance allows the derived trees to be viewed by any internet browser. Description of the ProgramThe TV program can read treefiles produced by Clustal W(Thompson et al.,1997).There are two main sections on the start page for data input.Thefirst is for naming the tree,which has a default value of UNKNOWN;the second is for uploading the tree data. Users may upload their saved treefiles using the Browse button.Currently,the program can only draw the radial tree type;more functions will be added in later versions.The output is similar to that of the TREEVIEW program.The protein sequence names are provided at the end of each branch;the unit lengths of branches are shown at the bottom of thefigure. Implementation of TV into Other ProgramsClustal WClustal W is a popular sequence alignment program developed by Thompson et al.(1997).It can be downloaded for local usage and can be used from the internet.For example,both the European Bioinfor-matics Institute(/clustalw/)and the Swiss Institute of Bioinformatics(http://www.ch. /software/ClustalW.html)provide these tools,but none of them supports automatic drawing of a phylogenetic tree.By adding our newly developed TV program,users can view the sequence alignment and phylogenetic tree at the same time.The Clustal W program also has a link to the AveHAS program(Zhai and Saier,2001),by which users can automatically generate average hydropathy,average amphipathicity and average similarity plots upon entry of a multiple alignment.The Clustal W program provides more functions and information allowing users to analyze a cluster or clusters of homologous proteins.TC-BlastTC-Blast is a similarity search program specifically designed for our transporter classification(TC)system (Saier,2000;web site:~msaier/ transport/).The details of the program will be*For correspondence.Email.msaier@;Tel.(858)534-4084;Fax.(858)534-7108.J.Mol.Microbiol.Biotechnol.(2002)4(1):69–70.JMMB Bioinformatics Corner #2002Horizon Scientific PressFlow Cytometry in MicrobiologyTechnology and Applications Edited by: MG Wilkinson c. 230 pp, June 2015Hardback: ISBN 978-1-910190-11-1 £159/$319 Ebook: ISBN 978-1-910190-12-8 £159/$319A thorough description of flow cytometry and includes practical and up-to-date information aimed specifically at microbiologists.!Probiotics and PrebioticsCurrent Research and Future Trends Edited by: K Venema, AP Carmo c. 560 pp, August 2015Hardback: ISBN 978-1-910190-09-8 £180/$360 Ebook: ISBN 978-1-910190-10-4 £180/$360With 33 chapters; an invaluable source of information and essential reading for everyone working with probiotics, prebiotics and the gut microbiotflora.!EpigeneticsCurrent Research and Emerging Trends Edited by: BP Chadwick c. 330 pp, June 2015Hardback: ISBN 978-1-910190-07-4 £159/$319 Ebook: ISBN 978-1-910190-08-1 £159/$319Thought-provoking discussions on classic aspects of epigenetics and on the newer, emerging areas.!Corynebacterium glutamicumFrom Systems Biology to Biotechnological ApplicationsEdited by: A Burkovski c. 190 pp, May 2015Hardback: ISBN 978-1-910190-05-0 £159/$319 Ebook: ISBN 978-1-910190-06-7 £159/$319Comprehensive and authoritative overview of current research; essential reading for everyone working with Corynebacterium and related organisms.!Advanced Vaccine Research Methods for the Decade of VaccinesEdited by: F Bagnoli, R Rappuoli c. 462 pp, April 2015Hardback: ISBN 978-1-910190-03-6 £180/$360 Ebook: ISBN 978-1-910190-04-3 £180/$360 A thorough and up-to-date review of vaccinology research in age of omics technologies. 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RECOMMENDED READING presented in a future paper describing the release of our transporter classification database(TC-DB).The TV program has been integrated into the TC-Blast program.Following a Blast search of TC-DB,users can automatically view the phylogenetic tree and sequence alignment of retrieved transporter family members.One can also view the position of the queried sequence in the phylogenetic tree to identify its relationship to established family members.In the latter multiple alignment,the query sequence will be presented in bold so as to facilitate comparison of the new sequence with the other established transporter sequences.ConclusionThe TV program is a CGI(common gateway interface) program written in the C programming language.This program has been integrated into Clustal W and TC-Blast.Both the TV and Clustal W programs are available on our web site(http://www.biology.ucsd. edu/~yzhai/biotools.html).The TC-Blast program will be released in conjunction with our transporter classi-fication database(TC-DB)in the near future. ReferencesCharleston,M.A.(1998).Spectrum:Spectral analysis of phylogenetic data.Bioinformatics,14:98–99.Choi,J.H.,Jung,H.Y.,Kim,H.S.,and Cho,H.G.(2000).PhyloDraw:A phylogenetic tree drawing system.Bioinformatics,16:1056–1058. Galtier,N.,Gouy,M.,and Gautier, C.(1996).SEAVIEW and PHYLO_WIN:Two graphic tools for sequence alignment and molecular put.Appl.Biosci.,12:543–548.Page,R.D.(1996).TreeView:An application to display phylogenetic trees on personal put.Appl.Biosci.,12:357–358. Saier,M.H.,Jr.(2000).A functional/phylogenetic classification system for transmembrane solute transporters.Microbiol.Mol.Biol.Rev., 64:354–411.Thompson,J.D.,Gibson,T.J.,Plewniak, F.,Jeanmougin, F.,and Higgins,D.G.(1997).The CLUSTAL_X windows interface:Flexible strategies for multiple sequence alignment aided by quality analysis tools.Nucleic Acids Res.,25:4876–4882.Van de Peer,Y.,and De Wachter,R.(1997).Construction of evolutionary distance trees with TREECON for Windows:Accounting for variation in nucleotide substitution rate among put. Appl.Biosci.,13:227–230.Zhai,Y.,and Saier,M.H.,Jr.(2001).A web-based program for the prediction of average hydropathy,average amphipathicity and average similarity of multiply aligned homologous proteins.J.Mol. Microbiol.Biotechnol.,3:285–286.70Zhai et al.。