林仲彥. Dec 4,
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1 林仲彥 Dec 4,
2 Coding Characters and Defining Homology Classical phylogenetic analysis by Morphology Molecular phylogenetic analysis By Bio-Molecules
3 Steps of Phylogenetic Analysis New Sequence Homology Search Alignment Phylogenetic Tree
4 Elements in a Phylogenetic Tree The tree is composed of nodes connected by branches. distance scale : scale which represents the number of differences between sequences (e.g. 0.1 means 10 % differences between two sequences) branch length : often represents the number of changes that have occurred in that branch. node : a node represents a taxonomic unit. Internal nodes External nodes root : is the common ancestor of all taxa. branch (edge): defines the relationship between the taxa.
5 Trees Only Represent The Order Of Branching Same topology in a different style Both trees have identical topologies, with some of the internal nodes rotated. ( David A. Baum et al., Science 11 November 2005:Vol no. 5750, pp )
6 The Ways to Construct the tree Distance-matrix methods Neighbor-joining Fitch-Margoliash method Using outgroups Maximum parsimony Branch and bound MALIGN and POY Maximum likelihood (Statistics Based) Bayesian inference (Probability Based)
7 Phylogeny Packages
8 Phylip
9 Interactive Interface for Phylip At this stage they do not have a mouse-windows interface for PHYLIP
10 Nucleic Acids Research, 2005
11 General Pipeline for Phylogenetic Analysis Substitution Model Tree Construction Multiple Sequence Alignment Bootstrapping Selection of inference Methods Evaluate phylogenetic tree
12 Flowchart of Analysis
13 Phylogenetic Analysis Tool
14 POWER: PhylOgenetic WEb Repeater Provide a seamless way to conduct the complex phylogenetic analysis for Biologists An integrated and user-optimized framework for biomolecular phylogenetic analysis POWER uses an open-source LAMP (Linux, Apache, MySQL, PHP) structure and infers genetic distances and phylogenetic relationships using well-established algorithms (ClustalW and PHYLIP) Through a user-friendly web interface, users can sketch a tree effortlessly in multiple steps Furthermore, iterative tree construction can be performed by adding sequences to, or removing them from, a previously submitted job
15 Integration of Phylip Packages into Automatic Flow
16 Inside of POWER
17 POWER: PhylOgenetic WEb Repeater Nucl. Acids Res : W553-W556
18 PhylOgenetic Web Repeater (POWER) Data Input MSA parameter selection Phylogeny inference
19 PhylOgenetic Web Repeater (POWER) Options of bootstrapping Selection of substitution model Selected method for phylogeny inference
20 PhylOgenetic Web Repeater (POWER) Result and Logs Online or as bookmark Online Tree editor Or notification Used parameters Downloaded files Re-perform the process by items added or deleted
21 PhylOgenetic Web Repeater (POWER) Add/ delete sequences to invoke new job
22 Publication in POWER Mol. Biol. Evol. 23(2): Bayesian Bayesian MP MP
23 Service Usage of POWER from 2005 July. Accumulative Visit by Country Accumulative Sequences by Country
24 Service Usage of POWER from 2005 July. More than 228,000 sequences Near 11,800 Visits
25 Automatic Online Demonstration in the Demo page
26 Conduct Distance Method in POWER The DNA sequences were aligned to known sequences in the GenBank database using BLAST (Altschul et al., 1990). Phylogenetic relationships were inferred using the neighbour joining method (NJ), based on the Kimura two-parameter model (K2P), in the PhylOgenetic WEb Repeater (POWER) (Lin et al., 2005).
27 NJ with 1000 Replicates in POWER Phylogenetic tree of urea transporters across taxa. The protein sequences were aligned using ClustalW software, followed by neighbour-joining (NJ) matrix for tree reconstruction and evaluated by means of a bootstrap of 1000 replicates at
28 Perform ML in POWER Phylogenetic tree depicting the relatedness of ACC synthase and aminotransferase protein sequences in GenBank.The phylogenetic tree was generated using the POWER server ( with default parameters for the maximum likelihood method (ML) and molecular clock, but without bootstrapping or random input of sequences.
29 Execute MP in POWER Phylogenetic comparison of fish full-length FSH-R and LH-R amino acid sequences analysed by POWER using maximum parsimony (MP) and default settings. A rooted consensus phylogenetic tree generated by means of the Neighbor-Joining algorithm, using the LGR sequence of the fruit fly (Drosophila melanogaster) and sea lamprey (Petromyzon marinus) as outgroups was drawn with njplot. Bootstrap values from 1000 replicates are indicated for each tree node.
30 POWER Listed in PHYLIP Programs maintained by Joe Felsenstein Recent listings: POWER server (26 August 2007) to align sequences and infer phylogenies, BioToolKit by CSHL press (BioSupplynet.com) ALL CATEGORIES / GENOMICS RESOURCES / EVOLUTIONARY AND COMPARATIVE BIOLOGY (80) Bioinformatics Links Directory DNA : Phylogeny Reconstruction ONLINE ANALYSIS TOOLS ( ExPASy (Phylogenetics and taxonomy databases & resources)
31 Distance Method, MP and ML Which method should we choose? The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees. ML is broadly similar to the maximum-parsimony (MP) method, but maximum likelihood allows additional statistical flexibility by permitting varying rates of evolution across both lineages and sites. ML, a better choice?
32 Maximum Likelihood Conditional probability of the data (Aligned sequences) given a hypothesis (a model of substitution with a set of parameter ɵ, and the tree τ, including topology and branch lengths) L(τ, ɵ )=Prob(Data τ, ɵ ) Or Prob(Aligned Sequences tree, model of evolution)
33 Relationships among some standard models of nucleotide evolution Trends in Genetics Volume 17, Issue 5, 1 May 2001, Pages
34 Illustration of DNA Substitution Model GTR (for four characters, as is often the case in phylogenetics) requires 6 substitution rate parameters (x1~x6), as well as 4 equilibrium base frequency parameters.
35 Illustration of Models for DNA
36 Models of Amino Acid Replacement Trends in Genetics Volume 17, Issue 5, 1 May 2001, Pages
37 陳淑華 PLoS ONE, 2009
38 Background for PALM Likelihood methods in phylogenetics relaxes the parameters for varying rates of evolution across both lineages and sites, which is robust in dealing with various extend of input sequence similarity. Model fitting has been suggested for many years, but many researchers select models arbitrarily. They often feel confusing either in making choice among models, or in dealing with the conflict on the results concluded by different models. The computing of likelihood method is intensive. Thus the MLbased model selecting procedure is hard to implement. Here we present the way to identify the best-fit model based on liklihood measurement. Consequently, model fitting is possible to be a routine practice integrated in a phylogenetic analysis.
39 Motivation I Provide a seamless way to conduct the complicated phylogenetic analysis for biologists and biomedical researchers. An integrated and user-friendly framework for conducting molecular phylogenetic analysis PALM is constructed on an open-source LAPP (Linux, Apache, PostgreSql, PHP) structure PALM infers genetic distances and phylogenetic relationships using well-established algorithms (ClustalW, PhyML, ProtTest, Modeltest) in an automatic pipeline.
40 Motivation II Fitness of model can be measured and selected by following criteria: likelihood ratio tests (hlrts), Akaike information criterion (AIC), and Bayesian information criterion (BIC) PALM helps user to construct the phylogenetic relationship by ML-based method with bootstrap using the best-fit substitution model. Through the friendly web interface, users can sketch a phylogenetic tree effortlessly Furthermore, iteration on phylogenetic reconstruction is possible by adding sequences to, or removing them from a previously result.
41 Component Programs of PALM PhyML 3.0 ModelTest 3.7 ProtTest 2.0 ClustalW ReadSeq
42 Models Used in PALM For DNA (56 models) JC69, K80, F81, HKY, TrN, TrNef, K3P, K3Puf, TIM, TIMef, TVM, TVMef, SYM, GTR Options of +I, +G For Protein (112 models), Time consuming LG, DCMut, JTT, MtREV, MtMam, MtArt, Dayhoff, WAG, RtREV, CpREV, Blosum62, VT, HIVb, HIVw Options of +I, +G, +F
43 Flowchart of PALM > Seq1 AAAATTTC >Seq2 AATTCGGAC... Sequences in Fasta Input FASTA Sequence alignment Aligned Sequence Input Aligned sequences DNA Protein Add/ delete nodes & re-submit input PhyML/ Modeltest/ PALMmonitor (56 Models) PhyML/ Prottest/ PALMmonitor (112 Models) Best Model selected by AIC/ AICc/ BIC/ LnL Controlled by PALM Daemon Bootstrap Result & Tree Reconstruction by PALMtree bootstrapping PhyML-MPICH2/ PALM Parallel Job controller
44 Distribution Computing by PalmMonitor for the Likelihood Estimation of Models 56 for DNA/ 112 for Protein 7 parallel processes n n n n n n n
45 Input and Output of PALM Input format (Protein and DNA) FASTA format Phylip format: Aligned Sequences User tree (if a valid tree is submitted) Output Tree topology Tree file in Newick format Aligned sequence in phylip format The best model selected by PALM Likelihoods of all available models > Seq1 AAAATTTC >Seq2 AATTCGGAC.....
46 Result of PALM C A D B E
47 PALMtree Unroot Tree Original Tree Tree in midpoint
48 Demo Flash of PALM
49 Some Suggestions Please be patient and make a reasonable choice of the input sequence set Only well aligned sequences lead to meaningful phylogenetic result. RNA editing may introduce bias during analysis. Avoid those regions that may have such conditions.
50 Bootstrap (BS) Analysis Bootstrap analysis is the most popular method for statistical evaluation of phylogenies. In general: BS >95%: Often close to 100% confidence in that branch BS>75%: Often close to 95% confidence in that branch BS<75% : Maybe a correct clade, while the original bias cannot be corrected by the re-sampling process.
51 Input Sequences Make the Tree Different HIV Hraber et al. Virology Journal :103 doi: / x-3-103
52 Future Plans for PALM Gateway to integrate users-defined substitution models Stand-a-lone version of PALM Improve and optimize the performance of whole pipeline by applying parallel computing/cloud computing Implement of advanced, sophisticated phylogenetic inference methods such as MrBayes.
53 Acknowledgement Chia-Ling Chen Chieh-Hwa Lin Li-Wei Lai Shu-Juan Hsu Ming-Hsin Tasi Chao A. Hsiung Daniel, Sheng-Yao, Su Pan-Han Kuo Tengi Huang Chen-Zen Lo Linda, Yi-Shuian Lu
54 Bioinformatics Core for Genomic Medicine and Biotechnology Development
55
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