Creating Phylogenetic Trees With MEGA

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Creating Phylogenetic Trees withMEGAPrat Thiru

Outline MEGA FeaturesBackground on Phylogenetic TreesBrief Overview of Tree Building MethodsMEGA Demo

MEGA Easy‐to‐use software with multiple features Features:–––––––Aligning sequencesEstimating evolutionary distancesBuilding trees using several methodsTesting tree reliabilityMarking Genes/DomainsTesting for selectionComputing sequence statistics

Phylogenetics Study of evolutionary relationship Phylogenetic tree is a graphical representation of theevolutionary relationships Phylogeny of ––––SpeciesStrainsGenesMetabolic Pathways Trees can be inferred by morphology or molecularinformation

Why Create Phylogenetic Trees? Reconstruct evolutionary history Draw conclusions of biological functions whichmight not be apparent Pre‐computed trees (eg. Ensembl, Pfam)might not include genes or species of interest

Parts of a Tree Nodes: taxonomic units (eg. genes, species, etc.)– Internal: ancestral state BifurcatingMultifurcating(Internal) Node– External: Operational Taxonomic Units (OTUs) Branches: relationships among the taxonomicunits (ie. ancestor‐descendent relationship).– Clade Branch Length: number of changes that haveoccurredTopology: branching patternRooted vs Unrooted treeOperationalTaxonomicUnit (OTU)

Steps to Create Phylogenetic Trees Identify and acquire the sequences that are tobe included on the tree Align the sequences (MSA using ClustalW,T‐Coffee, MUSCLE, etc.) Estimate the tree by one of several methods Draw the tree and present itFrom Hall, B.G. (p.3 ‐ see Further Reading Slide)

Tree Building ny‐ProbabilisticExample:Distance MatrixABCDA 10.8 0.4 0.6B 0.8 1 0.6 0.4C 0.4 0.6 1 0.8D 0.6 0.4 0.8 1Character State MatrixSpecies ASpecies BSpecies CSpecies DACTTCAGTTCCGTACCCTAC

Distance‐based Unweighted Pair Group Method with Arithmetic mean (UPGMA) Neighbor‐Joining (NJ)– Start with all taxa in a single node and decompose with each iteration– Pair of nodes pulled out (grouped) at each iteration are chosen so thatthe total length of the branches on the tree is minimized– Mutation rates are not constantbacdeaacdbecd be

Character‐based: Parsimony Preferred phylogenetic tree is the one with the fewest evolutionarysteps Identify informative sites For each possible tree, calculate the number of changes at eachinformative site Sum total number of changes for each possible tree, the tree withthe smallest number of changes is selected as the most likely tree*informative site

Character‐based: Probabilistic Maximum Likelihood– At each site, the likelihood is determined by evaluating theprobability that a certain evolutionary model (eg. BLOSSUM orPAM matrices) has generated the observed data.– The likelihood’s for each site are then multiplied to providelikelihood for each tree– Choose the tree with maximum likelihood Bayesian Inference– Recent variant of ML– Finds a set of trees with the greatest likelihood given the data

Comparison of Methods Distance‐based– Results in a single tree– UPGMA: reliable only for closely related species; replacedby NJ– NJ: fast, suitable for large dataset Character‐based––––Multiple trees will be foundMP: long branch attraction problemML: statistically well founded but slow for large datasetBI: faster to assess support for tree, prior distributionparameters must be specifiedHall, B.G. (p.60 ‐ see Further Reading slide)

Which Method to Use? Accuracy– looking at how close the estimated tree is to the true tree resulted (in order ofdecreasing accuracy):Bayesian Inference Æ Maximum Likelihood Æ Maximum Parsimony Æ Neighbor JoiningEase of Interpretation single vs multiple trees– Time and Convenience*Data SetNeighbor ferenceSmall Data1 sec3 sec6 sec‐Small Data**9 sec10 min1 hr 34 min29 min 40 secLarge Data1 sec22 sec3 min 29 sec‐Large Data**86 sec10 hr 2 min58 hr6 hr 33 min* MacPro dual processor (2.6 GHz); NJ and MP was using MEGA 4.0, ML by PHYML, and BI using MrBayes** with reliability estimateHall, B.G. (p.160 ‐ see Further Reading slide)

Tree Reliability: Bootstrapping A statistical re‐sampling procedure commonlyused for providing confidence to branches inphylogenetic trees A measure of repeatability, the probability thatthe branch would be recovered if the taxa weresampled again Bootstrapping values are typically presented from1000 repeated calculations Bootstrap values of 70% is recommended

Some Tips Use more than one method Use more than one software package Examine more than one tree if multiple treesare generated Bootstrap your data Homology vs Homoplasy Consider another intermediate taxa to resolverelationship, if needed

Phylogenetic Tree Software PHYLIP (the PHYLogeny Inference phylip.html PAUP*:http://paup.csit.fsu.edu MrBayes:http://mrbayes.csit.fsu.edu MEGA(Molecular Evolutionary Genetic Analysis)http://www.megasoftware.netA More Comprehensive Listing of Phylogeny phylip/software.html*Commercial software

SummaryNature Reviews Genetics 4, 275‐284 (2003)

Further Reading Kumar, S., Dudley, J., Nei, M., and Tamura, K., MEGA: A biologist‐centric software for evolutionaryanalysis of DNA and protein sequences. Briefings in Bioinformatics 9: 299‐306 (2008)Hulsenbeck, J.P., and Ronquist, F., MyBayes: Bayesian inferences of phylogeny. Bioinformatics17:754‐755 (2001)Holder, M., and Lewis, P.O., Phylogeny estimation: traditional and Bayesian approaches. NatureReviews Genetics 4: 275‐284 (2003)Books: Phylogenetic Trees Made Easy: A How‐to Manual 3rd Ed. Hall, B.G. (2008) Inferring Phylogenies. Felsenstein, J. (2003)

Background on Phylogenetic Trees Brief Overview of Tree Building Methods MEGA Demo. MEGA Easy‐to‐use software with multiple features Features: – Aligning sequences – Estimating evolutionary distances – Building trees using severa

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