PHYLOGENETICS WITH MEGA
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The document introduces phylogenetics and the use of the MEGA 6.0 software for phylogenetic analysis, emphasizing the importance of classification in understanding evolutionary relationships. It discusses traditional and molecular taxonomy, explains species versus gene trees, and highlights various phylogenetic methods and statistical validations such as bootstrapping. The document also outlines the functionalities of MEGA 6.0, its user-friendly features, and the steps to perform phylogenetic analysis.
PHYLOGENETICS WITH MEGA
- 1. PHYLOGENETICS An introduction to the concepts and analysis using MEGA 6.0
- 2. Today’s Objectives • To introduce the basis concepts involved in phylogenetic analysis. • To learn the usage of the phylogenetic package MEGA 6.0 • To discuss the manner in which you can apply phylogenetic analysis in your research approach, thesis and publications.
- 3. Why use Phylogenetics ? • The human mind is naturally inclined to classify information. • Classification facilitates logical understanding as well as the detection of heuristic patterns within data sets. • Logical understanding of a process facilitates the process of discovery.
- 4. Where will it be of use to me? • Classifying my sequence data within a global perspective. • Finding unique regions within my sequence data by comparison with a global data set. • Identification of genes which have not yet been widely characterized. • Infinitely many possibilities
- 5. Traditional Classification schemes • Based on Phenotypic traits (Phenetic) and taxonomic classifiers (TU) • Low level of resolution • Not applicable to molecular data • Difficult to resolve taxonomic ambiguities at higher levels.
- 6. From TUs to Genomic databases • DNA technology prompted a quantum shift in the resolving power of phylogenetics. • TU: < 100 classifiers • Amino Acids: Millions of combinations of AAs • Genomic level: Billions of bp of nucleotide data Does more information solve the problem?
- 8. Species trees • A species tree establishes the hierarchy of a species within a globally accepted framework of classification. • ITS:16s • ITS: rDNA • ITS: chloroplast and mitochondria • Genes: rbcL, ADH, cytC, Ig(SC)
- 9. Crab rRNA sequence data used to construct UPGMA tree, Note the out-group species that has been added to establish a perspective scale.
- 10. Gene trees • Gene trees facilitate the understanding of evolutionary processes occurring within genes across taxa or within a species. • The rates of evolution offer insights into the manner in which genes evolve as a family. • Gene trees can be transformed into species trees if they conform to evolutionary criteria.
- 11. Species v/s Gene trees • Which one do we select? The choice is determined by what we intend to characterize: Is it the organism within a genus / species? OR Is it a gene which is distributed across taxa?
- 12. Molecular taxonomy based on genes • Prokaryotes: 16s rDNA • Higher organisms: ITS rDNA, Cp, Mt • Do you want an evolutionary tree? • Does your “molecular tree” corroborate your “taxonomic tree”?
- 13. D. affinidisjuncta D. heteroneura D. mimica D. adiastola D. nigra S. albovittata D. crassifemur S. lebanonensis D. mulleri D. melanogaster D. pseudoobscura 0.000.050.100.150.200.25 Gene tree constructed using the Alcohol Dehydrogenase (ADH) gene from Drosophila spp. (UPGMA)
- 14. The molecular clock • A digital clock displays time as the cumulative function of the frequency of a silicon crystal. • A molecular clock graphically depicts evolution as the function of changing nucleotide / amino acid frequency versus time.
- 15. A highly simplified and idealized molecular clock ! The red bar is a gene, the colored bars represent nucleotide positions which change as a function of time.
- 16. Phylogenetic trees •Distance based methods: inclusive •Maximum parsimony methods: assumptive
- 17. NJT • Constructed Purely on the basis of pairwise genetic distance. • No prior assumptions are made pertaining to tree topology and branch lengths
- 18. Japanese Korean Southern Chinese Australian Papuan North Amerind South Amerind Finn Italian German English San Bantu Pygmy Nigerian 0.01 Neighbor Joining Tree (NJT) based on human genetic distance matrix: compares Pairwise Genetic Distances only
- 19. UPGMA • Originally developed for Phenogram construction (Sokal & Michener, 1958) • Adapted for Dendrogram construction • Can be used when there is a correlation between the distance measure used and the evolutionary timescale.
- 20. Japanese Korean Southern Chinese North Amerind South Amerind Italian Finn German English Australian Papuan San Pygmy Nigerian Bantu 0.000.010.020.030.040.05 UPGMA tree based on human genetic distance matrix: Assumes a constant rate molecular clock
- 21. VALIDATION: Bootstrapping • The concept of parsimony. • This is a re-sampling method by replacement with the same data matrix. • It allows calculation of standard deviations and variances.
- 22. Zea Oryza Nicotiana Pinus Marchantia Odontella Porphyra Synechocys Cyanophora Euglena 100 91 100 100 100 100 100 0.05 Bootstrap consensus tree constructed using the NJT algorithm. Based on chloroplast DNA protein coding regions.
- 23. Zea Oryza Nicotiana Marchantia Pinus Odontella Synechocys Porphyra Cyanophora Euglena 100 100 100 100 100 100 100 0.000.050.100.150.20 Bootstrap consensus tree constructed using the UPGMA algorithm Based on Chloroplast DNA protein coding regions
- 24. Why use MEGA 6.0 ? • Single platform, combines the functions of BIOEDIT,CLUSTALW, PAUP and TREEDIST • Imports FASTA files directly from GenBank: No editing! • Publication quality output / statistical corroboration. • Executes on your laptop / desktop. • User friendly GUI • Versatile / Flexible • Highest number of citations • Open source / Freeware • No codes to memorize
- 25. What can MEGA 6.0 do for you? • Download data from a Database / File / Sequencer • Align data using CLUSTAL W • Perform phylogenetic analysis using various Algorithms • Graphically depict phylogenetic trees • Perform evolutionary tests: Tajima’s Molecular Clock, Tajima’s neutrality, Z-test, Fishers-exact test, Nei- Gojobori distance
- 26. Getting started with MEGA • Input file • Processing commands • Output file
- 28. THE INPUT FILE • FASTA format • ABI format • Distance matrix files
- 29. THE ALIGNMENT COMMAND • This step requires discretion. After sequences have been aligned using CLUSTALW, 5’ and 3’ ends must be trimmed to develop a blunt composite set. • Save your output as XXXXX.MAS file • Before exiting save as XXXXX.MEG file
- 31. The ends of the composite sequence should be trimmed after CLUSTALW alignment as they can contribute significantly to error in determining true evolutionary divergence / sequence similarity
- 32. DEFINING YOUR OUTPUT • Distance Matrix File • Phylogenies: NJT / UPGMA / MP / ME • Parsimony trees • Evolutionary parameters • Molecular clocks
- 34. Some concepts to think about: • Gene clusters • Genes across geographical boundaries • Why does genetic evolution transcend species boundaries? • Why do some genes evolve faster that others? • Why do some genes evolve concurrently?
- 35. Some concepts to think about: • RNA families: clustering of ESTs • Comparative genomics within a supra genome • Evolutionary linkages within human genes
- 36. CITATION MEGA should be cited as: Tamura K, Dudley J, Nei M & Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Molecular Biology and Evolution 24:1596-1599. (Publication PDF at http://www.kumarlab.net/publications)
- 37. BIOINFORMATICS SESSION Follow the instructions on the screen and obtain your tree. If you have WIFI access to NCBI, you can develop your own unique alignments
- 38. THANK YOU “In the greater scheme of things, all systems tend to unity… all of human understanding and logic is based on this underlying principle.. and the genome is no exception… “