Phylogenetics (ALWAYS UNDER CONSTRUCTION)
BIOL 4300--01 Fall 2024
Syllabus
This course is co-instructed with Dr. Heath Ogden
Course description: This course delves into the principles and methodologies of phylogenetics, equipping students with the necessary skills to explore evolutionary relationships among organisms and genes. Emphasizing the importance of "tree-thinking," students will learn to interpret and construct phylogenetic trees, gaining insights into their implications for evolutionary biology and specific disciplines for their major. Key topics will include sequence alignment techniques, parsimony, maximum likelihood, and Bayesian tree reconstruction methods, and concepts of nodal support and trait evolution. Through hands-on projects, students will design reproducible workflows for phylogenetic inference and compile novel molecular datasets from reputable online databases. The course culminates in the application of phylogenetic reconstruction methodologies on datasets created by students. Participants will also develop skills in professional communication by synthesizing their analysis and results into formats suitable for scientific presentation, including posters, oral presentations, and manuscripts. By the end of this course, students will possess a comprehensive understanding of phylogenetic analysis and the ability to contribute meaningfully to the field of evolutionary biology and beyond through rigorous scientific inquiry and effective communication.
Learning Objectives
Upon completion of this course, students will be able to: 1. Demonstrate tree-thinking and its relation to phylogenetic trees. 2. Understand the basic principles of phylogenetic methods, such as: sequence alignment; parsimony, likelihood and Bayesian tree reconstruction; nodal support; trait evolution; among others. 3. Design a reproducible pipeline for phylogenetic inference 4. Compile a novel molecular dataset from appropriate online databases. 5. Execute phylogenetic reconstruction methodologies on the generated dataset 6. Synthesize a professional communication (poster, oral, manuscript, etc.) of the analysis, results, and interpretation of the phylogenetic project.
Schedule (Subject to Change)
Week | Dates | Topic | Assignment | Programs | Recommended Reading |
---|---|---|---|---|---|
1 | Aug. 20Aug. 22 | Welcome/IntroTree Thinking | Tree Thinking Activity | ||
2 | Aug. 25Aug. 27Aug. 29 | Tree ThinkingParsimony and SupportParsimony and Support | Tree Reconstruction ActivityGroups and Topic Picked-Aug. 29 | Handbook: Ch. 8 | |
3 | Sep. 1Sep. 3Sep. 5 | NO CLASS--LABOR DAYAlignmentsAlignments | Alignment Activity | FigTreeMAFFTMUSCLE | Handbook: Ch. 3 |
4 | Sep. 8Sep. 10Sep. 12 | Alignment ActivityRetrieving DataRetrieving Data | GitHub Made-Sep. 8Lit Review/Intro-Sep. 12 | GenBankRLinux<> | Handbook: Chapter 2 |
5 | Sep. 15Sep. 17Sep. 19 | Retrieving DataTrimming DataAligning your own data | Turn in dataset files-Sep. 19 | ||
6 | Sep. 22Sep. 24Sep. 26 | Model TestingModel TestingPartitioning and Super Matrices | Turn in Alignments-Sep. 22Model Testing Activity | IQ-Tree | Handbook: Ch. 4 |
7 | Sep. 29Oct. 1Oct. 3 | Maximum LikelihoodMaximum LikelihoodIQ-Tree Activity | ML Activity | IQ-Tree | Handbook: Ch. 6 |
8 | Oct. 6Oct. 8Oct. 10 | Bayesian MethodsBayesian MethodsBEAST Activity | BEAST Activity | BEAST2 | Handbook: Ch. 7 |
9 | Oct. 13Oct. 15Oct. 17 | Tree visualizationTree ActivityFALL BREAK--NO CLASS | R | Revell and Harmon: Ch. 1 | |
10 | Oct. 20Oct. 22Oct. 24 | Comparative Approaches | Phylogenetic Signal Activity | R | Revell and Harmon: Ch. 2-4 |
11 | Oct. 27Oct. 29Oct. 31 | Flex WeekFlex WeekPaper Discussion | TBD | ||
12 | Nov. 3Nov. 5Nov. 7 | Group ProjectsGroup ProjectsPaper Discussion | Draft Trees with Support-Nov. 3 | TBD | |
13 | Nov. 10Nov. 12Nov. 14 | Group ProjectsGroup ProjectsPaper Discussion | TBD | ||
14 | Nov. 17Nov. 19Nov. 21 | Group ProjectsGroup ProjectsPaper Discussion | TBD | ||
- | Nov. 24Nov. 26Nov. 28 | THANKSGIVING BREAK--NO CLASS | |||
15 | Dec. 1Dec. 3Dec. 5 | Final TouchesFinal PresentationsFinal Presentations | Final Tree (Bayesian or ML)-Dec. 3 | ||
16 | Dec. 10 | Final GitHub Submission | Dec. 10 |