Peter Beerli's Classes

Graduate/Undergraduate course:
Computational Evolutionary Biology

Class Meeting

Lectures:
Tuesdays and Thursdays 1:20-2:35 PM Dirac Science Library Room 422

Instructor

Peter Beerli
Office: 150-T DSL
Email: beerli@fsu.edu
Cell: (850) 559-9664 [Text appreciated]

Office Hours

Monday usually 1:00-3:00 PM by appointment.

Objectives

This course will introduce students to methods used in phylogenetics and population genetics and writing computer programs using these methods. The primary objectives of the course are:

1. to expose students to a large set of modern methods used in theoretical evolutionary biology and learn about the details of often used methods in phylogenetic and population genetics analyses.

2. to introduce students to the programming aspects of the field. Students will learn and use the programming language Python to develop scripts and to understand the details of the methods. Learning Python will be assisted using AI technology (chatGPT) which we will use extensively to write code.

3. to empower students to develop programming and analysis skills that involve the development of scripts to change data format, execute applications, and analyze results.

Rational for the objectives

Current biological studies have started to emphasize the importance of data analytics, but still, most courses simply use packaged programs, leaving the students at a disadvantage if no courses exist that improve the computational skills of students. This course prepares students for such a challenge.

Content

Advanced computational methods are becoming increasingly important in biology. A wide range of applications — including, for instance, identifying pathogens, tracing viral transmission pathways, and reconstructing the geographic expansion of humans out of Africa — rely on evolutionary inference. This course will cover the methods currently used for evolutionary inference, the stochastic models and inference principles they are based on, and how they are implemented in practice. The students will get hands-on experience in developing computational software. We expect that the students leave the course with the necessary skills to develop their own ideas and develop projects based on simulated data sets and scripts.

Textbook

We do not use a textbook, but if you feel that you need a book use this one: Yang, Z. 2006. Computational Molecular Evolution. Oxford University Press, Oxford, England. (Book website: http://abacus.gene.ucl.ac.uk/CME/)

Grading

• Grades will be based on students’ execution of programming assignments, each of which involves understanding the algorithms, code design, and program documentation [10 points each]

• Each student will work on a project on their own during the last 4 weeks of the semester and also give a short presentation of their work in the last regular lab-meeting. [15 points for the report and 15 points for the presentation]

• I take the liberty to quiz you about your assignments if I get the impression that you let chatGPT do all the work and you do not understand what is happening in your own code. Using a tool like chatGPT does not absolve you from learning the tools/methods. The quiz will affect the grade for the assignment either positively or negatively.

• There will be no mid-term and final exams; the project substitutes for a final examination.

A student who accumulates 90% or more of the possible points will receive a grade of "A", a student who accumulates between 80% and 89% of the possible points will receive a grade of "B", a student who accumulates between 70% and 79% of the possible points will receive a grade of "C", a student who accumulates between 60% and 69% of the possible points will receive a grade of "D", and a student who accumulates less than 60% of the possible points will receive a grade of "F".

Missed/Late Assignments

Deadlines for assignments will be announced in class; late assignments will be accepted for full grade only in meidcal cases. 10% of the points are deducted per day for late assignments; An assignment 1 day late will be 90% of the points, 2 days late 80%, ... If you think that the assignments are too difficult and you cannot get it right even with the help of chatGPT, talk to me early, I will not have time on the evening of the due date.

University Attendance Policy:

Excused absences include documented illness, deaths in the family, and other documented crises, call to active military duty or jury duty, religious holy days, and official University activities. These absences will be accommodated in a way that does not arbitrarily penalize students who have a valid written excuse. Consideration will also be given to students whose dependent children experience serious illness.

Academic Honor Policy:

The Florida State University Academic Honor Policy outlines the University’s expectations for the integrity of student’s academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Students are responsible for reading the Academic Honor Policy and for living up to their pledge to “. . . be honest and truthful and . . . [to] strive for personal and institutional integrity at Florida State University.” (Florida State University Academic Honor Policy, found at http://fda.fsu.edu/academic-resources/academic-integrity-and-grievances/academic-honor-policy)

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Florida State University (FSU) values diversity and inclusion; we are committed to a climate of mutual respect and full participation. Our goal is to create learning environments that are usable, equitable, inclusive, and welcoming. FSU is committed to providing reasonable accommodation for all persons with disabilities in a manner that is consistent with the academic standards of the course while empowering the student to meet the integral requirements of the course. Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Office of Accessibility Services; and (2) request a letter from the Office of Accessibility Services to be sent to the instructor indicating the need for accommodation and what type; and (3) meet (in person, via phone, email, skype, zoom, etc...) with each instructor to whom a letter of accommodation was sent to review approved accommodations. Please note that instructors are not allowed to provide classroom accommodations to a student until appropriate verification from the Office of Accessibility Services has been provided. This syllabus and other class materials are available in an alternative format upon request. For the latest version of this statement and more information about services available to FSU students with disabilities, contact the:

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Free Tutoring from FSU

On-campus tutoring and writing assistance are available for many courses at Florida State University. For more information, visit the Academic Center for Excellence (ACE) Tutoring Services, for a comprehensive list of on-campus tutoring options, see https://ace.fsu.edu/tutoring or contact tutor@fsu.edu. High-quality tutoring is available by appointment and on a walk-in basis. These services are offered by tutors trained to encourage the highest level of individual academic success while upholding personal academic integrity.

Syllabus Change Policy

Except for changes that substantially affect the implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice.

Statement on Public Health Protocols

Class meets in person. If necessary, however, we will shift to remote instruction. There are currently no mask mandates in place at FSU, but if you feel more secure wearing a mask do so. If you feel sick please let me know by email and stay home. You can find up-to-date guidance at: https://stayhealthy.fsu.edu.

Lectures: Topic overview

Course material will be on CANVAS and also on my own website https://peterbeerli.com (classes)

1. Processes and patterns

   • Population genetics: Wright-Fisher population models, Moran model, coalescence theory, genetic drift, gene flow, selection;

   • Phylogenetics: tree structures, speciation, birth/death models, Gene tree versus Species tree

   • Mutation models: real data (microsatellite markers, SNPs, sequences), modeling data, mutation/substitution model

   • Simulation of data

2. Inference:

   • Parsimony and Distance methods

   • Maximum likelihood, Bayesian inference, Markov chain Monte Carlo,

   • Approximate Bayesian Computation

   • Model selection

   • Bootstrap/Jacknife

Each topic will include computational algorithms, problematic aspects such as convergence, biases, main focus will be on Bayesian and maximum likelihood methods.

Homework/Assignments

Homework assignment (each will be 10 points) follow the weekly outline and usually will be given on Thursdays, and expected to be turned in on next Thursday morning. The project consists of an outline (10 points), a presentation (15 points) and a report (not more than 8 pages) (15 points).

Lecture Schedule

<thead> </thead> <tbody> </tbody>

Week	days	Topic	Practice
1	8/27,29	intro/tree	chatgpt,writing and reading trees
2	9/3,5	optimality/tree search	counting trees
3	9/10,12	parsimony (fitch, sankoff)	impl. Fitch with chatgpt
4	9/17,19	mutation models	distance methods
5	9/24,26	mutation models	(finite vs infinite sites models)
6	10/1,3	likelihood	creating a likelihood program
7	10/8,10	rate heterogeneity
8	10/15,17	bootstrap/jacknive
9	10/22,24	Bayesian inference
10	10/29,31	Priors	Approximate Bayes sampler
11	11/5,7	MCMC
12	11/12,14	Coalescence 1	simulation of trees and sequences
13	11/19,21	Coalescence 2	estimation of population size
14	11/26	Project time
15	12/3,5	Project time (presentation)

Peter Beerli, August 2024