Population Genetics |
BIOL4585 J-term
Evolutionary Genomics (J-term 2021-2022); Chemistry Bldg 306
In this course, we will use cutting edge genomic techniques to understand how studying whole genomes can shed light into the fundamental processes which allow natural populations to survive, and thrive in the context of complex ecological interactions. This is an advanced course and will provide a full two-week immersion program into the world of genomics.
Programming in R and Unix is preferable but not required, as there will be resources to learn these computer languages. As part of this course, we will engage in analyses of real and simulated genomic data. Each meeting will be divided in three portions. First a lecture, second a student-lead discussion on a selected paper, lastly a practicum on genomics data.
Frequently Asked Questions:
When is the course being offered?
This is a J-term course offered January 2022. The course will meet Jan 3 - Jan14, 2022. The class meets for 4 hours, from 10 am to 3 pm. This includes a lunch break at noon.
What is the intended audience for this course?
This class is aimed at biologists as well as individuals who are very interested in evolutionary genomics. The course will have a strong emphasis on reading primary literature about how the fundamental evolutionary forces: natural selection, mutation, recombination, and demography, shape species genomes and phenotypes. We will also explore data via practicums on our UVA supercomputer (Rivanna).
I am an advanced coder, is this class relevant for me?
This class is focused on the biology of genomes, thus, even if you are an advanced coder, there may still be value in joining us to explore the wonderful world of evolutionary genomics
What items do I need for this class?
You need a computer able to connect to the internet. PC or Mac, both work. If you don't have a personal computer you may get a loaner from UVA for the term. The course will have audio visual materials to go over during class and at home so headphones can be useful too.
How can I find the course in SIS?
This course is listed in SIS as BIOL 4585 offered in the 2022 January term. Section 001-SEM (10072). Topic: Evolutionary Genomics. The course is considered a seminar with 3 credits
Do I need to know coding to do well in the class?
No, you don't need to be an advanced coder to do well on the class. As part of this course we will learn the basis of doing biology in supercomputers. These skills will be covered from scratch. There will be a project portion of the class that includes coding but I will provide you with the tools to complete it.
I am a graduate student. Can I take the class?
This class is geared towards undergraduates. But, graduate students may join on a lecture-to-lecture basis, e.g., to discuss a paper of great interest. This is contingent on instructor permission, i.e., grad students should come talk to me first!
What are the prerequisite courses?
To take this course you should have completed the introductory biology series. e.g., BIOL2200, and BIOL3020 Evolution and Ecology.
What a class session looks like:
10 am - 11 am
A lecture discussing theory and applications of the day's topic
11 am -12 pm
A paper discussion led by a student or group of students
1 pm - 2 pm
Practicum on bioinformatic skills using UVA's supercomputer Rivanna
2 pm - 3 pm
Practicums may continue or may break into small group discussions
Supercomputer Tutorials
Topics:
Materials hosted in GitHub: https://github.com/Jcbnunez/biol4585j-yey2sn
Jan 3
This lesson has four goals.
1) Get to know other students in the class and form working groups.
2) Understand the expectation and dynamics of the class.
3) Learn how to operate the supercomputer, learn the basic functions for moving bioinformatic files around.
4) Learn the properties and structures of files in biology.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/1.Introduction_to_Rivanna
Jan 4
This lesson has two goals.
1) Complete a group discussion on the history of DNA sequencing (paper 1; Shendure et al.).
2) Learn how to use R to analyze biological datasets. This introduction to R will cover primarily the tidiverse library.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/2.Introduction_to_R
Jan 5
This lesson has two goals.
1) Complete a paper discussion on applications of phylogenetics (paper 2; Dunn et al.).
2) Learn how to manipulate DNA data to build, analyze and visualize phylogenetic trees.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/3.Phylogenies
Jan 6
This lesson has three goals.
1) Complete a paper discussion on HIV evolution (paper 3; Rambaut et al.).
2) Learn the basics of genome assembly from short reads.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/4.Genome_assembly
Jan 7
This lesson has 2 goals.
1) Complete a paper discussion on inbreeding depression for the Isle Royale Wolfs (paper 4; Robinson et al.).
2) Learn the basics of mapping reads to genomes using burrows-wheeler aligners.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/5.SNP_calling
Jan 10
This lesson has three goals.
1) Complete a paper discussion on the genetic history of humanity (paper 5; Li et al.).
2) Understand the genomic signatures produced by the basic evolutionary forces: selection, drift, and migration.
3) learn how to find signatures of these evolutionary forces in panels of genomic data using dimensionality reduction (Principal component analysis; PCA).
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/6.PCA
Jan 11
This lesson has two goals.
1) Complete a paper discussion on human demography (paper 6; Novembre et al.).
2) Learn the fundamental summary statistics used to describe genetic variation in population genetics and how to estimate them on genomic data.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/7.Genetic_Diversity
Jan 12
This lesson has two goals.
1) Complete a paper discussion on natural selection (paper 7; Gould and Lewontin).
2) Learn how to use population genetics summary statistics to detect the action of natural selection and how to estimate them on genomic data.
A complete guide to this lecture can be found here: https://github.com/Jcbnunez/biol4585j-yey2sn/tree/main/Class_Materials/8.Selection
Jan 13
This lesson has two goals.
1) Complete a paper discussion on natural selection in the intertidal (paper 8; Nunez et al).
2) Open time to work on the genomics challenge.
Jan 14
Final Presentations
Where to find each lesson's readings (click on the https link):
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Shendure, J., Balasubramanian, S., Church, G. et al. DNA sequencing at 40: past, present and future. Nature 550, 345–353 (2017). Where to access: https://doi.org/10.1038/nature24286
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Casey W. Dunn, Sally P. Leys, Steven H.D. Haddock, The hidden biology of sponges and ctenophores, Open Access Published: March 31, 2015. Where to access: https://doi.org/10.1016/j.tree.2015.03.003
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Rambaut, A., Posada, D., Crandall, K. et al. The causes and consequences of HIV evolution. Nat Rev Genet 5, 52–61 (2004). Where to access:https://doi.org/10.1038/nrg1246
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Jacqueline A. Robinson, Jannikke Räikkönen, Leah M. Vucetich, John A. Vucetich, Rolf O. Peterson, Kirk E. Lohmueller, Robert K. Wayne. Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Science Advances, 29 May 2019, Vol 5, Issue 5. Where to access: https://doi.org/10.1126/sciadv.aau0757
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Jun Z. Li, Devin M. Absher, Hua Tang, Audrey M. Southwick, Amanda M. Casto, Sohini Ramachandran, Howard M. Cann, Gregory S. Barsh, Marcus Feldman, Luigi L. Cavalli-Sforza, Richard M. Myers. Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation, Science • 22 Feb 2008, Vol 319, Issue 5866, pp. 1100-1104, https://doi.org/10.1126/science.1153717
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Novembre, J., Johnson, T., Bryc, K. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008). Where to access: https://doi.org/10.1038/nature07331
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S. J. Gould and R. C. Lewontin. The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Published:21 September 1979. Where to access: https://doi.org/10.1098/rspb.1979.0086
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Joaquin C. B. Nunez, Patrick A. Flight, Kimberly B. Neil, Stephen Rong, Leif A. Eriksson, David A. Ferranti, Magnus Alm Rosenblad, Anders Blomberg, David M. Rand. Footprints of natural selection at the mannose-6-phosphate isomerase locus in barnacles. Proceedings of the National Academy of Sciences Mar 2020, 117 (10) 5376-5385; Where to access: https://doi.org/10.1073/pnas.1918232117
Assignments
Class participation (500 pts)
Students will be given points for actively participating in class (up to 50 pts per class x 10 sessions). Participation will be assessed based on a predetermined rubric.
Class discussion leader (700 pts)
Students will obtain points based on their performance when leading the week’s discussion. This will be assessed based on a predetermined rubric.
Reading summaries (700 pts)
Every day, students will turn-in a short (1/2 page long) reading summary of the daily assigned reading. This will be assessed based on a predetermined rubric. 100 pts per paper (x 7 papers).
Final presentation (800 pts)
For the final project, students will analyze a mystery dataset and will use their new skills to discover what processes are at play in the dataset. This dataset will be given on the Friday of week one (giving a whole week for students to play with the data). Prior to final submission, the students will have the opportunity to obtain feedback from the instructor via an online platform and class dialogue. This assignment will be assessed based on a predetermined rubric.
Coding homework (200 pts)
I will give a small coding quiz at the end of each practicum to test newly acquired coding skills. Each mini-quiz is worth 25 pts. These are low-stakes quizzes meant to help solidify new concepts. These quizzes are intended for students who are brand new to coding.
