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Bioinformatics and phylogenetic inference, Autumn 2016

 

Teacher:  Sirkka-Liisa Varvio 

Scope: 5-10 cr

Type: intermediate / advanced studies

Teaching: lectures and working in computer class

Teaching schedule: intensive teaching period in August, computer class C128, 12-16 in Thu 25.8, Fri 28.8, Mon 29.8, Wed 31.8, and additional times to be negotiated.
Assignments during the intensive period => 5 cr option of the course. Additional assignments/project after this period => another 5 cr.

Topics: The goal is to provide understanding of life through statistical and computational analyses of molecular information. Models of molecular evolution and phylogeny inference, distance matrix methods, maximum likelihood, bayesian and network inference. Past, or traditional, practice on phylogenetics has been to describe relationships among species within the disciplines systematics and taxonomy. Today the scope is much wider. Phylogenetics is used as a necessary tool in all kind of bioscience research questions: relationships among genes, gene families, alleles, cell lines, various genomic components, histories of populations, evolutionary and epidemiological dynamics of bacteria and viruses, etc.  Phylomics is the natural extension of genomics and metagenomics, phylotyping the current way of understanding ecosystems within and outside organisms (environments) and evolutionary thinking is a complementary framework to medical genetics. Statistical and computational approaches, based on predictions derived from population genetics theory, aim at dissecting neutral (stochastic) and selected (darwinian natural selection) components of genetic diversity.
Course assignments will be from human, animal, virus, bacteria data.

Prerequisites: At least intermediate level of probability and statistical inference courses. No familiarity with bio-studies expected as prerequisites.

Moodle-area

This is the area for exercise and assignment submissions, for request of help and all kind of questions and discussion.

Exercises and assignments

  • Exercise set 1 (mainly pen-and-paper -type exercises) and phylogeny Assignment 1 (computer work) are based on lecture and tutorial which will be given during Thu 25.8 and Fri 26.8 sessions.
    • All students should perform these and submit solutions to Moodle on Sunday 11.9 at the latest.
  • In addition to Exercise set 1 and Assignment 1, there are five alternative assignments (2-6 in the table below). You are supposed to select three out of them in order to get the 5 credit option. There is also the possibility to write three essays instead of these three computer assignments (or one computer assignment, two essays, two computer assignments, one essay).
    • Time schedule: You can work according your own time schedule during the I-teaching period (i.e. till 23.10). This means that you have a possibility to complete the course (5 cr) in two about weeks ---- in about two months. 
  • You can choose: grade 1-5 or pass/fail. You can also choose to work as a group of 2-4 students. A group will get pass/fail, not a grade. In case you choose to work alone, you cannot replicate other students results, i.e. you must submit only your own results in your submissions. This does not mean that you cannot discuss with other students and ask for help from other students, of course you can! The results which you submit must represent your own performance. Essays cannot be group work, but it is possible to work as group for other assignments, and write the essays alone.
 Exercise set 1 
Assignment 1Initial_dataset_for_assignment_1.txtInitial_dataset_for_assignment_1_aligned.txt 
Assignment 2database: http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html
Assignment 3data_2.txt
Assignment 4data_3.txtdata_3_aligned.txt, data_3_aligned_IUPACedited.txt, data_4.txt
Assignment 5database: http://www.mtdb.igp.uu.se/
Assignment 6

Your own choice. Think about an idea and send it to Siru by email (sirkka-liisa.varvio at helsinki.fi) in order to get advise. Especially from viruses (how about the zika-virus...) and bacteria it is easy to find data which can serve as course assignment data. Also a human disease gene and its evolution might be a good idea.

 


 

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