Syllabus for Math 5233, Mathematical Foundations of Bioinformatics

This page will be updated throughout the semester. Comments on improvements are appreciated.

Instructor: Marshall Hampton

Office: 172 SCC

Email: mhampton at d.umn.edu (preferred contact method)

Telephone: 726-6329

Office hours: M, Tu: 3-5, W: 3-4, and by appointment.

Class homepage: http://www.d.umn.edu/~mhampton/m5233s7.html

Lecture Times: 2 - 2:50 pm, M, W, F. On Mondays we will be in MonH 209, and on Wednesdays and Fridays we will be in MonH 208.

Prerequisites: Any two of the following: Biol 5233, Math 3355, CS 1511, Stat 3611, or equivalents. Please come see me if you have any questions about the preparation required for this course.

Textbook: Computational Genome Analysis, by Richard C. Deonier, Simon Tavare, and Michael S. Waterman. ISBN: 0387987851.

Topics: The official course description is "Mathematical, algorithmic, and computational foundations of common tools used in genomics and proteomics. Topics include: sequence alignment algorithms and implementations (Needleman-Wunsch, Smith-Waterman, BLAST, Clustal), scoring matrices (PAM, BLOSUM), statistics of DNA sequences (SNPs, CpG islands, isochores, satellites), and phylogenetic tree methods (UPGMA, parsimony, maximum likelihood). Other topics will be covered as time permits: RNA and protein structure prediction, microarray analysis, post-translational modification prediction, gene regulatory dynamics, and whole-genome sequencing techniques." One thing not mentioned there that I would like to at least briefly cover is hidden Markov models (hmms). We will be using the programming language Python as our primary computational tool (see below). All of the software we will be using is free.

A common theme for our projects and examples will be malaria and the Plasmodium genus organisms that cause it (particularly P. falciparum).

Optional complementary texts: While our text is the overall best book I could find for the course, some aspects of the subject are covered better or differently elsewhere. I will probably use these to supplement examples in lectures and for assignments:

• Fundamental Concepts of Bioinformatics, by Dan E. Krane, Michael L. Raymer. This does a nice job with phylogenetic trees. The presentation is very clear for what it does cover.
• Bioinformatics and Functional Genomics, by Jonathan Pevsner. This has some fantastic biological examples. If it had a little more mathematics I would use it for our textbook.
• Discovering Genomics, Proteomics and Bioinformatics (2nd Edition), by A. Malcolm Campbell, Laurie J. Heyer. Same comments as the last one: this has a ton of great biology in it, but not quite enough math to use as a primary text.
• Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, by Andreas D. Baxevanis and B. F. Francis Ouellette. There are a lot of "hands-on" bioinformatics books, most of which are very superficial. This is by far the best I have found of that genre.
• Inferring Phylogenies, by Joseph Felsenstein. This is the book to read on this subject, along with Phylogenetics by Semple and Steel.
• Bioinformatics and Molecular Evolution, by Paul Higgs and Teresa Attwood. I am leaning towards using this book as the main text the next time I teach this course.

Grading: You will have the opportunity to be evaluated in a variety of ways: homework, class participation and presentations, and exams. The homework and class presentations will be the primary factors for determining grades.

Exams: There will be a team midterm Wednesday March 7th, and an individual final exam, 4-6 pm, Tuesday, May 8th.

Projects: Details TBA. There will be several projects and presentations for small groups.

Note to site visitors: If you are not on the University of Minnesota network, some of the links below to articles may not work since they require a subscription. If you are U of Mn affliated, you should be able to access everything here with a VPN connection.

Homework and labs:

• Read at least two of the following essays on bioinformatic curricula by Friday Jan. 19: paper 0 paper 1 paper 2 paper 3
• Read Chapter 1 as soon as possible. If you are confused by anything, make notes and we will discuss it in class.
• In preparation for Monday's class (Jan 22), read the Wikipedia entry on malaria.
• Read sections 2.1 and 2.2 in the text by Wednesday, Jan 24.
• Based on work done on Monday the 22nd, each group should be prepared to briefly summarize their findings on Friday the 26th.
• Read sections 6.1-6.2 and the emailed article by S.R. Eddy (What is dynamic programming? Nat. Biotechnol. 2004 Jul;22(7):909-10) by Monday, Jan 29.
• Monday's lab (January 29th)
• Read sections 6.3-6.6 in the text by Friday February 2nd.
• Monday's lab (February 5th)
• Read sections 6.7-6.8, and chapter 7 by Friday, February 9th. Section 7.3 is somewhat dense, so try to read it but don't hurt yourself.
• Read S.R. Eddy's primer on BLOSUM matrices and Stephen Altschul's Blast tutorial at NCBI by Wednesday, February 14th.
• Monday's lab (February 12th, due 2/21)
• Try to read Chapters 2.3 - 2.5 by February 21st.
• Read this paper and this paper by Friday February 23rd.
• Modify the python function here so that it computes a Smith-Waterman local alignment instead of doing the global Needleman-Wunsch algorithm. There are three changes that have to be made: (1) the borders of the array (the dynamic programming table) should be initialized to 0. (2) The lowest possible score should be 0. (3) The backtracing should start from the maximal score position and stop when it hits a score of 0. Your program should be emailed to me by Friday, February 23rd.
• Read 14.1 - 14.2 (CpG islands, transposons) and 9.4 (entropy and information) from the text by Wednesday, February 28th.
• Monday's lab (February 19th, due 2/28)
• Read the Plasmodium falciparum Plasmodium genome paper by March 2nd. I expect you to have questions after reading this - there is a lot there. I recommend bringing some notes on this to class.
• Read a review on apicoplast function, at least up through the 'carbon and energy' section. The rest is heavy on plant biochemistry. Try to read it by March 2nd.
• Read this overview of current multiple sequence alignments algorithms, this description of ClustalW, and this description of T-coffee by March 5th. Due to snow interruptions, we will try to discuss these on Friday March 9th.
• Monday's lab (February 26th, due 3/7)
• The Midterm (due 3/9)
• Regular expressions worksheet (using Prosite format).
• Mid-semester project topics, to be presented on Friday March 23rd and Wednesday March 28th. You should discuss how bioinformatics algorithms are applied in the given topic, and give an overview of the biological background and implications. One or two suggested references are given to get you started, but you should explore other articles and sources of information (for example, try to reproduce a given Blast result; also the bibliographies of the given papers should lead you to other relevant material).
  • Codon bias and usage in Apis mellifera (honeybee). Group: Terry, Alayna, Eric. Reference.
  • Plasmodium organelles and transport signals. Group: Amanda, Brad, Marissa, Ajit. Reference. A fascinating, but more mathematically intensive reference.
  • Mobile elements in the Plasmodium genome. Group: Noland, Luke, Doug. Reference.
  • Noncoding RNAs in Plasmodium. Group: Bethany, Shane, Maria. Reference. That link may not work: the PubMed ID for the article is 16183147, its by Upadhyay et al., and the title is "A screen for conserved sequences with biased base composition identifies noncoding RNAs in the A-T rich genome of Plasmodium falciparum".
• Lab for Monday, March 19th will be on Prosite-type protein patterns/regular expressions and using the Python re (regular expression) module. Due March 28th.
• Read S.R. Eddy's primer on Hidden Markov Models, and the text chapters 2.6 - 2.9 by Wednesday, March 28th.
• Extra credit will be awarded for finding typos in the T-Coffee paper previously assigned. There are at least two significant ones. Friday March 30 will be the last day that found typos will be accepted.
• Lab for Monday, March 26th will be HMMs and the Viterbi algorithm. Due April 4th.
• Read sections 10.1 - 10.4 and 12.1 in the text by Wednesday, April 4th. It may be helpful to read this material sooner than that if possible.
• Optional but highly recommended readings (pubmed entries; click on the journal banner on the right to get to the actual article):
  • an article on the cox2 split in Plasmodium and other alveolates compared to some green algae that also have a split cox2 gene; this is a nice transition to the topic of phylogenetics. The advantage of the PubMed link is you can see the related articles, such as the 2002 Funes et al paper that argues the opposite point of view.
  • a very recent article from Science on the transfer of mitochondrial genes to nucleus.
• Lab for Monday, April 2nd will be on UPGMA and phylogenetic trees. Due April 11th.
• Read the famous and well-written article by Cavalli-Sforza and Edwards (1967) by April 11th.
• Read 12.2 - 12.6 in the text by Monday, April 16th.
• Lab for Monday, April 9th: a great salty controversy. 'Due' in class April 18th and 20th.
• Read this article on phylgenomics and the tree of life project (pubmed id 15861208) by April 23rd.
• Lab for Monday, April 16th is on bootstrapping phylogenetic trees; due April 25th.


Pending:
• Lab for Monday, April 23rd is on the Coelomata-Ecdysozoa controversy, long-branch attraction and phylogenetic methods. Due May 4th.
• Lab-related reading on long-branch attraction and the Ecdysozoa/Coelomata controversy: 'Fatal attraction' paper(PubMed ID: 15854895), Nielsen article (apparently not in PubMed - if the link doesn't work the article is 'Proposing a solution to the Articulata-Ecdysozoa controversy', by Nielsen, Claus. Zoologica Scripta, Sep 2003, Vol. 32 Issue 5, p475), and Philip et al (PubMed ID: 15703245).
• Optional Ecdysozoa/Coelomata related reading: Giribet's review, pubmed id 12220976, and a 18S specific article on long branch attraction in metazoan phylogeny, pubmed id 15336677.
• Practice final

Web Resources:

Introductory biology material:

MIT OpenCourseWare Intro Biology Videos Very nice video lectures. Numbers 9-14 and 31-32 are probably most relevant to this course. 24-27 are good if you would like a better understanding of recombinant DNA technology.

A biology primer

Wikipedia entries for molecular biology and biology. There are a lot of links to other related topics on these pages. In general, I find wikipedia to be a pretty good place to get started or to look up specific terms.

Human Genome Project primer. This has some good basic background on DNA and the human genome project.

Intro to Bio a 16-slide pdf for the Stanford course listed below.

Molecular Genetics and Sequencing primer Nice overview with a focus on genome sequencing.

NCBI education site. This has many links to tutorials and primers on a variety of subjects.

Parables of the differences between geneticists and biochemists: geneticist version and biochemist version. Of course, they both need to learn more math.

NCBI Entrez Often the right place to start

Ensembl Genome browsers and other tools

EMBL-EBI European Bioinformatics Institute main page with links to many tools.

AmiGo Gene Ontology search

Bioinformatics: Building Bridges a symposium at the twin cities campus April 12-13.

Nature

Science

Nature reviews: genetics

Nature reviews: molecular biology

KEGG Pathway database

UCSC Genome Browser

ExPASy

Sequence-Evolution-Function a free online bioinformatics text on NCBI. There are many other books there as well!

Jobs abound for the interdisciplinarians.

Phylogeny software . A huge list from J. Felsenstein, a leader in the field and author of Phylip and the text 'Inferring Phylogenies'.

ExPASy Uniprot

ExPASy Prosite

NCBI Structure Database

NCBI Protein Database

NCBI Homologene

EMBL-EBI Interpro

Course notes from a sequence analysis course at McMaster University. A lot of useful information.

Sequence alignment applets. These do a nice job illustrating some of the basic alignment algorithms

BLAST statistics tutorial

NCBI BLAST page

MIT Computational Functional Genomics OpenCourseWare site

Stanford Representations and Algorithms for Computational Molecular Biology course page

University of Colorado Bioinformatics course taught in python.

PlasmoDB a database specializing in the genus Plasmodium.

Plasmocyc A Stanford University database on malarial Plasmodium.

Gene expression database for Plasmodium

Plasmodium review This is a good one.

Plasmodium genome paper Genome release papers usually have a wealth of information.

Official Python Site You can download python from links here, as well as find good documentation and other resources. If possible, try to use version 2.5.

wxPython download page. The docs-demos download has a pre-built PyCrust shell; the main package also includes that but it may be less obvious. I recommend installing both (after python itself).

Numeric package This is needed for a very small number of biopython pieces. The biopython developers are working on updating their code so that it works with the more supported "numpy" project, which replaced Numeric. If you install Python 2.5 on a Windows computer, install numpy instead of Numeric since there is a nice installer for that.

Biopython download page. Version 1.42 is beginning to show its age; to fix some problems you can download individual packages from the CVS sources. The download page has good installation instructions.

mxTextTools Required for biopython installation.

Report Lab Optional for biopython installation. I never actually use this, but I always install it anyway. It supplies some PDF output routines.

SAGE This is a python-based open-source math project that now includes biopython as an optional package.

Python Tutorial A short tutorial by the creator of python, Guido van Rossum. In addition, the Python Library Reference has more complete documentation.

Python for beginners. A collection of tutorials for beginners.

Python video tutorials Quality varies but some of these are very helpful.

If you want a hard copy books, these three are among the best I know: Core Python Programming, Programming Python, and Learning Python. Personally I liked the Core Python Programming book the best, but the best for you will depend on your taste and background.

Informatics in Biology Pasteur Institute Course

Python for Bioinformatics at the Pasteur Institute

Biopython Documentation The "Tutorial and Cookbook" is extremely helpful, although sometimes the code examples get a little out of date as biopython evolves.

Dive Into Python Site The text is pretty advanced, but very interesting. If you feel you have reached an intermediate level of programming with Python, this could help tip you into the expert category.

Text Processing in Python Same comments as above: this is not for beginners, but there is a lot of great stuff that is relevant to bioinformatics applications.

Andrew Dalke Python and Bioinformatics Resources Lots of good stuff here

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Disabilities: An individual who has a disability, either permanent or temporary, which might affect his/her ability to perform in this class should contact the instructor as soon as possible so that he can adapt methods, materials and/or tests as needed to provide for equitable participation.