Course Materials for Bioinformatics I
Fall 2009

Lecture materials:

1. Introduction to Bioinformatics I
   • What is Bioinformatics I?
   • Basic Definitions
2. Basic Internet resources for bioinformatics
   • Homework assignment due Sept. 1
   • Homework assignment due Sept. 8
   • Homework assignment due Sept. 12
3. Materials for presentation by Dr. Ross Hardison
   • The presentation
   • Paper on ENCODE
   • Cheng et al. 2008. Transcriptional enhancement by GATA1-occupied DNA segments is strongly associated with evolutionary constraint on the binding site motif. Genome Res. 18:1896-1905.
4. Materials for presentation by Dr. Anton Nekrutenko
   • To get an idea of how machine-learning techniques are applied to bionformatics, search for the string "SVM" (the abbreviation for Support Vector Machine) in the 2007, 2008, and 2009 NAR Web Server Issues (see the links, above); 10 papers in 2007, 7 papers in 2008 and 8 in 2009.
   • Improved base calling for the Illumina Genome Analyzer using machine learning strategies.
   • The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.
   • KIRMES: Kernal-based identification of regulatory modules in euchromoatic sequences
   • SVMs in Galaxy
5. Basics of bioinformatic algorithms
   • Notes for Chaper 4 of the Jones/Pevzer book
   • Notes for Chaper 5 of the Jones/Pevzer book
   • Solution to the Breakpoint Challenge Problem.
6. In Jones/Pevzner read about:
   • NP-complete problems, pages 49-51
   • graphs, and the Eulerian and Hamiltonian Cycle Problems, pages 248-258
   • the Shortest Superstring Problem, pages 264-265
   • Sequencing by Hybridization, and two solutions, pages 268-275
   • Website for Chapter 8 (Graphs and DNA Sequencing)
7. Sequence alignment
   • Website for introduction to dynamic programming.
   • Notes about the dynamic-programming algorithm for aligning two sequences
   • Notes about alignments scores and Psi Blast
   • Blast outline and paper
   • Multiple sequence alignment survey paper; recent paper about Clustal; website -> "Multiple Alignment (Ch 6)"
8. Weight matrices and introduction to probabilitic models
   • Notes on weight matrices.
   • Notes on log-odds methods
   • Notes on probabilistic sequence models
   • Survey by Gary Stormo
   • Amino-acid substitution scores
9. Hidden Markov Models
   • Notes
   • Website for Chapter 11 (HMMs)
   • GenScan paper (see especially Figure 3)
   • More notes on probabilistic models.
10. Study guide for the exam.

You might find it amusing to read the story of how I got involved in bioinformatics, or "10 Steps to Success in Bioinformatics".