1. Introduction - Algorithms in Bioinformatics

Bioinformatics I, WS’09-10, D. Huson, November 26, 2009 1
1 General Introduction
Bioinformatics I
Module “Bioinformatics I” M.Sc. Bioinformatik
Prof. Daniel Huson, Dr. Johannes Fischer,
and Dr. Stefan Henz
WS 2009/10
Office hours: see personal webpages.
1.1 Time and place
Lectures:
Mondays 10ct-12h A301, Sand 1
Wednesdays 10ct-12h A301, Sand 1
Problem sessions:
Day Time Where
Email:
Daniel Huson huson at informatik.uni-tuebingen.de
Stefan Henz stefan at henz@tuebingen.mpg.de
Johannes Fischer fischer at informatik.uni-tuebingen.de
Website: www-ab.informatik.uni-tuebingen.de/teaching/ws09/bioinformatics-i
1.2 How to get credit for this course
To pass this course you must:
• Always participate in the weekly problem sessions and present your results regularly.
• Obtain at least 60 % of all attainable points on the assignment sheets.
• Pass the mid-term exam.
• Pass the final exam.
You may work on and hand-in assignments and projects in groups of up to two people.
Dates (tentative): Mid-term exam on 14.11.2009, final exam on 17.2.2010
Grade calculation: 50% mid-term exam, 50% final exam.

2 Bioinformatics I, WS’09-10, D. Huson, November 26, 2009
1.3 Course notes and assignments
When possible, course notes (“the script”) will be handed out at the beginning of each lecture. The
lecture notes will also be made available on the course website.
Assignment sheets will usually be handed out and published on the course web-site on Mondays.
Assignments are due a week later. Solutions should be sent to the tutor by email or handed in before
the beginning of the lecture.
1.4 Contents of the lecture
Bioinformatics I: Sequences and machine learning
Bioinformatics II: Structures and systems biology
1.4.1 Overview Bioinformatics I
• Builds on “Grundlagen der Bioinformatik”
• Mandatory lecture for Msc. bioinformatics students
• Focuses on algorithms for the analysis of biological primary sequences
• Algorithms: dynamic programming, heuristics, machine learning
1.4.2 Textbooks
Introduction to Computational Biology by Michael Waterman
Introduction to Computational Biology by Setubal / Maidanis
Biological sequence analysis by Durbin, Eddy, Krogh and Mitchison
Bioinformatics - The Machine Learning Approach by Pierre Baldi and Soren Brunak
1.5 Summary of Grundlagen-der-Bioinformatik lecture
• Pairwise alignments (Scoring matrices, NW, SW)
• Blast
• Multiple alignments (SP score, star, progressive)
• Phylogeny (UPGMA, NJ, Maximum Parsimony)
• HMMS (CpG, Viterbi, supervised training)
• gene finding (ORF prediction in procaryotes, GenScan)
• RNA structures (Nussinov, Zuker)
• Protein secondary structures (classification, Chou-Fasman, SSP)
• Protein tertiary structures (classification, threading, de novo, comparison)

Bioinformatics I, WS’09-10, D. Huson, November 26, 2009 3
• Bionformatics Databases
• Microarray (Technology, Normalization, Clustering, Statistics)
1.6 Overview Bioinformatics I
1. Pairwise alignment (quick reminder, affine gaps, k-band, linear space)
2. Multiple alignment (T-Coffee, Muscle)
3. BLAST and psi-BLAST, BLAT
4. Phylogeny (ML and Bayesian, network methods)
5. Suffix trees (Generation, searches, repeats)
6. Motif finding
7. Hidden Markov Models (Training, Viterbi Training, Baum-Welch)
8. Gene finding (GenScan, Twinscan)
9. Support Vector Machines (subcellular location)
10. Physical mapping (3 protocols, 3 algorithms)
11. Sequencing and assembly
12. Population genetics