1. Introduction - Algorithms in Bioinformatics

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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
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1. Introduction - Algorithms in Bioinformatics

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