CS174 Bioinformatics

Syllabus, Readings and Lectures Notes

• General course information. Brief introduction to bioinformatics/computational biology.
• Lecture1.pdf
• Python Tutorial
• python_tutorial.pdf
• Gene discovery, Open reading frame (ORF), ORF length distribution
• Lecture2.pdf
• Pattern discovery, regulatory motifs
• Lecture3.pdf
• Reading: How does DNA sequence motif discovery work?, Patrik D'haeseleer, Nature Biotechnology, 24, 959-961 (2006) (not required)
• Positional weight matrix, probabilistic model of motif discovery, Expectation-Maximization algorithm
• motif_mixture.pdf (note that: this note is intended for students who wish to understand detailed theoretical derivations of the EM-algorithm, only the first eight pages and materials covered in class are required)
• Reading: Fitting a Mixture Model by Expectation Maximization to Discover Motifs in Bipolymers TL Bailey and C Elkan, UCSD (not required)
• Exact string matching
• Lecture5.ppt
• Sequence alignment, dynamic programming, Smith-Waterman algorithm
• Local and global sequence alignments
• A brief introduction to BLAST
• NCBI BLAST Server
• Genome assembly: Hamitonian Path, Eulerian Path, deBruijin Graph
• genome_assembly.pdf
• Genome sequence assembly primer
• Markov chains and hidden Markov models (HMM)
• Lecture notes: HMM (only the first 14 slides are required)
• An Introduction to Hidden Markov Models for Biological Sequences A Krogh, in S. Salzberg et al., eds., Computational Methods in Molecular Biology, 1998. Elsevier.
• HMM demo matlab code
• Other topics in bioinformatics: comparative genomics, protein structure prediction, and machine learning in bioinformatics
• CS174_other_topics.pdf
• Machine learning in bioinformatics