CS284A: Representations & Algorithms for Molecular Biology

Syllabus, Readings and Lectures Notes

• Introduction to Computational biology, Molecular Biology and Genomics. Sept 24, 2009
• reading
• L. Hunter. Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, 1993.
• Human Genome Project: Frequently Asked Questions
• A Quick Introduction to Elements of Biology, a primer by Alvis Brazma et al.
• lecture notes
• Lecture 1: Introduction to Molecular Biology and Genomics
• resources
• Genbank
• USCS Genome Browser
• Ensembl Genome Browser
• SWISS-PROT
• Protein Data Bank
• PubMed
• List of Molecular Biology Databases(from Nucleic Acids Research)
• S-Star Bioinformatics Online Course Schedule, a collection of video primers.
• FAQ on bioinformatics.
• Online courses related to bioinformatics and computational biology
• Gene Discovery
• Open reading frame, geometric distribution, hypothesis testing, p-value
• Pattern discovery, regulatory motifs
• Lecture3.pdf
• Reading: How does DNA sequence motif discovery work?, Patrik D'haeseleer, Nature Biotechnology, 24, 959-961 (2006)
• 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
• Sequence alignment, dynamic programming, Smith-Waterman algorithm
• Local and global sequence alignments
• A brief introduction to BLAST
• NCBI BLAST Server
• Markov chains and hidden Markov models (HMM)
• Lecture notes: HMM
• 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