
Syllabus, Readings and Lecture Notes
 Background
 Topics: Why computational biology, biological information, challenges in computational
biology.
 Required reading
 Recommended reading
 Lecture notes
 Course Overview (PDF, PPTX) (9/7)
 Molecular Biology 101 (PDF, PPTX) (9/12)
 Sequence Assembly
 Topics: Fragment assembly, Sequencing by hybridization, Overlaplayoutconsensus
 Required reading:
 Recommended reading:
 Lecture notes
 Sequence Alignment
 Topics: Introduction to biological sequences, DNA sequence, dynamic programming methods for global and local alignment,
gap penalty functions, heuristics in alignment, BLAST, pairwise sequence alignment, multiple sequence alignment
 Required reading
 Sections 2.12.5, 2.7, 2.8 (1.3 for brief background in probability)
 Sections 6.16.4
 Recommended reading
 Lecture notes
 Pairwise Sequence Alignment (PDF, PPTX) (9/26, 9/28) (updated 10/4)
 Intro to Probability for Discrete Variables (PDF, PPTX) (10/3)
 The statistics of pairwise alignment (PDF, PPTX) (10/3, 10/5) (updated 10/14)
 Heuristic methods for sequence database searching
(PDF, PPTX) (10/5)
 Multiple sequence alignment
(PDF, PPTX) (10/10), (PDF, PPTX) (10/12)
 Phylogenetic Trees
 Topics: Distance, parsimony, and probabilistic methods of phylogenetic tree construction, models of sequence evolution
 Required reading
 Chapter 7, Sections 7.17.5 in Durbin et al.
 Chapter 8, Sections 8.18.3 in Durbin et al.
 Recommended reading
 J. Alfoldi, K. Toh. Comparative genomics as a tool to understand evolution and disease.
 Lecture notes
 Introduction to Phylogenetic trees (PDF, PPTX) (10/12)
 Distancebased methods for phylogenetic tree reconstruction (PDF, PPTX) (10/17, 10/19)
 Parsimony methods for phylogenetic tree reconstruction. (PDF, PPTX) (10/19)
 Probabilistic methods for phylogenetic tree reconstruction. PDF, PPTX) (10/24, 10/26)
 Annotating genomes
 Topics: Markov chains, highorder Markov models, Forward/Backward/Viterbi algorithms, applications to genome segmentation and annotation.
 Required reading
 Sections 3.0, 3.1
 Sections 3.2, 3.3 in Durbin et al.
 Recommended reading
 Lecture notes
 Markov models. (PDF, PPTX) (11/2, 11/7)
 Hidden Markov models. (PDF, PPTX) (11/7, 11/9)
 Hidden Markov model parameter estimation. (PDF, PPTX) (11/9, 11/14)
 HMM applications. (PDF, PPTX) (11/14, 11/16)
 Clustering approaches to ``omic'' datasets.
 Topics: highthroughput technologies, clustering algorithms, evaluation of clusters
 Required reading
 Recommended reading
 Lecture notes
 Highthroughput omic datasets and clustering. (PDF, PPTX) (11/16)
 Flat clustering. (PDF, PPTX) (11/22)
 Hierarchical clustering. (PDF, PPTX) (11/28)
 Clustering evaluation. (PDF, PPTX) (11/28)
 Modeling and analysis of networks
 Topics: Biological networks, computational problems in network biology, Bayesian networks, module networks, parameter and structure learning, regressionbased network inference, network applications
 Required reading
 Recommended reading
 Lecture notes
 Introduction to biological molecular networks. (PDF, PPTX) (11/28, 11/30)
 Network structure learning: Bayesian networks. (PDF, PPTX) (11/30,12/5,12/7)
 Module networks. (PDF, PPTX) (12/7,12/12)
 Dependency networks. (PDF, PPTX) (12/12)
 Final thoughts
 Final thoughts. (PDF, PPTX) (12/12)
