
Syllabus, Readings and Lecture Notes
 Background
 Topics: Why computational biology, biological information, challenges in computational
biology.
 Required reading
 Recommended reading
 lecture Topics
 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
 Recommended reading
 Lecture notes
 Biological sequences and computational problems with sequence similarity. pdf ppt notes. 9/4
 Pairwise Sequence Alignment
pdf pptx notes. 9/9
 Scores and substitution matrices
pdf pptx 9/11
 Practical algorithms in sequence alignment and search
pdf pptx 9/16
 Multiple Sequence Alignment/Practical algorithms
pdf ppt. 9/18
 Practical algorithms for Multiple Sequence Alignment pdf ppt. 9/23
 Phylogenetic Trees
 Topics: Probability distributions, data likelihood, distance, parsimony and probabilistic methods of phylogenetic tree construction, models of sequence evolution
 Required reading
 Chapter 7, Sections 7.17.4 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 and distance based methods for Phylogenetic tree reconstructions . pdf ppt 9/23 (updated 9/25)
 Parsimony methods for phylogenetic tree reconstruction.pdf ppt 9/30
 Probabilistic methods for phylogenetic tree reconstruction. pdf ppt 10/1
 Probabilistic methods for phylogenetic tree reconstruction Part 2. pdf ppt 10/7
 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, 3.5 in Durbin et al.
 Sections 3.2, 3.3 in Durbin et al.
 Recommended reading
 Lecture notes
 Introduction to Markov models. pdf ppt 10/7 (updated 10/9)
 Midterm review. pdf ppt 10/9
 Hidden Markov models. pdf ppt 10/16, 10/21
 Parmeter learning in HMMs. pdf pptx 10/21,10/23
 HMMs in practice. pdf pptx 10/23
 Clustering approaches to ``omic'' datasets.
 Topics: highthroughput technologies, clustering algorithms, evaluation of clusters
 Required reading
 Recommended reading
 Lecture notes
 Flat clustering approaches for highthroughput datasets. pdf ppt 10/28 (updated 10/30)
 Gaussian mixture modelbased clustering (10/30,11/4). matlab code data
 Hierarchical Clustering approaches and cluster evaluation. pdf ppt 11/4,11/6
 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
 N. Friedman, I. Nachman and D. Pe'er. Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm In Proceedings of the 15th Annual
Conference on Uncertainty in Artificial Intelligence 1999.
 E. Segal, D. Pe'er, A. Regev, D. Koller, N. Friedman Module networks.
 V. A HuynhThu, A. Irrthum, L. Wehenkel, P. Geurts. Inferring Regulatory Networks from Expression Data Using TreeBased Methods. Plos One 5(9), 2010.
 R. D. Smet and K. Marchal. Advantages and limitations of current network inference methods. Nature Reviews Microbiology. 8:717729, 201
 Recommended reading
 Lecture notes
 Introduction to networks. pdf pptx 11/6
 Network structure learning: Bayesian networks.
pdf pptx 11/11, 11/13
 Module networks.
pdf pptx 11/13,11/18,11/20 (updated)
 Dependency networks.
pdf pptx 11/25 (updated)
 Network applications/Networks in practice.
pdf pptx 12/2
 Network applications/Networks in practice Part 2.
pdf pptx 12/9
 Class review
