Biostatistics & Medical Informatics 576
Computer Sciences 576
Introduction to Bioinformatics (Fall 2014)

General Course Information
Course overview
Syllabus, Readings, Lecture Notes
Schedule
Homework Assignments
Grading criteria
DNA
Image source: Costanzo et al., Science 2010

Syllabus, Readings and Lecture Notes

  • Background

  • 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 7.1-7.4 in Durbin et al.
      • Chapter 8, Sections 8.1-8.3 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, high-order 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.

  • Modeling and analysis of networks
  • Class review