Integrated Biological Sciences Summer Research Program:
Computational Biology & Biostatistics Workshop
(Summer 2008)

Overview

Course Description

The purpose of this course is to serve as a brief introduction to computational and statistical methods in biology. We will discuss a breadth of algorithms for analyzing and organizing biological data. This includes biological sequence alignment and analysis, gene expression and genetic marker analysis using both "unsupervised" and "supervised" machine learning techniques, protein structure prediction, and even data mining from the biomedical literature.

The workshop is part of the ISB Summer Research Program, in the Computational Biology & Biostatistics track.

Meeting Schedule

May 29 & 30*, June 2, 4*, & 5, 10:00-11:30am
Room 1210 (*1217c) Medical Sciences Center [map].

Class Personnel

• Burr Settles - Lectures 1-4
  Office: 6775 MSC
  Email: bsettles@cs
• Ameet Soni - Lecture 5
  Office: 6749 MSC
  Email: soni@cs

Syllabus, Lecture Notes, and Readings

• Introduction to Bioinformatics (5/29)
  • Overview of DNA, RNA, proteins, the Central Dogma, and the types of genomics data available.
  • Reading: L. Hunter. Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
  • Lecture 1 - Introduction to Bioinformatics
• Sequence Alignment (5/30)
  • Dynamic programming for global and local sequence alignment, linear and affine gap penalty functions, alignment statistics, and substitution matrices.
  • Recommended reading: Chapter 2, Durbin et al. (see resources below).
  • Lecture 2 - Sequence Alignment
• Probabilistic Sequence Models (6/2)
• Basic probability theory, Markov chain models, HMMs, forward & Viterbi algorithms, applications to biological problems and biomedical text mining.
• Recommended readings: Chapters 2.1 & 9, Manning & Schutze; Chapter 3, Durbin et al. (see resources below).
• Lecture 3 - Probabilistic Sequence Models
• Gene Expression Analysis (6/4)
• High-throughput technologies, differential expression, clustering algorithms, classification algorithms, genome-wide association studies.
• Reading: M. Molla, M. Waddell, D. Page and J. Shavlik. Using Machine Learning to Design and Interpret Gene-Expression Microarrays AI Magazine, 25(1):23-44, 2004.
• Interesting: DNA Microarray Methodology Animation
• Recommended reading: Chapter 14, Manning & Schutze (see resources below).
• Lecture 4 - Gene Expression Analysis
• Protein Structure Prediction (6/5)
• Secondary structure prediction, threading, the ROSETTA method, docking
• Lecture 5 - Structure Prediction

Lecture notes and some reading materials can be downloaded here in Adobe PDF format. Lectures are based on the notes of Mark Craven, Michael Molla, Burr Settles, and Ameet Soni.

Other Bioinformatics Resources

Recommended Textbooks

	Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. R. Durbin, S.R. Eddy, A. Krogh, & G. Mitchison. Cambridge University Press, 1998. An introduction and overview for probabilistic models of proteins and nucleic acids.
	Foundations of Statistical Natural Language Processing. C.D. Manning & H. Schutze. MIT Press, 2001. An excellent introduction to NLP algorithms, many of which are also used in computational biology applications.

Relevant Journals and Conferences

• Bioinformatics - Oxford Journals
• BMC Bioinformatics - BioMed Central
• Intelligent Systems in Molecular Biology (ISMB)
• International Conference on Research in Computational Molecular Biology (RECOMB)
• European Conference on Computational Biology (ECCB)
• Other resources (from Wikipedia)

Java Programming Help

• Guide to the Java 1.5.0 API
• Java Tutorial