Next:
Practicalities
Statistical Methods in Genetics and Bioinformatics
Practicalities
Logistics
Instructor
TAs
Grading
Website
Time and Place:TTh 11.-12.15pm , 370-370
First session on R:Th 5-6pm , Sequoia PC room
Section on Python, Th 5-6pm, Sequoia 200
Textbooks
Homeworks
Homework 1: Due Tuesday October, 15th in class
Homework 2: Due Thursday October, 17th in class
Homework 3 Due October 24th, 2002
Projects
Step one : to obtain a midterm grade
Handouts
Lectures
Lecture 1 : Presentation: 09/26/02
Reading for non biologists
Reading for biologists
Lecture 2: Introduction to Genetics 10/01/02
Building blocks
Atomic Level
Nucleotide -Level
Amino Acids
Proteins
Probabilistic Tools
Binomial Distribution
Multinomial Distribution
Lecture 3: What is ML, what is MC? 10/03/02
Maximum Likelihood of Multinomial Cell Probabilities
The Bayesian Paradigm
About Priors
Calibrating degrees of belief
Conjugate Priors
Binomial-Beta
Beta priors for the Binomial parameter
Beta family
Normal-Normal
Multinomial-Dirichlet
What is a
Monte Carlo
Method?
Lecture 4: Markov Chains and Hidden Markov Models
Lecture 5: Hidden Markov Models, Viterbi, Forward and Backward Algorithms
Lecture 6: HMM for Protein Families
Lecture 7: More Bayesian Computations
Lecture 7b: Semi hidden Markov Models: Genscan
Lecture 8: Gibbs sampling for motif recognition
Lecture 9: Molecular Evolution and Continuous Time Markov chains
Lecture 10: Evolutioniary trees: nucleotide level
Lecture 11: Evolutioniary trees: software and examples
Lecture 12a: Phylogenetic Trees: software and examples
Lecture 12b: Extreme Value Theory
Lecture 12c: Multivariate Statistical Methods: a taxonomy
Lecture 14:
References
General Statistics Links
Testing and classical Statistics Links
What is R?
Helpful note on X servers and windows
Courses on Genetics,Molecular Evolution, Computational Biology
Probability and Statistics Classes
Databases
Lists of biocomputing software
Glossaries
Susan Holmes 2002-11-05
