Announcements

Lectures

Lab

Homework

Final project

Datasets

References

Time and place

N.B. New classroom for lectures
Lectures: TuTh 2:00-3:30pm, 2320 Tolman Hall.
Computer lab: Thursday 11:00-12:00, 340A Haviland Hall.

Sandrine Dudoit
E-mail: sandrine@stat.berkeley.edu
Office hours: Tu 3:30-4:30pm and Th 5:30-6:30pm in 109 Haviland Hall.

Merrill Birkner
E-mail: mbirkner@stat.berkeley.edu

This course provides an introduction to statistical methods in computational and genomic biology.

Statistical topics, to be introduced in a biological context, include: numerical and graphical summaries of data, basic notions in probability, loss-based estimation (maximum likelihood, least-squares), model selection, multiple hypothesis testing, Markov chains, hidden Markov models, resampling, simulation, introduction to R. Biological questions to be considered include, but are not limited to: modeling meiosis, genetic mapping, nucleotide and protein sequence analysis, DNA microarray data analysis.
The course will also introduce statistical computing resources for the analysis of biological data, with emphasis on the R language and environment, and Bioconductor software packages.
In addition to discussing specific statistical and computational methods, the course will provide an introduction to basic notions in genetics and molecular biology and will involve the critical reading of articles related to statistical analyses in the biological and medical sciences.

I encourage you to attend PH 296, the Statistics and Genomics Seminar (schedule and abstracts)  

Public Health 143, Section 001
Course control number: 76053 
Units: 4

PH142A or consent of instructor. 

Some familiarity with the S (R or S-Plus) language. Tutorials are available on the R and Bioconductor websites.

No formal training in biology is required; basic notions will be presented in class and references will be provided for further reading.

50% homework and 50% final project. 

Assignments will involve both theory and biological data analysis using R.

The final project will consist of a written report and poster presentation on a topic that involves the application of statistical and computational methods to address a particular biological question. I will provide a list of suggested topics.

There is no required text. I will provide lecture notes and references on the website.