Statistics 345:
Statistical Methods in Computational Biology
Spring 2008
● T Th
11:00-12:15 PM
●
Redwood G19
Nancy R. Zhang
● nzhang
atstanford
●
Office Hours: Tues 2:15-4:00 PM Sequoia Hall 141
C O U R S E D E S C R I P T I O N
This course will have 3 parts:
 |
Methods for analysis of biological sequences.
Topics include sequence scans for predefined signals, pairwise sequence
alignments, multiple sequence alignments, and genome-wide alignment. This
is a more traditional subject in computational biology and the lectures will
focus on the basic theoretical concepts. However, there are also many exciting new
developments, which students will have a chance to explore
by presenting a critique of a paper of their choosing. A list of possible
papers will be provided. |
 |
Statistical modeling of transcription regulation.
We will
cover models for representation of transcription regulatory sites, and
methods for detecting them from a combination of sequence, expression,
and phylogeny data. As for part 1, the lectures will focus on the
statistical methodology. Students will present a critique of a paper of
their choosing from a provided list. |
 |
Analysis of whole genome profiling data. We will
look at statistical methods for the analysis of genome-wide profiling data,
focusing on DNA-copy number data. The lectures will explore common
themes such as hidden Markov models, signal detection, segmentation, and
cross-sample analysis. |
T A
Andres Villaquiran, office hours TBAT E X T B O O K S
A good text book for the first part of the course is:
Ewens and Grant, Statistical Methods in Bioinformatics, 2001
Springer-Verlag, New York.
The materials for the second and third part of the course will be posted as
needed on this website.
P R E R E Q U I S I T E S
The first four lectures (on sequence alignment and scans) are more
theoretical and require a background in probability at the level of Statistics
217 and 218. If you do not have this background, you may want to prepare by
reading chapters 1, 2, 4, and 7 in Ewens and Grant. The rest of the course
require statistics at the level of Stat 200. You also need to have
background in genetics at the undergraduate level. You are not
required to be proficient in any specific programming language, however, I
imagine you need to be proficient in some language in order to complete the
final project.
L E C T U R E S
| Date |
Subject |
Reading / Handouts |
| Tu 4/1 |
Scanning a single biological sequence for a signal.
Lecture Slides |
The background on random processes are covered in EG
Chapter 7. Course material are taken from:
Brendel and Karlin
(1992), Karlin and
Altschul (1990). |
| Th 4/3 |
Pairwise sequence alignment. |
EG 6.4-6.5, 10.1 - 10.3. |
| Tu 4/8 |
Pairwise sequence alignments, phase transitions.
Lecture Slides |
Course materials taken from: Altschul et al. (2001),
Waterman and Vingron (1994),
Chan (2003). |
| Th 4/10 |
Pairwise alignments, PAM matrices, ClustalW. |
Thompson et al.
ClustalW paper |
| Tu 4/15 |
Multiple sequence alignments, HMMs.
Lecture Slides |
EG Chapter 12, HMM notes.
Hughey and Krogh (1995) |
| Th 4/17 |
Whole genome alignments and other miscellaneous topics
Lecture Slides |
Siepel et al. (2005),
Supplementary information. |
| Tu 4/22 |
Discussion of papers |
Reading list for part I. |
| Th 4/24 |
Motif analysis: Introduction to transcription regulation.
Lecture Slides |
|
| Tu 4/29 |
Motif analysis: Gibbs Sampler based
approaches. Lecture Slides |
Liu et al. (1995) |
| Th 5/1 |
Motif analysis: Regression based approaches.
Lecture Slides |
Zhang et al. (2008) |
| Tu 5/6 |
|
|
| Th 5/8 |
Motif analysis: Phylogenetic footprinting
and other topics. Lecture Slides |
Zhou and
Wong (2008) Notes for Gibbs
Samplers in motif finding. |
| Tu 5/13 |
Paper Discussions |
Reading list for part
II., papers are here. |
| Th 5/15 |
Genome wide profiling: Change-point methods |
|
| Tu 5/20 |
Genome wide profiling:
More on hidden Markov models |
|
| Th 5/22 |
Genome wide profiling: Cross
-sample analysis |
|
| Tu 5/27 |
Genome wide profiling:
Low-level normalization. |
|
| Th 5/29 |
TBA |
|
| Tu 6/3 |
Final presentations |
|
| Th 6/5 |
Final Presentations |
|
G R A D I N G
| 2 paper reviews |
each 25% |
| Project |
50% |