Thomas Schlitt

MSc Bioinformatics
Data Analysis of Large-Scale Experiments in Molecular Biology

go to the 7BBYMDAL module webpage

Content

Aims
Learning Outcomes
Provisional Syllabus
Module Schedule
Reading List

Aims

To extend students' understanding of the theory of data analysis of large-scale experiments in molecular biology, as well as provide students with an overview of the current major areas of research in this field.

Learning Outcomes

At the end of this module students should be able to:

compare methods for the analysis of microarray, protein-protein interaction and ChIP-on-chip datasets
compare different microarray technology platforms
design the necessary steps for microarray data analysis
understand and apply statistical methods used in microarray data analysis
evaluate different approaches to identify transcription factor binding sites
compare different approaches to modelling gene regulatory networks
explain power-law, small-world characteristics
appraise different types of large-scale biological data-sets
compare multiple alignment and phylogenetics methods applied to biological sequence data
set up and assess the output of simple R scripts for microarray and other data

Provisional Syllabus

Microarray technologies

cDNA arrays, competitive hybridisation
oligonucleotide arrays for gene expression, exon arrays, tiling arrays

Microarray data processing

data quality control
background correction and empirical models for RNA hybridisation
normalisation methods (local regression, variance stabilisation, etc.)

Finding differential expression

standard hypothesis testing, linear models with fixed and/or random effects
bayesian analysis (empirical bayesian models, conjugate priors approaches)
bootstrap and permutation methods
multiple testing corrections

Finding transcription factors binding sites

transcriptional regulation
regulatory regions
finding consensus binding sites
transfac

Introduction to Gene Networks Models

what is a network model?
why do network modelling?
dynamic models of small gene networks: boolean, difference, differential and hybrid models

Large-scale Gene Networks

power-law structure, small world behaviour
large-scale gene networks
transcription networks

Large-scale Protein Networks

protein interaction networks
literature networks
ChIP-chip networks
Predicting gene functions from networks
Assessing predictions using ROC curves

Exam to coursework ratio 70:30
Assessment 1 exam, 1 seminar presentation
Normally examined in June
Course Structure 20 hours lectures, 10 hours tutorials

taught in the second term
Lecturers Eric Blanc, Roli Roberts, Sophia Tsoka, Thomas Schlitt
Prerequisites none

Module Schedule

Week/Lecturer	Lecture 2 hrs	Seminar/Tutorial 1 hr
Week 1 Eric Blanc	Microarray technologies cDNA arrays, competitive hybridisation oligonucleotide arrays for gene expression, exon arrays, tiling arrays	tutorial
Week 2 Eric Blanc	Microarray data processing data quality control background correction and empirical models for RNA hybridisation normalisation methods local regression, variance stabilisation, etc.	tutorial
Week 3 Eric Blanc	Finding differential expression I standard hypothesis testing, linear models with fixed and/or random effects bayesian analysis empirical bayesian models, conjugate priors approaches	Journal Club (assessed)
Week 4 11.02.09 Eric Blanc	Finding differential expression II Clustering & biological interpretation of results	tutorial
Week 5 Thomas Schlitt	Introduction to Gene Networks Models transcriptional regulation regulatory regions finding consensus binding sites transfac what is a network model? why do network modelling? dynamic models of small gene networks: boolean, difference, differential and hybrid modelstranscriptional regulation	tutorial
Week 6 Thomas Schlitt	Large-scale Gene Networks power-law structure, small world behaviour large-scale gene networks transcription networks ChIP-chip networks	tutorial
Week 7 03.03.09 Thomas Schlitt	Large-scale Protein Networks I protein interaction networks literature networks	tutorial
Week 8 Thomas Schlitt	Large-scale Protein Networks II metabolic networks	in course assessment mock exam handout
Week 9 Thomas Schlitt	Large-scale Protein Networks III Predicting gene functions from networks Assessing predictions using ROC curves	mock exam submission
Week 10	tba	mock exam results

Reading list

Sorin Draghici "Data Analysis Tools for DNA Microarrays"
Chapman & Hall/CRC; Har/Cdr Re edition 2003 ISBN-10: 1584883154
Uri Alon "An Introduction to Systems Biology: Design Principles of Biological Circuits"
Chapman & Hall/CRC 2006 ISBN-10: 1584886420
Michael J. Crawley "Statistics: An Introduction Using R"
John Wiley and Sons Ltd 2005 ISBN-10: 0470022981
R documentation
in particular W. N. Venables, D. M. Smith and the R Development Core Team
"An Introduction to R"
or alternatively William N. Venables, David M. Smith "An Introduction to R"
Network Theory Limited 2002 ISBN-10: 0954161742
Quackenbush, J. (2002).
Microarray data normalization and transformation. Nat Genet 32 Suppl, 496-501.
Schlitt, T., and Brazma, A. (2005).
Modelling gene networks at different organisational levels. FEBS Lett 579, 1859-1866.