Statistical Bioinformatics

Statistical Bioinformatics • Genomics • Transcriptomics • Proteomics • Systems Biology

Multiple Sequence Alignment (MSA)

Multiple Sequence Alignments (MSA):

Some past forces shaping MSAs • Divergence of sequences by speciation and nucleotide substitution (Phylogenetics). • Horizontal gene transfer (recombination), especially in bacteria and viruses.

TOPALi v.1 Recombination detection FrankWright,Iain Milne & Dirk Husmeier

TOPALi applied to Roseburia and Eubacterium sequences

Some past forces shaping MSAs • Divergence of sequences by speciation and nucleotide substitution (Phylogenetics). • Horizontal gene transfer (recombination), especially in bacteria and viruses. • Selective pressure acting on functional domains.

TOPALi v2 Future plans • Detect genomic regions under selective pressure functional domains in proteins • Methodology development: combined prediction of breakpoints due to recombination and evolutionary rate change. • Improved phylogenetic analysis • Investigate use of UK GRID computationalresources for faster analyses

Genes differently expressed between two conditions • Affymetrix microarray Mouse liver experiment • Low fat diet vs high fat diet (6 per group) • Plot of log-fold change vs. average log intensity. • Points far away from the horizontal line seem “differentially expressed”. • Which are significant?

Statistical Methods (SAM, Limma,…) help to detect significant genes • BUT: Many methods assume that the variances in both groups are the same • If this is not the case: • Algorithms might give wrong answers • The definition of “differential expression” becomes more difficult

Claus Mayer (BioSS) • More complex statistical tests for detecting differential gene expression. • Situations where standard assumptions are violated. • Allows for different variance-covariance structures in both populations.

Proteomics: 2-D Gels How to compare gels 1 and 2? gel 1 gel 2

Chris Glasbey: Nonlinear Warping John Gustafsson, Chalmers University, Sweden WARP

2-D Gel Comparison Two gels superimposed (in different colours)

Proteomics:2-DGel Interpretation • Graham Horgan • Identify spots which differ between treatments using variance and covariance information from other spots differently expressed proteins • Assessment of associations between spot densities and physiological variables.

Detect active pathways in a “known” network • Network of protein-protein and protein-DNA interactions “known” from the literature • Gene expression profiling for different conditions • Bacterial strains: promoting - preventing inflammation • Mice on a low-fat vs. high-fat diet • Can we identify different pathways associated with these conditions? • We need a robust method • Expression data: noisy, missing values • Post-translational modifications

Cytokine Network • Collaboration with SCGTI • Interferon Pathway • Cytokines • Pivotal role in modulating the innate and adaptive mammalian immune system • Network of protein-protein and protein-DNA interactions from the literature • Two gene expression times series from bone marrow-derived macrophages in mice • Infected with cytomegalovirus • Infected and treated with IFN-gamma

Reverse Engineering of Regulatory Networks • Can we learn the network structure from postgenomic data themselves? • Statistical methods to distinguish between • Direct correlations • Indirect correlations • Challenge: Distinguish between • Correlations • Causal interactions • Breaking symmetries with active interventions: • Gene knockouts (VIGs, RNAi)

Evaluation: Raf signalling pathway • Cellular signalling network of 11 phosphorylated proteins and phospholipids in human immune systems cell • Laboratory data from cytometry experiments • Down-sampled to 100 measurements • Sample size indicative of microarray experiments • Two types of experiments: • Passive observations • Active interventions (gene knockouts) • Literature: “gold-standard” network

Statistical Bioinformatics