Higher eukaryotes exploit alternative pre-mRNA splicing to diversify their proteome, and to regulate gene expression with developmental stage-and tissue-specificity. Alternative splicing is prevalent, for example, the human genome contains 22,000 to 25,000 genes (less than twice the number of gene found in Drosophila melanogaster), with more than 60% of the genes are alternatively spliced. A striking example of the coding potential generated by pre-mRNA alternative splicing is found in the Drosophila Dscam gene which, through alternative splicing, as the potential to produce more than 33,000 different proteins. In order to get a better understanding of how alternative splicing regulates gene expression in Drosophila, we have developed a micro-array platform that is aimed at monitoring changes in the level of alternatively spliced mRNAs. In addition to providing a measurement of variation in gene expression, this platform enables us to identify changes in abundance of alternatively splice transcripts. A description of the platform used, our computational approach, as well as the different experiments performed using this platform will be presented.

The pioneering work of R. A. Fisher, E. J. G. Pitman and B. L. Welch on randomization tests published in the 1930ies did not find its way to statistical practice for a long time. The conceptually simple principle of conditioning on all permutations of the data is helpful to address a huge class of independence problems. Given that powerful and flexible software implementations are available we argue that a fresh look at permutation tests is fruitful.

Based on the theoretical framework of permutation tests published by Strasser & Weber (1999) we propose a unified computational framework for conditional inference. Applications include tests on independence between two variables measured at arbitrary scales as well as multiple testing procedures. Based on this framework it is easy to implement conditional versions of well-known procedures like linear rank tests, Cochran-Mantel-Haenszel tests or linear association tests and less well-known methods like maximally selected two-sample statistics. Much more interesting is the fact that new strategies for assessing independence can be implemented and evaluated on the fly.

To illustrate the flexibility of both the theoretical and computational components, permutation tests are applied to remove the variable selection bias from recursive partitioning procedures. We show how tree-structured regression models can be embedded into a statistical framework, i.e., with control of well defined errors. Moreover, we suggest an internal stop criterion for trees based on multiple testing procedures applied to the observations in each node of a tree. Benchmark experiments show that statistical internal stopping performs at least as good as the conventional post-pruning approach.

Joint work with Kurt Hornik and Achim Zeileis, Wirtschaftsuniversitaet Wien.

Cis-regulatory modules composed of multiple transcription factor binding sites control gene expression in eukaryotic genomes. We propose a hierarchical mixture approach to model the cis-regulatory module structure. Based on the model, a new de novo motif-module discovery algorithm, CisModule, is developed for the Bayesian inference of module locations and within-module binding sites. We illustrate the use of CisModule by its application to the discovery of a novel tissue-specific regulatory module in Ciona savignyi. In addition, comparative genomic studies show that regulatory elements are more conserved across species due to evolutionary constraints. Thus we further extend our approach to combine both module structures and cross-species orthology in motif discovery. We use a hidden Markov model (HMM) to capture the module structure in each species and couple these HMMs through multiple-species alignment. Our new method has been tested on both simulated and biological data sets, where significant improvement over other module discovery and phylogenetic motif discovery methods was observed.

Joint work with Wing Hung Wong.