Functional Genomics Research

Our group has a number of ongoing research projects related to regulation of gene expression and analysis of large scale functional genomics data. The focus is on understanding how gene expression depends on molecular regulatory mechanisms, as well as genetic and experimental factors. We are also interested in transcriptomic/genomic associations to human diseases. In particular, we are interested in integrative approaches that draw on the vast amounts of public data collected in ArrayExpress and other EBI resources, and we partake in a number of international collaborative projects.

Here is a brief summary of individual projects; feel free to contact us for additional information.

Human Gene Expression Map

We have integrated and analyzed a large amount of publicly available microarray data in order to map, for the first time, the global "expression space" of human gene activity (Lukk et al., 2010). Click here for more information.

A similar approach has been applied to study similarities in gene expression patterns between human and mouse (Manuscript submitted).

Human Gene Expression Map. Each dot represents a biological sample in a multidimensional gene expression space projected on the principal plane formed by the first (hematopoietic) and second (malignancy) principal axes. The dots are colored according to the biological group the sample belongs to. (a) The first principal component separates hematopoietic system–derived samples from the rest of the samples. (b) The second principal axis predominantly arranges cell line samples at the bottom, neoplasm samples in the middle and a mixture of non-neoplastic disease and normal samples at the top.

Fission yeast transcriptional regulation

In collaboration with Jürg Bähler's group at UCL, we study global gene expression programs in fission yeast (S. pombe). Currently we apply high-throughput sequencing technologies (in particular RNA-seq) to analyze gene regulatory networks during cell proliferation, differentiation and quiescence as well as the effect of various genetic and environmental perturbations (Aligianni et al., 2009).

Systems biology of fungal pathogens

The SYBARIS project investigates the genetic basis of susceptibility to fungal diseases and aims at elucidating the molecular mechanisms of drug resistance in fungal pathogens. We will use computational and bioinformatics methods to identify: (i) biomarkers of resistance to currently available treatments and (ii) novel putative drug target genes and pathways, in different fungi.

Methods for next generation sequencing data analysis

We have developed an R/Bioconductor based pipeline for RNA-seq data analysis, named ArrayExpressHTS, which will be available soon through Bioconductor and the EBI R workbench (Manuscript in preparation).

Also, methods for DNA sequence analysis and detection of different types of genetic variants are under development within the CAGEKID project, which focuses on renal cancer, as part of the International Cancer Genome Consortium.

Genome-wide association studies

We are studying how genetic variations affect gene expression using eQTL analysis, in particular to elucidate gene regulatory mechanisms and how different biases can be compensated for in the analysis. We are particularly interested in genomic and transcriptomic effects related to type 2 diabetes (Rung et al., 2009).

Within the ENGAGE project, we are working towards a large scale integrative analysis of diabetes related experiments in ArrayExpress and are also developing a pipeline for genome-wide imputation of genotypes based on data from the 1000 genomes and HapMap projects.

Visualisation of omics data for systems biology

One of our PhD students, Nils Gehlenborg, has recently finished a project on new bioinformatic methods for visualization and pattern detection in large scale biological datasets (Gehlenborg et al., 2010).

Bibliography

Aligianni, S. et al. (2009). The Fission Yeast Homeodomain Protein Yox1p Binds to Mbf and Confines Mbf-Dependent Cell-Cycle Transcription to G1-S Via Negative Feedback. PLoS Genetics 5:e1000626

Gehlenborg, N. et al. (2010). Visualization of omics data for systems biology. Nat Methods 7(3 Suppl):S56-68.

Lukk, M. et al. (2010). A global map of human gene expression. Nat Biotechnology 28:322-4

Rung, J. et al. (2009). Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genetics 41:1110-5.