Computational Genomics - Mark Ragan
|Professor Mark Ragan|
We use advanced bioinformatic and computational methods to investigate similarities and differences among genomes and the molecules they encode. Our goal is to make rigorous quantitative inferences, at both global and fine scales, about how genomes, gene and protein families, regulatory networks and cellular functions have evolved and diversified. To deal with very large quantities of data we use advanced data-management methods, implement high-throughput computational workflows, and develop new algorithms, approaches and software.
Genomes have diversified, both structurally and functionally, from shared ancestral states. We develop methods and employ analytical workflows to reconstruct the paths of descent (phylogenomics) and to study processes of change through time (evolutionary genomics). We have characterised pathways of lateral genetic transfer in which genetic information moves across, not within, bacterial lineages, and have developed statistically based approaches to discover genetically recombined regions and recombination breakpoints. We are applying these approaches to understand how laterally acquired genetic material is incorporated into the regulatory and functional networks that underlie bacterial pathogenicity and virulence.
The other major direction of our research is the inference, comparison and analysis of biomolecular networks in mammalian cells in normal development and disease. We are developing scalable approaches that let us interrogate diverse data types including molecular sequences (single-nucleotide polymorphisms and copy-number variation), protein and RNA structures, metabolic and signalling pathways, regulatory and molecular interaction networks, gene expression profiles, subcellular localisation, cellular function, orthology maps and phylogenetic profiles.
Lateral genetic transfer and the evolution of pathogenicity
In many bacteria, genetic material can be transmitted not only vertically from parent to offspring within a cellular lineage, but also laterally (horizontally) among unrelated lineages. Lateral genetic transfer (LGT) can occur not only under strongly selective conditions such as those found in hospitals or agricultural feedlots but also in natural environments, generating metabolic innovation and enabling the origin and spread of multidrug-resistant superbugs. We are developing novel, highly scalable bioinformatic workflows that identify genomic regions of lateral origin, infer LGT relationships and delineate communities of genetic exchange. We collaborate with genomic scientists to sequence and annotate bacterial genomes, with computer scientists to develop rigorous algorithms and carry out exact computation at extremely large scale, and with experimentalists to validate our predictions and understand their biological consequences.
We are investigating the role of LGT in the evolution of cellular networks of gene regulation, protein-protein and protein-small molecule interaction, and signalling in specific groups of pathogenic bacteria. We are also funded to examine the interplay among units of lateral transfer, structural features of genetic exchange communities, and systems properties of genetic flow across the microbial biosphere including phage and plasmid vectors.
Inference of biomolecular interaction networks in cancer
Life is sustained by networks of biomolecular interactions within and among cells. As a cell develops, its regulatory and transcriptional program unfolds. Successive states of this program – the developmental trajectory – can be defined by differential abundance of individual gene products at different subcellular locations. Sometimes a trajectory culminates not in a normal mature phenotype, but instead in one characterised by self-sufficiency in growth signals, insensitivity to inhibitory signals, and the ability to avoid apoptosis, divide indefinitely, metastasise, invade other tissues and sustain angiogenesis – i.e. cancer.
We are using data from whole-genome and exon sequencing, transcriptomics and epigenomics to infer and analyse signalling and transcriptional regulatory networks in normal and malignant cells. We are developing analytical modules that use machine learning, ontologies and advanced data integration to infer local network topology. We run these workflows on high-performance computing infrastructure, and collaborate with specialists in systems modelling.
We are selectively recruiting postdoctoral researchers, postgraduate students and exceptional undergraduate trainees into both the bacterial LGT and cancer network project areas (details here). Your training may be in genomics, molecular biology, cancer biology or microbiology, or alternatively in computer science, statistics or discrete, computational or applied mathematics. Postdoctoral candidates in particular must demonstrate strong commitment to high-level research across disciplinary boundaries, i.e. biologists must bring strong quantitative skills, and all group members will address real biological problems through hands-on analysis of large empirical datasets.
- Scalable vertical and lateral phylogenomics based on k-mers
- LGT as a driver of bacterial pathogenicity and virulence
- Genetic exchange communities: biological nature, robustness and connectivity
- Fine-scale mapping of orthology and paralogy across vertebrate genomes
- Understanding how mutations, genomic rearrangements and transcriptional variation rewire cellular networks in breast, ovarian or pancreatic cancer
- Molecular interaction networks in cellular (spatiotemporal) context
Skippington, E., and Ragan, M.A. (2011). Lateral genetic transfer and the construction of genetic exchange communities. FEMS Microbiology Reviews 35: 707-735.
Chan, C.X., Beiko, R.G., and Ragan, M.A. (2011). Lateral transfer of genes and gene fragments in Staphylococcus extends beyond mobile elements. Journal of Bacteriology 193: 3964-3977.
Ragan, C., Zuker, M., and Ragan, M.A. (2011). Quantitative prediction of miRNA-mRNA interaction based on equilibrium concentrations. PLoS Computational Biology 7: e1001090.
Bodländer, H.L., Fellows, M.R., Langston, M.A., Ragan, M.A., Rosamond, F.A., and Weyer, M. (2011). Quadratic kernelization for convex recoloring of trees. Algorithmica 61: 362-388.
Maetschke, S.R., Kassahn, K.S., Dunn, J.A., Han, S.P., Curley, E.Z., Stacey, K.J., and Ragan, M.A. (2010). A visual framework for sequence analysis using n-grams and spectral rearrangement. Bioinformatics 26: 737-744.
Davis, M.J., Sehgal, M.S., and Ragan, M.A. (2010). Automatic, context-specific generation of Gene Ontology slims. BMC Bioinformatics 11: 498.
Walter, T., Shattuck, D.W., Baldock, R., Bastin, M.E., Carpenter, A.E., Duce, S., Ellenberg, J., Fraser, A., Hamilton, N., Pieper, S., Ragan, M.A., Schneider, J.E., Tomancak, P., and Hériché, J.-K. (2010). Visualization of image data from cells to organisms. Nature Methods 7: S26-S41.
Chan, C.X., Darling, A.E., Beiko, R.G., and Ragan, M.A. (2009). Are protein domains modules of lateral genetic transfer? PLoS ONE 4: e4524.
Kassahn, K.S., Dang, V.T., Wilkins, S.J., Perkins, A.C., and Ragan, M.A. (2009). Evolution of gene function and regulatory control after whole-genome duplication: comparative analyses in vertebrates. Genome Research 19: 1404-1418.
Ragan, M.A., and Beiko, R.G. (2009). Lateral genetic transfer: open issues. Philosophical Transactions of the Royal Society of London B: Biological Sciences 364: 2241-2251.
|Computational Genomics - Mark Ragan section|