UCD School of Medicine & Medical Science : Bioinformatics & Computational Biology

Data Interpretation through High Performance Computing

The 'Omic revolution has resulted in the creation of large data sets of information on protein, DNA and RNA sequence, structure and interactions. The development of high throughput screening technologies has accelerated this data creation and has presented scientists with a significant data interpretation challenge. New algorithms, software solutions and visualisation techniques have been developed by 'bioinformaticians' to assist in the interpretation of genomic, proteomic, transcriptomic and metabolomic information from molecular medicine research.

Example Research

Some examples of individual efforts and collaborative initiatives are shown below.

Multiple Sequence Alignment & Bioinformatics

Research in Prof Des Higgins' group centres on developing new bioinformatics and statistical tools for evolutionary biologists. The laboratory collaborates on the widely used Clustal and T-Coffee programs for multiple sequence alignment, researches the use of multivariate statistical methods for analysis of microarray data sets and addresses molecular evolutionary questions using bioinformatics approaches: for example, the evolution of promoters, of introns and of noncoding RNA genes.

In the 1980's Prof Higgins created the renowed Clustal programme which is widely used for aligning sets of related protein, DNA or RNA sequences together. Modern molecular biology revolves around the determination and analysis of sequences, and one of the most commonly used essential analyses is to compare a sequence to some relatives. Clustal has become the standard analysis method and continues to be developed by Prof Des Higgins and his collaborators at the European Molecular Biology labs in Heidelberg, Germany, and Hinxton, UK, and in Strasbourg, France.

Computational Infection Biology

Funded by the UK Wellcome Trust in 2011, the Computational Infection Biology programme is developing graduates with expertise in biology and computational modelling to lead interdisciplinary research programmes within biomedical research, bio-pharma and agri-food industries. The programme comprises 4 principal project areas:

Genomic analysis of microbial pathogens of animals and man.

Next generation sequencing technologies are being used to investigate virulence characteristics of pathogenic species. Mining the large data sets produced requires expertise in computational analysis which is accessed through collaborations with members of the bioinformatics and systems biology groups.

Novel and challenging targets in infectious disease.

Protein engineering, NMR, X-ray crystallography and other experimental methods are being employed in combination with computational modelling and prediction to identify key determinants critical for biomolecular interactions in infectious disease. The evolution of pathogens experiencing drug-induced selection is also being investigated.

Molecular mechanisms of viral disease.

Functional genomics and statistical methods are being applied to the study of drug response and evolution of viruses such as HIV, FIV and hepatitis B using large-scale genomic and proteomics datasets.

Host-pathogen interactions.

Several UCD groups are currently using functional genomics and proteomics approaches and comprehensive signal transduction analysis to study the response of the host and the pathogen during infection with Mycobacterium spp, Leptospira interrogans, Legionella, Rhodococcus, and influenza A viruses. Glycomics is also being used to study the interaction of microbes with the intestinal tract.

This research programme engages our researchers with investigators from other Schools and disciplines including biology, computer science, statistics, engineering, mathematics, chemistry, physics. PhD students in this programme participate in multi-disciplinary taught modules and are co-supervised by both an experimental research investigator and a computational or mathematics analyst.

Identifying Functional Regions of Proteins and Genes

Prof Denis Shields is UCD Professor of Clinical Bioinformatics and Principal Investigator at UCD Conway Institute and UCD Complex & Adaptive Systems Laboratory (UCD CASL). His research group focuses on identifying functional regions of proteins and genes that impact on disease risk and therapy. This is focused on functional oligopeptides and polymorphism in disease.

Genome wide association technology and sequencing technology are revolutionising the discovery of genetic associations. Prof Shield's team is interested in two disease areas: cardiovascular disease, and autism. They have particular interests in the identification of structural re-arrangements associated with disease, and in the role of functional amino acid changes that do not simply weaken protein structure.

The human genome sequence gives us a set of predicted protein sequences. Many of these proteins represent useful drug targets. However, simply knocking out or over-activating the primary enzymatic or signal transduction activity of such targets may not yield the most appropriate clinical response. For this reason, the pharmaceutical industry is increasingly focusing on targeting protein-protein interactions involved in more subtly modifying signalling in cells.

There is strong evidence (www.elm.eu.org) that some short regions of proteins (<10 amino acids) have independent function, often involved in mediating transient protein interactions. These are attractive starting points for modulating protein interactions, since the interface between proteins is often relatively small, making it easier to modulate.

Our group develop bioinformatic methods to systematically survey and identify short peptides and motifs of most interest from human and viral proteins. With collaborators, we experimentally validate which of these are likely to be bioactive peptides. Such peptides/peptidomimetics may then be used to tease apart signalling pathways important in health and disease, and ultimately may themselves be lead therapeutics to target aspects of pathways.

For this reason, we develop methods to identify non-peptide mimetic compounds that may be more powerful and clinically applicable modulators. The bioinformatic methods combine evolutionary modelling, structural modelling, chemical modelling and statistical modelling, and our researchers have been drawn from many backgrounds: biology, computer science, statistics, mathematics, chemistry, and engineering.