BIOINFORMATICS

This is a new research area and currently six members of staff (Alex Gammerman, Alberto Paccanaro, Hugh Shanahan, Victor Solovyev, and Chris Watkins) are involved in building up collaborative research.

World-wide research in molecular biology is producing large amounts of data that stand in need of computerised analysis. The data is of several types: not only sequence data of DNA and of proteins, such as that provided by the Human Genome Initiative, but also the three-dimensional structures of some proteins, the simultaneous measurement of levels of activity of large numbers of genes, and data on the binding affinities of many antibodies to many substances.

This new wealth and variety of data contains the answers to many scientific questions and the keys to many medical advances. But to answer the new types of question that are being asked, new computational techniques are needed, and machine learning is a field that is providing some of the answers. Within the last four years, techniques from machine learning have become standard methods in computational biology

Current Research

We are involved in a variety of different areas within Bioinformatics including gene prediction, protein function prediction, protein-protein interaction predictions, reconstructing Biological networks from transcriptomic data and identifying markers in human serum using proteomic data. In particular, we are adapting machine learning and other methods developed in the Department in this area.

Research Opportunities

This is a new and exciting area of research and we are looking for postgraduate students to work with us. We have an active PhD community and the MSc in Computer Science by Research includes a Bioinformatics Strand.

Recent and Currently Held Research Grants

• Overlapping community detection methods for biological applications
• Development of graph theoretic approaches to predict protein function by integrating large scale heterogeneous data
• BioSynLab: a software platform for the analysis of metabolic data
• Graph theoretic approaches for predicting protein-protein interactions
  
• Pattern recognition techniques for gene and promoter identification and classification in plant genomic sequences
• AMORPH - Amorphous computing, Random Graphs and Complex Biological Systems

Bioinformatics resources and tools developed at Royal Holloway

• Cluster ONE -- Clustering with Overlapping Neighborhood Expansion
• SCPS -- Spectral Clustering of Protein Sequences
• GFam
  
• SimplyGO
• BioSynLab
  
• Plant promotor and gene prediction
• HTHquery - a method for detecting DNA binding proteins with a HTH motif

Publications from the group