IRR: Bioinformatics
Bioinformatics, (including Computational Biology, Systems Biology and Synthetic Biology)
What differentiates the sub-area from the other programmes on the MSc portfolio?
Bioinformatics is an interdisciplinary topic that unites all of the major disciplines in the computational sciences with those across the biological sciences. From the computational perspective there are deep roots in databases and data sciences for storing, handling and querying the highly complex data that the biosciences generate. Equally important are methods from artificial intelligence and natural language processing from where many of the analytical methods originate. From the biological perspective bioinformatics research spans the entire gamut from fundamental basic biology and ecology through to the highly domain specific biomedical sciences that focus on human health and disease.
Are there go-to sources to find reviews and perspectives on these research topics?
Reviews and perspectives in bioinformatics are less commonly available as it is interdisciplinary. All of the journals below contain a mix of articles, reviews and application notes (e.g. software packages).
What are the key journals or conferences in the field for finding high quality research papers in these topics?
BCM Bioinformatics (https://bmcbioinformatics.biomedcentral.com)
Bioinformatics Journal (https://academic.oup.com/bioinformatics/issue/37/17)
Frontiers in Bioinformatics (https://www.frontiersin.org/journals/bioinformatics)
Molecular Systems Biology (https://www.embopress.org/journal/17444292)
Briefings in Bioinformatics (https://academic.oup.com/bib)
Conferences
Intelligent Systems for Molecular Biology (https://www.iscb.org/about-ismb)
European Conference on Computational Biology (http://eccb.iscb.org)
Rocky Bioinformatics Conference (https://www.iscb.org/rocky2021)
Are there any particular high profile or rapidly growing research areas in the programme that you would suggest might be worth looking at for potential IRR themes.
Phylogenetics and the computational modelling of evolution. One of the first topics that helped start bioinformatics and remains a hot topic - e.g. the world is currently obsessed with viral evolution.
Data integration. How to integrate across multiple, complex and noisy data sources obtained from biological samples.
Computational drug design. A whole series of topics where the fundamental sequences of DNA/Proteins are modelled in 3D and the best estimates for their structures matched against databases of known chemical entities to try and predict interactions for potential drug pathways but also for potential toxicity prediction.
Bio-ontologies. Again a pioneering subject in bioinformatics. The inherently hierarchical structure of much of the biosciences (e.g. anatomy, phylogeny) lends itself to advanced computational methods for data representation and reasoning. This is extending into graphical models and databases (next topic)
Network analysis. Interaction networks pervade biology from the smallest scale molecular interaction networks to environmental scale interactions ( e.g. species and habitats).
Computational diagnosis of disease and/or treatment planning. The use of large scale biological datasets (e.g. genomics, proteomics, metabolomics) to predict disease progression (e.g. cancer, neurodegeneration, cardiovascular) and/or the best type of treatment to tailor to an individual.
Biomedical natural language processing. Using NLP and text-mining techniques to structure largely unstructured datasets from medical and literature sources to enrich datasets enabling novel modeling and understanding of biological and biomedical processes and systems.
Contributers: JD Armstrong, T Ian Simpson
Last update: 13 October 2021