Healthcare @ IBM Research | Australia - Genomics
Genomics @ IBM Research | Australia
Genomic technologies are poised to revolutionise the healthcare and life sciences industries with the promise of truly personalised medical treatments, targeted clinical trials and rapid resolution of pathogen outbreaks.
The costs of data generation are falling at an unprecedented rate and focus is shifting to the difficulties of data analysis, which have traditionally required advanced bioinformatics expertise. However, to truly realise the potential of genomic technologies we need tools that will empower bioinformatic-naïve staff and enable reliable data interpretation with minimal user-interaction time. Researchers at IBM Research - Australia are designing a platform to meet these needs.
A Scalable Enterprise Genomics Platform
Unlike other platforms, our output–oriented approach allows users to select and execute analyses based on the question they wish to answer. Users can readily explore the data, results and associated sample information to define populations of interest, while the automated logging procedures generate strict audit trails such as required for clinical or public health use. The platform is designed to be flexible and suitable for a broad range of genomics applications.
Click here to learn more about this technology.
Developing New Analysis Algorithms
Our team of software engineers, bioinformatics and subject-matter experts are also developing new analysis algorithms to help researchers, public health professionals and clinicians effectively interpret genomic data.
Understanding the way in which genomic variations are associated with phenotypic (physical, biochemical or behavioural) variations is an essential pre-requisite for utilising genomic data. Our team is collaborating with researchers at IBM Research - Haifa and the K-RITH Institute to find genomic signatures of antibiotic resistance among the bacterium which causes tuberculosis.
Collaborators: IBM Research - Haifa, K-RITH Institute
B. Goudey, M. Abedini, J. L. Hopper, M. Iniuye, E. Makalic, D. F. Schmidt, J. Wagner, Z. Zhou, J. Zobel, and M. Reumann. High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. BMC Health Information Science and Systems 2014 in press.
K. L. Wyres, T. C. Conway, S. Garg, C. Queiroz, M. Reumann, K. Holt and L. I. Rusu. WGS analysis and interpretation in clinical and public health microbiology laboratories: What are the requirements and how do existing tools compare? Pathogens 2014 vol. 3, p. 437-458.