Analyzing Big Data Sets From Small Molecules

The SSML focuses on metabolite research - small molecules such as sugar and amino acids. The group is a key node of Metabolomics Australia (MA) a Bioplatforms Initiative that supports life science research through technology and infrastructure investment in platform ‘OMICS technologies.

After the initial excitement surrounding the potential of genomics and proteomics, Garth feels that people are cautious about over-hyping metabolomics, but interest in the field is steadily growing. “Metabolomic analysis can be applied to anything from blood to fungus, and our projects range from clinical studies to agricultural biosecurity.” This exciting new field is increasingly being used for biomarker discovery and diagnostics in medicine, for example identification of metabolites that may indicate the presence of cancer cells.

The SSML use pattern recognition to separate treatments from control and identify which metabolites are contributing to variance, highlighting which biochemical pathways are involved. “Essentially, we use the Unscrambler to make sense of the large data sets we generate from very complex samples” explains Garth.

“We often profile over 400 different analytes in a single sample. Picking the patterns in that data would be impossible without the Unscrambler” says Garth. “We recently had a case where a client had data but couldn’t make sense of the results. After coming to us, we put their data into the Unscrambler and immediately could analyze patterns and were able to determine what was causing the difference between the samples.”