High-throughput generation of genomics data leads to huge quantities of information about the inner workings of the cell, but this requires the right kind of analytics for interpretation.
Researchers at The Genome Analysis Centre (TGAC) and Jagiellonian University have used a new workflow to find links between metabolites and genes using a wide range of different data sources, which include vast amounts of information about various types of molecules. This is in order to understand more about cellular metabolism and gene expression in complex biological systems, and can be a very complex process. The paper about the workflow, dubbed ONION (omics data integration on molecular networks), is published in PLOS ONE, and the workflow prototype is available at Bitbucket.
The process breaks the lipidomics and transcriptomics data down into smaller groups based on known molecular interactions, and then analyses the groups using statistical methods. According to the researchers, this results in more accurate results than analysing the whole dataset at once. As an example, the technique was used to detect the genes related to lipid metabolism in a mouse nutritional study, and it improved the detection of genes by 15%.
The first step will be to use the workflow to research the benefits of broccoli for prostate cancer, in collaboration with the Institute of Food Research, as well as the health benefits of flavonoids in a variety of fruits and vegetables, in collaboration with the University of East Anglia.
Wiktor Jurkowski, Integrative Genomics Group Leader at TGAC, says: “Knowledge gathered in molecular networks can be harnessed to improve data integration and interpretation. Our approach, integrating transcriptomics and metabolomics data will help interpret signals measured by omics techniques to extend our knowledge of processes under specific biological conditions. Therefore, benefiting biologists in interpreting data, creating better hypothesises and pinpointing genes and metabolites involved to unravel the mechanism of interest.
While GenoKey wasn’t involved in this research, the company has created tools and techniques that allow the mining of very complex data sets.