Novel workflow provides cellular insight

Opposed to current methods that apply statistical analysis to data sets as a whole, the proposed workflow breaks the initial data into smaller groups determined by known molecular interactions, the researchers said.

Statistical methods can then be applied to these groups resulting in more accurate results than if the analysis had been applied to the whole dataset.

According to the researchers, this technique has been shown to improve the detection of genes related to lipid metabolism on an example mouse nutritional study that increases our understanding of biochemical fluctuations by 15%.

Identifying associations between metabolites, small molecules produced during metabolism, and genes is crucial to understanding processes in the cell.

However, uncovering these relationships is a complex task, especially when integrating data that concern various types of molecules. Adding to this complexity is the vast quantity of data available for analysis, a result of the development of new experimental high-throughput techniques.

Study co-author Wiktor Jurkowski said: “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,” Jurkowski added.

A full account of the research was first published in the journal PLOS ONE.