An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.
ASJC Scopus subject areas
- Plant Science