REACTOMEgsa · Extending the REACTOME Pathway Database for multi-omics biomedical data analysis

Extending the REACTOME Pathway Database for multi-omics biomedical data analysis

"The increasing availability of high-throughput ‘omics technologies results in unprecedented opportunities for precision medicine and biomedical research. Increasingly available approaches such as Transcriptome sequencing (RNA-seq), mass spectrometry (MS)-based shotgun proteomics, and microarray studies enable us to characterise genome- and proteome-wide expression changes. All of these techniques share a common challenge: to derive biologically meaningful knowledge from long lists of regulated genes or proteins. Pathway analysis techniques have emerged as a solution to this problem. Pathway analysis techniques use existing biological knowledge for data reduction. Instead of working with a list of single proteins or genes, researchers can work on the biologically more relevant pathway level allowing a more intuitive interpretation of the data. Linking genes through pathways additionally increases the power of the statistical analysis. While single genes or proteins may only show small, non-significant changes, synchronous changes within a pathway may reveal a biologically important finding. Reactome is a free, open access, open source, open data, curated and peer-reviewed knowledge base of biomolecular pathways. Its powerful web interface has made Reactome one of the most popular resources for pathway information with 16,450 users per month from February 2016 to February 2017 (a 22% increase from the previous year). Its stringent manual curation by PhD-level scientists with backgrounds in cell and molecular biology and constant peer-review through close cooperation with independent investigators within the community provide highly reliable pathway data for biomedical research. In this project, I developed a novel pathway analysis system ""ReactomeGSA"" for the existing Reactome pathway resource. ReactomeGSA can perform comparative pathway analyses across species and 'omics technologies. Therefore, it is now possible to, for example, easily compare a mouse-based proteomics experiment with the data from a clinical human study. This enables researchers to quickly see whether studies led to comparable results. Additionally, it is also possible to immediately see whether the results obtained from animal or cell-line based experiments are consistent with matching human data. Previously, such comparisons required in-depth bioinformatics knowledge and were thus not available to most researchers. The new analysis system allows researchers to now perform these previously complex analysis within minutes."

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз