Abstract
Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction)
Highlights
Modern life science research projects often produce quantitative data, for example to quantify gene expression or metabolite concentration in tissue samples
A good example for such a type of factor can be the comparison of a wild (Solanum pennellii) versus a domesticated (S. lycopersicum) tomato species
The availability, efficiency, and relatively low cost of next-generation sequencing and metabolomics technologies allows their application in a wide variety of plant science research projects
Summary
Modern life science research projects often produce quantitative data, for example to quantify gene expression or metabolite concentration in tissue samples. The life scientist, having ordered such RNAseq or other Omics experiments, needs to investigate these count data to form scientific hypotheses and identify underlying biological patterns This involves plotting the quantitative data, carrying out Principal Component Analyses and correlation-based hierarchical clustering to elucidate differences between experimental conditions. Genetic or metabolic responses to the tested experimental conditions and treatments often are summarized by identification of enriched traits within significantly up- or down-regulated genes or metabolites of interest These steps often require manual programming, installation of software, or sending potentially confidential data to webservers for analysis. Such a file can be published for example in the form of an article’s supplement enabling readers to directly obtain the data, see plots and analysis results and even carry out their own subsequent investigations
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