Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

GXP: Analyze and Plot Plant Omics Data in Web Browsers

Constantin Eiteneuer
,
David Velasco
ORCID logo ,
Joseph Atemia
,
Dan Wang
,
Andrea Schrader
ORCID logo ,
Roland Pieruschka
ORCID logo ,
Sven Fahrner
ORCID logo ,
Rainer Schwacke
,
Vanessa Wahl
,
Ulrich Schurr
ORCID logo ,
Björn Usadel
,
Asis Hallab
* ORCID logo
Version 1 : Received: 11 January 2022 / Approved: 24 January 2022 / Online: 24 January 2022 (12:36:04 CET)

A peer-reviewed article of this Preprint also exists.

Eiteneuer, C.; Velasco, D.; Atemia, J.; Wang, D.; Schwacke, R.; Wahl, V.; Schrader, A.; Reimer, J.J.; Fahrner, S.; Pieruschka, R.; Schurr, U.; Usadel, B.; Hallab, A. GXP: Analyze and Plot Plant Omics Data in Web Browsers. Plants 2022, 11, 745. Eiteneuer, C.; Velasco, D.; Atemia, J.; Wang, D.; Schwacke, R.; Wahl, V.; Schrader, A.; Reimer, J.J.; Fahrner, S.; Pieruschka, R.; Schurr, U.; Usadel, B.; Hallab, A. GXP: Analyze and Plot Plant Omics Data in Web Browsers. Plants 2022, 11, 745.

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, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

Keywords

RNA sequencing; metabolomics; data visualization; overrepresentation analysis; cluster analysis; principal component analysis; scientific plotting; Mapman; Mercator

Subject

Biology and Life Sciences, Biology and Biotechnology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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