scholarly journals Metabolite AutoPlotter - an application to automatically process and visualise metabolite data in the web-browser

2020 ◽  
Author(s):  
Matthias Pietzke ◽  
Alexei Vazquez
Keyword(s):  
Stable Isotope ◽  
Data Processing ◽  
Ease Of Use ◽  
Complex Data ◽  
Isotope Tracing ◽  
Wide Audience ◽  
Web Browser ◽  
Seamless Integration ◽  
Metabolomics Data ◽  
Copy And Paste

Abstract Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not processed in full depth, so potential novel findings might get lost. Methods To tackle this problem we developed Metabolite AutoPlotter, a tool to process and visualise metabolite data. It reads as input pre-processed compound-intensity tables and accepts different experimental designs, with respect to number of compounds, conditions and replicates. The code was written in R and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter. Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13-C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each compound as well as sorted and restructured tables that can be used for further analysis. Conclusion With the help of Metabolite AutoPlotter it is now possible to automate data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual processing. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

scholarly journals Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser

2020 ◽  
Author(s):  
Matthias Pietzke ◽  
Alexei Vazquez
Keyword(s):  
Stable Isotope ◽  
Data Processing ◽  
Ease Of Use ◽  
Complex Data ◽  
Isotope Tracing ◽  
Wide Audience ◽  
Web Browser ◽  
Seamless Integration ◽  
Metabolomics Data ◽  
Scripting Language

Abstract Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not analysed in full depth, so potential novel findings might get lost. Methods To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter. Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis.Conclusion With the help of Metabolite AutoPlotter it is now possible to simplify data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

scholarly journals Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

Metabolites ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 378 ◽  
Author(s):  
Selina Hemmer ◽  
Sascha K. Manier ◽  
Svenja Fischmann ◽  
Folker Westphal ◽  
Lea Wagmann ◽  
...  
Keyword(s):  
Data Processing ◽  
Open Source ◽  
High Resolution Mass Spectrometry ◽  
Liver Microsomes ◽  
Reversed Phase ◽  
Ease Of Use ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Metabolomics Study ◽  
High Flexibility

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

scholarly journals Physiological impact of in vivo stable isotope tracing on cancer metabolism

2021 ◽  
pp. 101294
Author(s):  
Manuel Grima-Reyes ◽  
Adriana Martinez-Turtos ◽  
Ifat Abramovich ◽  
Eyal Gottlieb ◽  
Johanna Chiche ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Cancer Metabolism ◽  
Isotope Tracing ◽  
Physiological Impact

Stable isotope tracing to assess tumor metabolism in vivo

2021 ◽  
Author(s):  
Brandon Faubert ◽  
Alpaslan Tasdogan ◽  
Sean J. Morrison ◽  
Thomas P. Mathews ◽  
Ralph J. DeBerardinis
Keyword(s):  
Stable Isotope ◽  
Tumor Metabolism ◽  
Isotope Tracing

Bringing ESMValTool to the Jupyter Lab

2021 ◽  
Author(s):  
Peter C. Kalverla ◽  
Stef Smeets ◽  
Niels Drost ◽  
Bouwe Andela ◽  
Fakhereh Alidoost ◽  
...  
Keyword(s):  
Ease Of Use ◽  
System Model ◽  
Limiting Factor ◽  
Scientific Quality ◽  
Evaluation Tool ◽  
Web Browser ◽  
Playing Field ◽  
The Earth ◽  
Command Line Tool ◽  
Shared Infrastructure

<p>Ease of use can easily become a limiting factor to scientific quality and progress. In order to verify and build upon previous results, the ability to effortlessly access and process increasing data volumes is crucial.</p><p>To level the playing field for all researchers, a shared infrastructure had to be developed. In Europe, this effort is coordinated mainly through the IS-ENES projects. The current infrastructure provides access to the data as well as compute resources. This leaves the tools to easily work with the data as the main obstacle for a smooth scientific process. Interestingly, not the scarcity of tools, but rather their abundance can lead to diverging workflows that hamper reproducibility.</p><p>The Earth System Model eValuation Tool (ESMValTool) was originally developed as a command line tool for routine evaluation of important analytics workflows. This tool encourages some degree of standardization by factoring out common operations, while allowing for custom analytics of the pre-processed data. All scripts are bundled with the tool. Over time this has grown into a library of so-called ‘recipes’.</p><p>In the EUCP project, we are now developing a Python API for the ESMValTool. This allows for interactive exploration, modification, and execution of existing recipes, as well as creation of new analytics. Concomitantly, partners in IS-ENES3 are making their infrastructure accessible through JupyterLab. Through the combination of these technologies, researchers can easily access the data and compute, but also the workflows or methods used by their colleagues - all through the web browser. During the vEGU, we will show how this extended infrastructure can be used to easily reproduce, and build upon, previous results.</p>

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