El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics

  • Shubhra Agrawal
  • Sahil Kumar
  • Raghav Sehgal
  • Sabu George
  • Rishabh Gupta
  • Surbhi Poddar
  • Abhishek JhaEmail author
  • Swetabh PathakEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1978)


Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.


Mass spectrometry Data processing Metabolomics Bioinformatics Data analysis Metabolic pathways Liquid chromatography-mass spectrometry MAVEN 



El-MAVEN is an extension of MAVEN, and as such we would like to acknowledge the creators of MAVEN. We would also like to acknowledge El-MAVEN contributors and community on GitHub, especially Victor Chubukov, Lance Parsons, and Eugene Melamud for their help in identifying bugs and fixes. The content and figures in this paper were structured, written, edited, and formatted by Chandni Valiathan at Illumetis, LLC.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shubhra Agrawal
    • 1
  • Sahil Kumar
    • 1
  • Raghav Sehgal
    • 1
  • Sabu George
    • 1
  • Rishabh Gupta
    • 1
  • Surbhi Poddar
    • 1
  • Abhishek Jha
    • 1
    Email author
  • Swetabh Pathak
    • 1
    Email author
  1. 1.Elucidata, Inc.CambridgeUSA

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