Skip to main content

Computational Core for Plant Metabolomics: A Case for Interdisciplinary Research

  • Conference paper
  • First Online:
Book cover Big Data Analytics (BDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10721))

Included in the following conference series:

Abstract

Computational Core for Plant Metabolomics (CCPM) is a web-based collaborative platform for researchers in the field of metabol-omics to store, analyze and share their data. Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using chromatography, mass spectrometry and NMR. There is abundant volume and variety in the data, with velocity being unpredictable. An interdisciplinary engagement such as this faces significant non-technical challenges solvable using a balanced approach to software management in a university setting to create an environment promoting collaborative contributions. In this paper we report on our experiences, challenges and methods in delivering a usable solution. CCPM provides a secure data repository with advanced tools for analysis including preprocessing, pretreatment, data filtration, statistical analysis, and pathway analysis functions; and also visualization, integration and sharing of data. As all users are not equally IT-savvy, it is essential that the user interface is robust, friendly and interactive where the user can submit and control various tasks running simultaneously without stopping/interfering with other tasks. In each stage of its pipeline architecture, users are also allowed to upload external data that has been partially processed till the previous stage in other platforms. Use of open source softwares for development makes the maintenance and development of our modules easier than the others which depend on proprietary softwares.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Computational core for plant metabolomics: Jnu platform. http://metabolomics.jnu.ac.in/lims. Accessed 10 Oct 2017

  2. Computational core for plant metabolomics: Main platform. http://metabolomics.iiit.ac.in/user/login. Accessed 10 Oct 2017

  3. GitLab. https://about.gitlab.com/. Accessed 10 Oct 2017

  4. gplots: Various R programming tools for plotting data. https://cran.r-project.org/web/packages/gplots/index.html. Accessed 10 Oct 2017

  5. Metabolome. https://en.wikipedia.org/wiki/Metabolome. Accessed 10 Oct 2017

  6. RNetCDF: Interface to NetCDF datasets. https://cran.r-project.org/web/packages/RNetCDF/index.html. Accessed 10 Oct 2017

  7. Mind meld. Nature Editorial, 525(7569), September 2015

    Google Scholar 

  8. Biswas, A., Mynampati, K.C., Umashankar, S., et al.: MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation. Bioinformatics 26(20), 2639–2640 (2010)

    Article  Google Scholar 

  9. Fiehn, O., Kristal, B., Ommen, V., et al.: Establishing reporting standards for metabolomic and metabonomic studies: A call for participation. OMICS A J. Integr. Biol. 10(2), 158–163 (2006)

    Article  Google Scholar 

  10. Fiehn, O., Robertson, D., Griffin, J., et al.: The metabolomics standards initiative (MSI). Metabolomics 3(3), 175–178 (2007)

    Article  Google Scholar 

  11. Giacomoni, F., Le Corguille, G., Monsoor, M., et al.: Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31(9), 1493–1495 (2015)

    Article  Google Scholar 

  12. Kanehisa, M., Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000)

    Article  Google Scholar 

  13. Lommen, A.: MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal. Chem. 81(8), 3079–3086 (2009)

    Article  Google Scholar 

  14. Matthews, L., Miller, T.: ASTM protocols for analytical data interchange. JALA J. Assoc. Lab. Autom. 5(5), 60–61 (2000)

    Article  Google Scholar 

  15. Di Pierro, M.: web2py for scientific applications. Comput. Sci. Eng. 13, 64–69 (2011)

    Article  Google Scholar 

  16. Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M.: MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. Bioinformatics 11(395) (2010)

    Google Scholar 

  17. Smith, C.A., Want, E.J., OMaille, G., Abagyan, R., Siuzdak, G.: XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78(3), 779–787 (2006)

    Article  Google Scholar 

  18. Wishart, D.S.: Current progress in computational metabolomics. Brief Bioinform. 8(5), 279–293 (2007)

    Article  Google Scholar 

  19. Xia, J., Psychogios, N., Young, N., Wishart, D.S.: MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37(Web Server issue), W652–W660 (2009)

    Google Scholar 

  20. Xia, J., Sinelnikov, I., Wishart, D.S.: MetATT: A web-based metabolomics tool for analyzing time-series and two-factor datasets. Bioinformatics 27, 2455–2456 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by a research grant from the Department of Biotechnology, Govt. of India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vikram Pudi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pudi, V., Rani, P., Mitra, A., Ghosh, I. (2017). Computational Core for Plant Metabolomics: A Case for Interdisciplinary Research. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72413-3_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72412-6

  • Online ISBN: 978-3-319-72413-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics