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Data Mining Techniques for the Life Sciences pp 533–547Cite as

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics

Part of the Methods in Molecular Biology book series (MIMB,volume 1415)

Abstract

In modern plant biology, progress is increasingly defined by the scientists’ ability to gather and analyze data sets of high volume and complexity, otherwise known as “big data”. Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Key words

  • Big data
  • Genomics
  • Next-generation sequencing
  • Proteomics
  • Mass spectrometry
  • Databases
  • iPlant

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Acknowledgements

This work was supported by the National Science Foundation (project IOS-1025642 to S.C.P.) and by CNCSIS-UEFISCDI (project PN-II-PT-PCCA-2011-3.1-1350 to G.V.P.). C.N. was supported by the iPlant Collaborative grant DBI-0735191.

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Authors and Affiliations

  1. National Institute for Laser, Plasma & Radiation Physics, Str. Atomistilor, Nr. 409, Magurele, 077125, Bucharest, Romania

    George V. Popescu

  2. Cold Spring Harbor Laboratories, 1 Bungtown Street, Cold Spring Harbor, NY, 11731, USA

    Christos Noutsos

  3. Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, 411 Dorman Hall, PO Box 9655, Mississippi State, MS, 39762, USA

    Sorina C. Popescu

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  1. George V. Popescu
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  2. Christos Noutsos
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  3. Sorina C. Popescu
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Correspondence to George V. Popescu .

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Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Popescu, G.V., Noutsos, C., Popescu, S.C. (2016). Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_27

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