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Protein Identification as a Suitable Application for Fast Data Architecture

  • Roman ZounEmail author
  • Gabriel Campero Durand
  • Kay Schallert
  • Apoorva Patrikar
  • David Broneske
  • Wolfram Fenske
  • Robert Heyer
  • Dirk Benndorf
  • Gunter Saake
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 903)

Abstract

Metaproteomics is a field of biology research that relies on mass spectrometry to characterize the protein complement of microbiological communities. Since only identified data can be analyzed, identification algorithms such as X!Tandem, OMSSA and Mascot are essential in the domain, to get insights into the biological experimental data. However, protein identification software has been developed for proteomics. Metaproteomics, in contrast, involves large biological communities, gigabytes of experimental data per sample, and greater amounts of comparisons, given the mixed culture of species in the protein database. Furthermore, the file-based nature of current protein identification tools makes them ill-suited for future metaproteomics research. In addition, possible medical use cases of metaproteomics require near real-time identification. From the technology perspective, Fast Data seems promising to increase throughput and performance of protein identification in a metaproteomics workflow. In this paper we analyze the core functions of the established protein identification engine X!Tandem and show that streaming Fast Data architectures are suitable for protein identification. Furthermore, we point out the bottlenecks of the current algorithms and how to remove them with our approach.

Keywords

Fast Data Bioinformatics Metaproteomics Proteomics Cloud computing Protein identification 

Notes

Acknowledgment

The authors sincerely thank Xiao Chen, Sebastian Krieter, Andreas Meister and Marcus Pinnecke for their support and advice. This work is partly funded by the de.NBI Network (031L0103), the DFG (grant no.: SA 465/50-1), the European Regional Development Fund (grant no. 11.000sz00.00.017 114347 0), the German Federal Ministry of Food and Agriculture (grants nos. 22404015) and dedicated to the memory of Mikhail Zoun.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Roman Zoun
    • 1
    Email author
  • Gabriel Campero Durand
    • 1
  • Kay Schallert
    • 2
  • Apoorva Patrikar
    • 3
  • David Broneske
    • 1
  • Wolfram Fenske
    • 1
  • Robert Heyer
    • 2
  • Dirk Benndorf
    • 2
  • Gunter Saake
    • 1
  1. 1.Working Group Databases and Software EngineeringUniversity of MagdeburgMagdeburgGermany
  2. 2.Chair of Bioprocess EngineeringUniversity of MagdeburgMagdeburgGermany
  3. 3.Accenture GmbHKronberg im TaunusGermany

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