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International Conference on Database and Expert Systems Applications

DEXA 2018: Database and Expert Systems Applications pp 168–178Cite as

Protein Identification as a Suitable Application for Fast Data Architecture

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Part of the book series: Communications in Computer and Information Science ((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.

Supported by de.NBI.

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Notes

  1. 1.

    The mission of UniProt is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequence and functional information [2].

  2. 2.

    Mutation in the protein sequence change an amono acid.

  3. 3.

    Spark, Mesos, Akka, Cassandra and Kafka as streaming pipeline for real time data processing [13].

  4. 4.

    CPU 48x Intel Xeon E5-2650 v4; 512 GB RAM.

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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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Authors and Affiliations

  1. Working Group Databases and Software Engineering, University of Magdeburg, 39104, Magdeburg, Germany

    Roman Zoun, Gabriel Campero Durand, David Broneske, Wolfram Fenske & Gunter Saake

  2. Chair of Bioprocess Engineering, University of Magdeburg, 39104, Magdeburg, Germany

    Kay Schallert, Robert Heyer & Dirk Benndorf

  3. Accenture GmbH, Kronberg im Taunus, Germany

    Apoorva Patrikar

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  1. Roman Zoun
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  9. Gunter Saake
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Correspondence to Roman Zoun .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

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Zoun, R. et al. (2018). Protein Identification as a Suitable Application for Fast Data Architecture. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-99133-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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