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Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory

(2014) PROTEOMICS. 14(4-5). p.367-377
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.
Keywords
Crowdsourcing, Cloud computing, Distributed computing, Parallelized computing, PROTEIN DATA-BANK, TANDEM MASS-SPECTRA, SPECTROMETRY-BASED PROTEOMICS, BIOMARKER DISCOVERY, PARALLEL TANDEM, DATA SETS, IDENTIFICATION, PIPELINE, ALGORITHMS, LOCALIZATION, Bioinformatics

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Citation

Please use this url to cite or link to this publication:

Chicago
Verheggen, Kenneth, Harald Barsnes, and Lennart Martens. 2014. “Distributed Computing and Data Storage in Proteomics: Many Hands Make Light Work, and a Stronger Memory.” Proteomics 14 (4-5): 367–377.
APA
Verheggen, K., Barsnes, H., & Martens, L. (2014). Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory. PROTEOMICS, 14(4-5), 367–377.
Vancouver
1.
Verheggen K, Barsnes H, Martens L. Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory. PROTEOMICS. 2014;14(4-5):367–77.
MLA
Verheggen, Kenneth, Harald Barsnes, and Lennart Martens. “Distributed Computing and Data Storage in Proteomics: Many Hands Make Light Work, and a Stronger Memory.” PROTEOMICS 14.4-5 (2014): 367–377. Print.
@article{4259603,
  abstract     = {Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.},
  author       = {Verheggen, Kenneth and Barsnes, Harald and Martens, Lennart},
  issn         = {1615-9853},
  journal      = {PROTEOMICS},
  keyword      = {Crowdsourcing,Cloud computing,Distributed computing,Parallelized computing,PROTEIN DATA-BANK,TANDEM MASS-SPECTRA,SPECTROMETRY-BASED PROTEOMICS,BIOMARKER DISCOVERY,PARALLEL TANDEM,DATA SETS,IDENTIFICATION,PIPELINE,ALGORITHMS,LOCALIZATION,Bioinformatics},
  language     = {eng},
  number       = {4-5},
  pages        = {367--377},
  title        = {Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory},
  url          = {http://dx.doi.org/10.1002/pmic.201300288},
  volume       = {14},
  year         = {2014},
}

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