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Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines

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Protein Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1558))

Abstract

Data-independent acquisition mass spectrometry (DIA-MS) strategies and applications provide unique advantages for qualitative and quantitative proteome probing of a biological sample allowing constant sensitivity and reproducibility across large sample sets. These advantages in LC-MS/MS are being realized in fundamental research laboratories and for clinical research applications. However, the ability to translate high-throughput raw LC-MS/MS proteomic data into biological knowledge is a complex and difficult task requiring the use of many algorithms and tools for which there is no widely accepted standard and best practices are slowly being implemented. Today a single tool or approach inherently fails to capture the full interpretation that proteomics uniquely supplies, including the dynamics of quickly reversible chemically modified states of proteins, irreversible amino acid modifications, signaling truncation events, and, finally, determining the presence of protein from allele-specific transcripts. This chapter highlights key steps and publicly available algorithms required to translate DIA-MS data into knowledge.

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Author information

Authors and Affiliations

  1. Nemours Alfred I. DuPont Hospital for Children, 1701 Rockland Road, Wilmington, DE, 19803, USA

    Erin L. Crowgey

  2. Advanced Clinical BioSystems Research Institute, Cedars Sinai Medical Center, Heart Institute, Los Angeles, CA, 90048, USA

    Andrea Matlock, Vidya Venkatraman, Justyna Fert-Bober & Jennifer E. Van Eyk

Authors
  1. Erin L. Crowgey
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  2. Andrea Matlock
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  3. Vidya Venkatraman
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  4. Justyna Fert-Bober
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  5. Jennifer E. Van Eyk
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Corresponding author

Correspondence to Erin L. Crowgey .

Editor information

Editors and Affiliations

  1. Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, USA

    Cathy H. Wu

  2. Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, USA

    Cecilia N. Arighi

  3. Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA

    Karen E. Ross

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Crowgey, E.L., Matlock, A., Venkatraman, V., Fert-Bober, J., Van Eyk, J.E. (2017). Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines. In: Wu, C., Arighi, C., Ross, K. (eds) Protein Bioinformatics. Methods in Molecular Biology, vol 1558. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6783-4_19

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  • DOI: https://doi.org/10.1007/978-1-4939-6783-4_19

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6781-0

  • Online ISBN: 978-1-4939-6783-4

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