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A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes

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Proteome Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1549))

Abstract

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one’s own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

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Authors and Affiliations

  1. Department of Chemistry and Biomolecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia

    Mohammad Tawhidul Islam, Abidali Mohamedali, Ishmam Nawar & Shoba Ranganathan

  2. Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia

    Abidali Mohamedali, Seong Beom Ahn & Mark S. Baker

Authors
  1. Mohammad Tawhidul Islam
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  2. Abidali Mohamedali
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  3. Seong Beom Ahn
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  4. Ishmam Nawar
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  5. Mark S. Baker
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  6. Shoba Ranganathan
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Corresponding author

Correspondence to Shoba Ranganathan .

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Editors and Affiliations

  1. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Shivakumar Keerthikumar

  2. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Suresh Mathivanan

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Islam, M.T., Mohamedali, A., Ahn, S.B., Nawar, I., Baker, M.S., Ranganathan, S. (2017). A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes. In: Keerthikumar, S., Mathivanan, S. (eds) Proteome Bioinformatics. Methods in Molecular Biology, vol 1549. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6740-7_13

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  • DOI: https://doi.org/10.1007/978-1-4939-6740-7_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6738-4

  • Online ISBN: 978-1-4939-6740-7

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