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Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry pp 65–79Cite as

Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data

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Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

The process of identification of peptides from the mass spectra and the constituent proteins in a sample is called protein identification. In the current literature, there exist many proposed approaches for the protein identification problem based on tandem mass spectrometry (MS/MS) data. While there are many two-step protein identification procedures that first identify peptides in a separate process and then use the results in protein identification, in recent years there have been attempts to develop a one-step solution to the problem through simultaneous identification of proteins and peptides in a sample. We briefly introduce the probabilistic and likelihood-based two-step and one-step procedures and report some comparative performances of these procedures for different MS/MS data.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Louisville, Louisville, KY, 40292, USA

    Ryan Gill

  2. Department of Biostatistics, University of Florida, Gainesville, FL, USA

    Susmita Datta (Professor)

Authors
  1. Ryan Gill
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  2. Susmita Datta
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Correspondence to Ryan Gill .

Editor information

Editors and Affiliations

  1. Department of Biostatistics, University of Florida, Gainesville, Florida, USA

    Susmita Datta

  2. Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, RC Leiden, The Netherlands

    Bart J. A. Mertens

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Gill, R., Datta, S. (2017). Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data. In: Datta, S., Mertens, B. (eds) Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-45809-0_4

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