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Open AccessReview

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

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Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
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Department of Computer Science, Saint Louis University, St. Louis, MO 63103, USA
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Program in Bioinformatics & Computational Biology, Saint Louis University, St. Louis, MO 63103, USA
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Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(8), 2873; https://doi.org/10.3390/ijms21082873
Received: 18 March 2020 / Revised: 16 April 2020 / Accepted: 18 April 2020 / Published: 20 April 2020
(This article belongs to the Section Biochemistry)
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks. View Full-Text
Keywords: bioinformatics analysis; computational proteomics; machine learning bioinformatics analysis; computational proteomics; machine learning
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MDPI and ACS Style

Chen, C.; Hou, J.; Tanner, J.J.; Cheng, J. Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. Int. J. Mol. Sci. 2020, 21, 2873.

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