Cited by 283Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic NetworksMass Spectrometry Techniques and Applications

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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

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Chen, Chen, Hou, Jie, Tanner, John J., Cheng, Jianlin·2020·International Journal of Molecular Sciences

DOI 10.3390/ijms21082873PMC PMC7216093

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Abstract

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.

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Figure 1 General workflow of bioinformatics analysis in mass spectrometry-based proteomics. (a) MA-plot from protein differential abundance analysis. X-axis is the log2 transformed fold change and Y-axis is the average protein abundance from replicates. (b) Distribution of protein abundance data before and after normalization. (c) Heatmap for protein abundance with clustering; (d) Protein set enrichment analysis, Y-axis in the above plot shows the ranked list metric, and in the bottom plot shows the running enrichment score. X-axis is the ranked position in protein list. (e) Machine learning-based sample clustering. (f) Illustration of a network inferred from proteomics data. (g) Dimensionality reduction of proteomics expression profile.CC BY · PubMed Central

Figure 2 A typical workflow for protein abundance quantification using Trans-Proteomic Pipeline (TPP). After the raw data files are converted to one of the supported open XML formats, the data are processed with a designated search engine and the search results are validated at spectrum, peptide, and protein level. Meanwhile, the quantification tools will perform the protein quantification with the normalized data. The final output are ready for various downstream analysis.CC BY · PubMed Central

Figure 3 Illustration of enrichment analysis with proteomics data. (a) Gene Ontology (GO) enrichment with Circos plot. The left part of the circle shows the differentially expressed proteins and their significant levels. The right part shows the enriched GO Terms. (b) Pathway enrichment in dot plot. The colors of the dots represent the adjusted p-value and the sizes of the nodes represents the ratio of the proteins that are differentially expressed to the total proteins in the pathway. (c) The running sum score of the gene set enrichment analysis (GSEA). Significant proteins will increase the enrichment score and proteins that are not in the list will decrease the score. (d) Protein enrichment with KEGG pathway database with Pathview. The color of the boxes represents the log2 fold change of the protein abundances.CC BY · PubMed Central

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Authors: Chen, C., Hou, J., Tanner, J. J., & Cheng, J.
Affiliations: University of Missouri (US)
Saint Louis University (US)
Corresponding author: Cheng, Jianlin
ORCID: Chen, Chen
Hou, Jie
Tanner, John J.
Cheng, Jianlin
Venue: International Journal of Molecular Sciences
Publisher: MDPI AG
Published: 2020-04-20
Volume: 21
Issue: 8
Pages: 2873
ISSN: 1422-0067 (ISSN-L)
DOI: 10.3390/ijms21082873
PMC: PMC7216093
OpenAlex: W3016487203
Cited by: 280
References: 192
Citations by year: 2026: 22 · 2025: 58 · 2024: 56 · 2023: 51 · 2022: 54 · 2021: 33 · 2020: 6
FWCI: 4.31
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: International Journal of Molecular Sciences (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institutes of Health (R01GM093123)
National Science Foundation (DBI1759934, IIS1763246)
Topics: Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
Keywords: proteomics data analysis, mass spectrometry, protein identification, protein quantification, bioinformatics software, statistical analysis, machine learning, protein-protein interaction network, signaling networks, quantitative proteomics
MeSH terms: Machine Learning, Data Analysis, Humans, Mass Spectrometry, Computational Biology, Protein Interaction Mapping, Proteomics, Workflow, Protein Interaction Maps

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