The DOI matches its Crossref record; 191 of the 192 references it cites were found in a registry or an index.
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Open accessAbstract
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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“For example, mRNA abundance from transcriptomics alone is insufficient to infer protein abundance [Chen et al., 2020; Reimegård et al., 2021]”
From Cell signaling pathways discovery from multi-modal data (2025) · cites this paper as Chen et al., 2020
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Proteomes Are of Proteoforms: Embracing the Complexity
Carbonara, K., Andonovski, M., & Coorssen, J. R. (2021) · Proteomes · doi:10.3390/proteomes9030038 · Cited by 121
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Metadata
- Authors
- Chen, C., Hou, J., Tanner, J. J., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Saint Louis University (US)
- Corresponding author
- 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)
- 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
- 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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