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

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 access
Chen, Chen, Hou, Jie, Tanner, John J., Cheng, Jianlin·2020·International Journal of Molecular Sciences
DOI 10.3390/ijms21082873PMC PMC7216093
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/ijms21082873

Cites

191 of 191 references verified0 citation errors · 0 not checkable

Statistics

Figures

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.
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.
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.
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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 192 references

Research works printed but not linked: 1

  1. Fischer, B., Roth, V., Grossmann, J., Baginsky, S., Gruissem, W., Roos, F., Widmayer, P., and Buhmann, J.M. (2005). A hidden markov model for de novo peptide sequencing. Advances in Neural Information Processing Systems, MIT Press. · DOI

Cited by

Sorted by citations

Items 101–200 of 283

Related

See all

Metadata

Authors
Chen, C., Hou, J., Tanner, J. J., & Cheng, J.
Affiliations
  • University of Missouri (US)
  • Saint Louis University (US)
Corresponding author
Cheng, Jianlin
ORCID
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review