DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data
Open accessAbstract
Summary: Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis. Availability and implementation: DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.
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Differential abundance analysis was conducted using the DEP2 package, which utilizes limma and fdrtools.
“To determine differential abundance, statistical analysis was conducted using the DEP2 package85 which utilizes limma and fdrtools.”
From APOE genotypes differentially remodel the astrocytic lipid droplet proteome to shape lipid droplet dynamics (2026) · cites this paper as [85]
The Results section describes DEP2 as a differential-analysis toolkit and says its classical pipeline uses a moderated t-test from limma. Our copy does not mention fdrtools, so it does not support that part of the description. (LLM, conf=0.88)
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- Authors
- Feng, Z., Fang, P., Zheng, H., & Zhang, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Henan Medical University (CN)
- Corresponding author
- Zhang, Xiaofei
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-08-01
- Volume
- 39
- Issue
- 8
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC10466079
- OpenAlex
- W4386156770
- Cited by
- 83
- References
- 1
- Citations by year
- 2026: 50 · 2025: 25 · 2024: 8
- FWCI
- 7.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Guangdong Pearl River Talents Program (2019QN01Y051)
- National Key R&D Program of China (2021YFA1100401, 2021YFA1101304)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- quantitative proteomics, differentially expressed proteins, functional enrichment, Shiny app
- MeSH terms
- Data Analysis, Quality Control, Mass Spectrometry, Proteome, Proteomics
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