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  Benchmarking differential expression, imputation and quantification methods for proteomics data

Lin, M.-H., Wu, P.-S., Wong, T.-H., Lin, I.-Y., Lin, J., Cox, J., et al. (2022). Benchmarking differential expression, imputation and quantification methods for proteomics data. Briefings in Bioinformatics, 23(3): bbac138. doi:10.1093/bib/bbac138.

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 Creators:
Lin, Miao-Hsia1, Author
Wu, Pei-Shan1, Author
Wong, Tzu-Hsuan1, Author
Lin, I-Ying1, Author
Lin, Johnathan1, Author
Cox, Jürgen2, Author           
Yu, Sung-Huan1, Author
Affiliations:
1external, ou_persistent22              
2Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              

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Free keywords: QUANTITATIVE PROTEOMICS; COMPUTATIONAL PLATFORM; EXTRACTION; LABELBiochemistry & Molecular Biology; Mathematical & Computational Biology; differential expression; proteomics; benchmark data; imputation; matching between runs;
 Abstract: Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray-and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

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Language(s): eng - English
 Dates: 2022
 Publication Status: Published online
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000784515200001
DOI: 10.1093/bib/bbac138
 Degree: -

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Title: Briefings in Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 23 (3) Sequence Number: bbac138 Start / End Page: - Identifier: ISSN: 1467-5463
CoNE: https://pure.mpg.de/cone/journals/resource/974392606063