NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses.


Journal

Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011

Informations de publication

Date de publication:
20 08 2020
Historique:
accepted: 08 06 2020
revised: 20 04 2020
received: 18 02 2020
pubmed: 12 6 2020
medline: 29 9 2020
entrez: 12 6 2020
Statut: ppublish

Résumé

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

Identifiants

pubmed: 32526036
pii: 5856122
doi: 10.1093/nar/gkaa498
pmc: PMC7641313
doi:

Substances chimiques

Protein Precursors 0
Formaldehyde 1HG84L3525
Nocodazole SH1WY3R615

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e83

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Auteurs

Shisheng Wang (S)

West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.

Wenxue Li (W)

Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.

Liqiang Hu (L)

West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.

Jingqiu Cheng (J)

West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.

Hao Yang (H)

West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.

Yansheng Liu (Y)

Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.
Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.

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