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

Nucleic Acids Research
Shisheng WangYansheng Liu

Abstract

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 in...Continue Reading

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Citations

Jun 2, 2021·Journal of Proteome Research·Janine EgertClemens Kreutz
Sep 2, 2021·Expert Review of Proteomics·Jacqueline S Gerritsen, Forest M White

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Datasets Mentioned

BETA
PXD017476

Software Mentioned

Impseqrob
MinDet
motifeR
Seq
GMS
Spectronaut based
mathop
QR
Google Chrome
NAguideR

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