We use an integrated benchmarking approach to empirically establish guidelines for data acquisition, statistical approach, and replicate numbers for accurate quantification. We evaluated three workflows for protein- and peptide-level quantitative accuracy: data dependent acquisition (DDA), data independent acquisition (DIA), and chemical labeling via tandem mass tags (TMT). The former two datasets were generated in our lab, so we have published them here.
[doi:10.25345/C5B994]
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: Benchmarking ; Data-Independent Quantitation ; Data-Dependent Quantitation ; Statistics ; Best Practices
Principal Investigators: (in alphabetical order) |
John Denu, University of Wisconsin - Madison, United States |
Submitting User: | ljwright2 |
Dowell JA, Wright LJ, Armstrong EA, Denu JM.
Benchmarking Quantitative Performance in Label-Free Proteomics.
ACS Omega. 2021 Feb 2;6(4):2494-2504. Epub 2021 Jan 20.
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Experimental Design | ||
Conditions:
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Biological Replicates:
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Identification Results | ||
Proteins (Human, Remapped):
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Proteins (Reported):
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Peptides:
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Variant Peptides:
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PSMs:
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Quantification Results | ||
Differential Proteins:
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Quantified Proteins:
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