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Benchmarking and Best Practices for Quantitative Proteomics

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606)

SUBMITTER: John Denu

PROVIDER: MSV000085239 | MassIVE | Mon Apr 06 13:50:00 BST 2020

SECONDARY ACCESSION(S): PXD018408

REPOSITORIES: MassIVE

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Publications

Benchmarking Quantitative Performance in Label-Free Proteomics.

Dowell James A JA Wright Logan J LJ Armstrong Eric A EA Denu John M JM

ACS omega 20210120 4

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses r ...[more]

PMID: 33553868

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