Abstract:
:We describe a new reproducibility-optimization method ROPECA for statistical analysis of proteomics data with a specific focus on the emerging data-independent acquisition (DIA) mass spectrometry technology. ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression. Using a 'gold standard' spike-in data and a hybrid proteome benchmark data we show the competitive performance of ROPECA over conventional protein-based analysis as well as state-of-the-art peptide-based tools especially in DIA data with consistent peptide measurements. Furthermore, we also demonstrate the improved accuracy of our method in clinical studies using proteomics data from a longitudinal human twin study.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Suomi T,Elo LLdoi
10.1038/s41598-017-05949-ysubject
Has Abstractpub_date
2017-07-19 00:00:00pages
5869issue
1issn
2045-2322pii
10.1038/s41598-017-05949-yjournal_volume
7pub_type
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