A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Enhanced differential expression statistics for data-independent acquisition proteomics
Tekijät: Tomi Suomi, Laura L. Elo
Kustantaja: NATURE PUBLISHING GROUP
Julkaisuvuosi: 2017
Journal: Scientific Reports
Tietokannassa oleva lehden nimi: SCIENTIFIC REPORTS
Lehden akronyymi: SCI REP-UK
Artikkelin numero: 5869
Vuosikerta: 7
Numero: 1
Sivujen määrä: 8
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-017-05949-y
Verkko-osoite: https://www.nature.com/articles/s41598-017-05949-y
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/25088055
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
