High‐precision iRT prediction in the targeted analysis of data‐independent acquisition and its impact on identification and quantitation. Issue 15 (28th June 2016)

• Record Type: Journal Article
• Authors: Bruderer, Roland; Bernhardt, Oliver M.; Gandhi, Tejas; Reiter, Lukas
• Other Names: Aebersold Ruedi guestEditor.; Bensimon Ariel guestEditor.; Collins Ben C. guestEditor.; Ludwig Christina guestEditor.; Sabido Eduard guestEditor.
• Is Part Of: Proteomics. Volume 16:Issue 15/16(2016)
• Page Start: 2246
• Page End: 2256
• Publication Date: 2016-06-28
• Subjects: Bioinformatics -- Chromatography -- Data‐independent acquisition -- iRT -- Retention time alignment -- Retention time normalization -- Retention time prediction; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201500488
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : Targeted analysis of data‐independent acquisition (DIA) data is a powerful mass spectrometric approach for comprehensive, reproducible and precise proteome quantitation. It requires a spectral library, which contains for all considered peptide precursor ions empirically determined fragment ion intensities and their predicted retention time (RT). RTs, however, are not comparable on an absolute scale, especially if heterogeneous measurements are combined. Here, we present a method for high‐precision prediction of RT, which significantly improves the quality of targeted DIA analysis compared to in silico RT prediction and the state of the art indexed retention time (iRT) normalization approach. We describe a high‐precision normalized RT algorithm, which is implemented in the Spectronaut software. We, furthermore, investigate the influence of nine different experimental factors, such as chromatographic mobile and stationary phase, on iRT precision. In summary, we show that using targeted analysis of DIA data with high‐precision iRT significantly increases sensitivity and data quality. The iRT values are generally transferable across a wide range of experimental conditions. Best results, however, are achieved if library generation and analytical measurements are performed on the same system.