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DIANA - algorithmic improvements for analysis of data-independent acquisition MS data.

Type Information
Nr 44 (Research article)
Authors Teleman, Johan; Röst, Hannes; Rosenberger, George; Schmitt, Uwe; Malmström, Lars; Malmström, Johan; Levander, Fredrik
Title DIANA - algorithmic improvements for analysis of data-independent acquisition MS data.
Journal Bioinformatics (2014) 31(4) 555-62
DOI 10.1093/bioinformatics/btu686
Citations 119 citations (journal impact: 4.62)
Abstract MOTIVATIONData independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently targeted data extraction was proposed as a novel analysis strategy for this type of data but it is important to further develop these concepts to provide quality-controlled interference-adjusted and sensitive peptide quantification.RESULTSWe here present the algorithm DIANA and the classifier PyProphet which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate in a complex gold standard data set. Importantly we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms as well as increased numbers of quantified peptide precursors in complex biological samples. Availability DIANA is implemented in scala and python and available as open source Apache 2.0 license or pre-compiled binaries from httpquantitativeproteomics.orgdiana. PyProphet can be installed from PyPi httpspypi.python.orgpypipyprophet.
Synopsis We describe a new way of analysing SWATH-MS data. We also describe pyProphet.