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

Citation Teleman, Johan; Rost, Hannes; Rosenberger, George; Schmitt, Uwe; Malmstrom, Lars; Malmstrom, Johan; Levander, Fredrik; DIANA - algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics (2014), : .
Abstract MOTIVATION : Data 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. RESULTS : We 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 http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet).
Synopsis We describe a new way of analysing SWATH-MS data. We also describe pyProphet.
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