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Bioinformatics. 2015 Feb 15;31(4):555-62. doi: 10.1093/bioinformatics/btu686. Epub 2014 Oct 27.

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

Author information

1
Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
2
Department of Clinical Sciences, Lund University, BMC B14 221 84 Lund, Department of Immunotechnology, Lund University, Medicon Village (Building 406) 223 81 Lund, Sweden, Department of Biology, Institute of Molecular Systems Biology, ITS Scientific IT Services, ETH Zurich and SIT, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.

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 dataset. 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 AND IMPLEMENTATION:

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).

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25348213
DOI:
10.1093/bioinformatics/btu686
[Indexed for MEDLINE]

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