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

• Published in: Bioinformatics Vol. 31; no. 4; pp. 555 - 562
• Main Authors: Teleman, Johan, Röst, Hannes L, Rosenberger, George, Schmitt, Uwe, Malmström, Lars, Malmström, Johan, Levander, Fredrik
• Format: Journal Article
• Language: English
• Published: England 15.02.2015
• Subjects: Algorithms; Bacterial Proteins - chemistry; Bacterial Proteins - metabolism; Bioinformatics; Clinical Medicine; Data Mining - methods; Databases, Protein; Extraction; Humans; Infectious Medicine; Infektionsmedicin; Klinisk medicin; Licenses; Markov Chains; Mass spectra; Medical and Health Sciences; Medicin och hälsovetenskap; Peptide Fragments - analysis; Peptides; Proteomics - methods; Recall; Software; Source code; Streptococcus pyogenes - metabolism; Tandem Mass Spectrometry - methods
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btu686

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.