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

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


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Bacterial Proteins / chemistry
  • Bacterial Proteins / metabolism*
  • Data Mining / methods*
  • Databases, Protein*
  • Humans
  • Markov Chains
  • Peptide Fragments / analysis*
  • Proteomics / methods*
  • Software*
  • Streptococcus pyogenes / metabolism
  • Tandem Mass Spectrometry / methods*


  • Bacterial Proteins
  • Peptide Fragments