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J Proteomics. 2015 Jan 1;112:156-65. doi: 10.1016/j.jprot.2014.08.017. Epub 2014 Sep 16.

3DSpectra: A 3-dimensional quantification algorithm for LC-MS labeled profile data.

Author information

1
Department of Biology, Institute of Molecular Systems Biology, ETH, Auguste-Piccard-Hof 1, ETH Honggerberg, CH-8093 Zürich, Switzerland; Department of Information Engineering, University of Padova, Via Gradenigo, 6/B, 35131 Padova, Italy. Electronic address: nasso@imsb.biol.ethz.ch.
2
Bioinformatics Group, Institute for Knowledge Discovery, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria.
3
Biocenter, Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6010 Innsbruck, Austria.
4
Department of Information Engineering, University of Padova, Via Gradenigo, 6/B, 35131 Padova, Italy.
5
Research Institute of Molecular Pathology (IMP), Dr. Bohr-Gasse 7, A-1030 Vienna, Austria.

Abstract

Mass spectrometry-based proteomics can generate highly informative datasets, as profile three-dimensional (3D) LC-MS data: LC-MS separates peptides in two dimensions (time, m/z) minimizing their overlap, and profile acquisition enhances quantification. To exploit both data features, we developed 3DSpectra, a 3D approach embedding a statistical method for peptide border recognition. 3DSpectra efficiently accesses profile data by means of mzRTree, and makes use of a priori metadata, provided by search engines, to quantify the identified peptides. An isotopic distribution model, shaped by a bivariate Gaussian Mixture Model (GMM), which includes a noise component, is fitted to the peptide peaks using the expectation-maximization (EM) approach. The EM starting parameters, i.e., the centers and shapes of the Gaussians, are retrieved from the metadata. The borders of the peaks are delimited by the GMM iso-density curves, and noisy or outlying data are discarded from subsequent analysis. The 3DSpectra program was compared to ASAPRatio for a controlled mixture of Isotope-Coded Protein Labels (ICPL) labeled proteins, which were mixed at predefined ratios and acquired in enhanced profile mode, in triplicate. The 3DSpectra software showed significantly higher linearity, quantification accuracy, and precision than did ASAPRatio in this real use case simulation where the true ratios are known, and it also achieved wider peptide coverage and dynamic range.

BIOLOGICAL SIGNIFICANCE:

Quantitative proteomics is pivotal for many systems biology related fields, such as biomarker discovery. The quantification quality provided by the adopted software is crucial for the success of protein differential expression studies. To determine the reliability of a quantitative computational method, we suggest evaluating performance parameters like accuracy and precision of the quantifications, robustness to outliers and proteome coverage. A quantitative comparison of these parameters is highly desirable since it enables to benchmark software performance. We applied this strategy to 3DSpectra, a 3-dimensional approach to spectra analysis for MS1 peptide quantification. It distinguishes peptide peaks from spurious peaks interfering in the survey scan. 3DSpectra was compared to ASAPRatio in terms of quantification quality performance parameters and showed an overall improvement.

KEYWORDS:

3D quantification; Profile LC–MS data quantification; Quantification MATLAB software; Quantitative proteomics

PMID:
25218586
DOI:
10.1016/j.jprot.2014.08.017
[Indexed for MEDLINE]
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