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Mol Cell Proteomics. 2016 Aug;15(8):2829-38. doi: 10.1074/mcp.O115.056879. Epub 2016 May 26.

PyQuant: A Versatile Framework for Analysis of Quantitative Mass Spectrometry Data.

Author information

1
From the ‡McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205; §§Ginkgo Bioworks, 27 Drydock Ave, Boston, MA 02210, USA pandey@jhmi.edu chris.mit7@gmail.com.
2
From the ‡McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205; ‖Department of Applied Chemistry, Kyung Hee University, Yongin, Gyeonggi, South Korea;
3
From the ‡McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205;
4
From the ‡McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205; §Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205; pandey@jhmi.edu chris.mit7@gmail.com.

Abstract

Quantitative mass spectrometry data necessitates an analytical pipeline that captures the accuracy and comprehensiveness of the experiments. Currently, data analysis is often coupled to specific software packages, which restricts the analysis to a given workflow and precludes a more thorough characterization of the data by other complementary tools. To address this, we have developed PyQuant, a cross-platform mass spectrometry data quantification application that is compatible with existing frameworks and can be used as a stand-alone quantification tool. PyQuant supports most types of quantitative mass spectrometry data including SILAC, NeuCode, (15)N, (13)C, or (18)O and chemical methods such as iTRAQ or TMT and provides the option of adding custom labeling strategies. In addition, PyQuant can perform specialized analyses such as quantifying isotopically labeled samples where the label has been metabolized into other amino acids and targeted quantification of selected ions independent of spectral assignment. PyQuant is capable of quantifying search results from popular proteomic frameworks such as MaxQuant, Proteome Discoverer, and the Trans-Proteomic Pipeline in addition to several standalone search engines. We have found that PyQuant routinely quantifies a greater proportion of spectral assignments, with increases ranging from 25-45% in this study. Finally, PyQuant is capable of complementing spectral assignments between replicates to quantify ions missed because of lack of MS/MS fragmentation or that were omitted because of issues such as spectra quality or false discovery rates. This results in an increase of biologically useful data available for interpretation. In summary, PyQuant is a flexible mass spectrometry data quantification platform that is capable of interfacing with a variety of existing formats and is highly customizable, which permits easy configuration for custom analysis.

PMID:
27231314
PMCID:
PMC4974355
DOI:
10.1074/mcp.O115.056879
[Indexed for MEDLINE]
Free PMC Article

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