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An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data

Mueller, Lukas N., Brusniak, Mi-Youn, Mani, D. R., Aebersold, Ruedi·2008·Journal of Proteome Research

Abstract

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.

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Metadata

Authors
Mueller, L. N., Brusniak, M.-Y., Mani, D. R., & Aebersold, R.
Venue
Journal of Proteome Research
Published
2008-01-01
Volume
7
Issue
1
Pages
51-61
ISSN
  • 1535-3893 (ISSN-L)
  • 1535-3907
OpenAlex
W2038249420
Cited by
449
References
5
FWCI
31.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. https://app.cochat.ai/papers/10.1021/pr700758r
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Mueller, L. N., Brusniak, M.-Y., Mani, D. R., & Aebersold, R. (2008). An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. Journal of Proteome Research. https://doi.org/10.1021/pr700758r

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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