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. 2016 Mar 4;15(3):809-14.
doi: 10.1021/acs.jproteome.5b00852. Epub 2015 Nov 17.

Testing and Validation of Computational Methods for Mass Spectrometry

Free PMC article

Testing and Validation of Computational Methods for Mass Spectrometry

Laurent Gatto et al. J Proteome Res. .
Free PMC article


High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets ( ) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

Conflict of interest statement


The authors declare no competing financial interest.


Figure 1
Figure 1
Overview of a typical mass spectrometry data analysis pipeline, applied to shotgun proteomics. Most of these steps equally apply to metabolomics experiments. We highlight the flow of information through the pipeline and overlay important notions related to computational method validation discussed in the text.

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