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Challenges in Computational Analysis of Mass Spectrometry Data for Proteomics

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Abstract

Mass spectrometry is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification and quantification, and post translational modification characterization in proteomics research. Both the size and the complexity of the data produced by this experimental technique impose great computational challenges in the data analysis. This article reviews some of these challenges and serves as an entry point for those who want to study the area in general.

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  1. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    Bin Ma

  2. Dingsheng Technologies, Beijing, 100085, China

    Bin Ma

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Correspondence to Bin Ma.

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This work is supported by the National High-Tech Research and Development 863 Program of China under Grant No. 2008AA02Z313, NSERC RGPIN under Grant No. 238748-2006, and a start up grant at University of Waterloo.

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Ma, B. Challenges in Computational Analysis of Mass Spectrometry Data for Proteomics. J. Comput. Sci. Technol. 25, 107–123 (2010). https://doi.org/10.1007/s11390-010-9309-1

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