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Biochim Biophys Acta. 2014 Jan;1844(1 Pt A):42-51. doi: 10.1016/j.bbapap.2013.04.032. Epub 2013 May 18.

Using R and Bioconductor for proteomics data analysis.

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Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK. Electronic address:


This review presents how R, the popular statistical environment and programming language, can be used in the frame of proteomics data analysis. A short introduction to R is given, with special emphasis on some of the features that make R and its add-on packages premium software for sound and reproducible data analysis. The reader is also advised on how to find relevant R software for proteomics. Several use cases are then presented, illustrating data input/output, quality control, quantitative proteomics and data analysis. Detailed code and additional links to extensive documentation are available in the freely available companion package RforProteomics. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.


Data analysis statistics; Mass spectrometry; Quality control; Quantitative proteomics; Software

[Indexed for MEDLINE]

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