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Combining quantitative proteomics data processing workflows for greater sensitivity


We here describe a normalization method to combine quantitative proteomics data. By merging the output of two popular quantification software packages, we obtained a 20% increase (on average) in the number of quantified human proteins without suffering from a loss of quality. Our integrative workflow is freely available through our user-friendly, open-source Rover software (

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Figure 1: Analysis of different quantification workflows for dataset 1, experiment A.
Figure 2: Quality control of the combined results from multiple data-processing workflows for dataset 1, experiment A.


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C.V.H. is supported by a grant of the Research Foundation–Flanders (project 3G003908). We thank B. Ghesquière, F. Impens and E. Timmerman for providing prepublication access during algorithm development to their now published data. We acknowledge the support of Ghent University (Multidisciplinary Research Partnership “Bioinformatics: from nucleotides to networks”) and EU 7th Framework Programme (contract 262067-PRIME-XS). K.G. and J.V. acknowledge funding from the Fund for Scientific Research–Flanders (Belgium) (project G.0042.07), the Concerted Research Actions (project BOF07/GOA/012) from Ghent University and the Interuniversity Attraction Poles (IUAP06).

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Authors and Affiliations



N.C. developed the combination algorithm and wrote the first draft of the manuscript. C.V.H. contributed to algorithm development and to manuscript writing. S.D. contributed to algorithm development and manuscript writing. A.S. assisted with data processing and manuscript writing. J.V. and K.G. supervised part of the work and contributed to manuscript writing. L.M. supervised the work, contributed to algorithm development and wrote the manuscript.

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Correspondence to Lennart Martens.

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The authors declare no competing financial interests.

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Supplementary Figures 1–3, Supplementary Table 1–2, Supplementary Data (PDF 11502 kb)

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Colaert, N., Van Huele, C., Degroeve, S. et al. Combining quantitative proteomics data processing workflows for greater sensitivity. Nat Methods 8, 481–483 (2011).

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