Journal
BIOINFORMATICS
Volume 26, Issue 12, Pages 1574-1575Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq171
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Funding
- National Institutes of Health [HG004537]
- Canadian Institutes of Health Research
- Diabetes and Endocrinology Research Center
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Identifying biologically significant changes in protein abundance between two conditions is a key issue when analyzing proteomic data. One widely used approach centers on spectral counting, a label-free method that sums all the tandem mass spectra for a protein observed in an analysis. To assess the significance of the results, we recently combined the t-test and G-test, with random permutation analysis, and we validated this approach biochemically. To automate the statistical method, we developed PepC, a software program that balances the trade-off between the number of differentially expressed proteins identified and the false discovery rate. This tool can be applied to a wide range of proteomic datasets, making data analysis rapid, reproducible and easily interpretable by proteomics specialists and non-specialists alike.
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