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Abstract
Data processing forms an integral part of biomarker discovery and contributes significantly to the ultimate result. To compare and evaluate various publicly available open source label-free data processing workflows, we developed msCompare, a modular framework that allows the arbitrary combination of different feature detection/quantification and alignment/matching algorithms in conjunction with a novel scoring method to evaluate their overall performance. We used msCompare to assess the performance of workflows built from modules of publicly available data processing packages such as SuperHirn, OpenMS, and MZmine and our in-house developed modules on peptide-spiked urine and trypsin-digested cerebrospinal fluid (CSF) samples. We found that the quality of results varied greatly among workflows, and interestingly, heterogeneous combinations of algorithms often performed better than the homogenous workflows. Our scoring method showed that the union of feature matrices of different workflows outperformed the original homogenous workflows in some cases. msCompare is open source software (https://trac.nbic.nl/mscompare), and we provide a web-based data processing service for our framework by integration into the Galaxy server of the Netherlands Bioinformatics Center (http://galaxy.nbic.nl/galaxy) to allow scientists to determine which combination of modules provides the most accurate processing for their particular LC-MS data sets.
Highlights
More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverneamendment
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Assignment of features in the spiked human urine dataset that are derived from spiked peptides A list of features, that are derived from the added peptides (CA digest and synthetic peptides), was assigned based on 4 analyses of samples containing only peptides used for spiking at a 100-fold dilution of the stock solution and analyzing the resulting data with OpenMS, MZmine and SuperHirn
Summary
Assignment of features in the spiked human urine dataset that are derived from spiked peptides A list of features, that are derived from the added peptides (CA digest and synthetic peptides), was assigned based on 4 analyses of samples containing only peptides used for spiking at a 100-fold dilution of the stock solution and analyzing the resulting data with OpenMS, MZmine and SuperHirn. The resulting data were analysed with OpenMS, MZmine and SuperHirn homogenous workflows, and a feature was considered to belong to one of the spiked peptides if it was detected by one of the workflows in at least two separate chromatograms.
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