msCompare: A Framework for Quantitative Analysis of Label-free LC-MS Data for Comparative Candidate Biomarker Studies
Berend Hoekmana b c, Rainer Breitling Ac d e, Frank Suitsf, Rainer Bischoffb c, Peter L. Horvatovichb c✉
- aDepartment of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands
- bAnalytical Biochemistry, Department of Pharmacy, University of Groningen, 9713 AV Groningen, The Netherlands
- cNetherlands Bioinformatics Centre, 6525 GA Nijmegen, The Netherlands
- dCollege of Medical, Veterinary and Life Sciences, Institute of Molecular, Cell and Systems Biology, University of Glasgow, G12 8QQ Glasgow, United Kingdom
- eGroningen Bioinformatics Centre, University of Groningen, 9747 AG Groningen, The Netherlands
- fIBM T. J. Watson Research Centre, Yorktown Heights, New York 10598
- ✉Corresponding author
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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.