ArticleMolecular & cellular proteomics : MCP2012
msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative candidate biomarker studies.
Article in Molecular & cellular proteomics : MCP, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
- ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.Briefings in bioinformatics · 2020Article
- Computational advances of tumor marker selection and sample classification in cancer proteomics.Computational and structural biotechnology journal · 2020Review
- Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.Molecular & cellular proteomics : MCP · 2019Article
- BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics.Journal of proteome research · 2016Article
- Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective.Biochimica et biophysica acta · 2014Review
- An adaptive alignment algorithm for quality-controlled label-free LC-MS.Molecular & cellular proteomics : MCP · 2013Article
- Tools for label-free peptide quantification.Molecular & cellular proteomics : MCP · 2013Review
- Nestly--a framework for running software with nested parameter choices and aggregating results.Bioinformatics (Oxford, England) · 2013Article
- A critical assessment of feature selection methods for biomarker discovery in clinical proteomics.Molecular & cellular proteomics : MCP · 2013Article
- Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.Journal of proteome research · 2012Article
- Data preprocessing method for liquid chromatography-mass spectrometry based metabolomics.Analytical chemistry · 2012Article
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Authors and funding
5 authors.
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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.
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