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qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Walzer M, Pernas LE, Nasso S, Bittremieux W, Nahnsen S, Kelchtermans P, Pichler P, van den Toorn HW, Staes A, Vandenbussche J, Mazanek M, Taus T, Scheltema RA, Kelstrup CD, Gatto L, van Breukelen B, Aiche S, Valkenborg D, Laukens K, Lilley KS, Olsen JV, Heck AJ, Mechtler K, Aebersold R, Gevaert K, Vizcaíno JA, Hermjakob H, Kohlbacher O, Martens L - Mol. Cell Proteomics (2014)

Bottom Line: We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative).In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema.We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities.

View Article: PubMed Central - PubMed

Affiliation: From the ‡Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Dept. of Computer Science, University of Tuebingen, Germany;

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Simple QC workflow as implemented in KNIME. An input mzML file is first preprocessed (feature finding/identification with standard parameters), allowing the QCCalculator to subsequently create a basic qcML file. On top of this, the ID ratio (recorded versus identified MS2 on M/Z over RT), the mass accuracy (ppm error histogram), the fractional mass (experimentally recorded versus theoretically expected on fractional mass over nominal mass), and the TIC are all plotted. Finally, verbose or redundant attachments, as source data for generated plots, are removed for a slim report file. More examples can be found in the supplementary information.
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Figure 3: Simple QC workflow as implemented in KNIME. An input mzML file is first preprocessed (feature finding/identification with standard parameters), allowing the QCCalculator to subsequently create a basic qcML file. On top of this, the ID ratio (recorded versus identified MS2 on M/Z over RT), the mass accuracy (ppm error histogram), the fractional mass (experimentally recorded versus theoretically expected on fractional mass over nominal mass), and the TIC are all plotted. Finally, verbose or redundant attachments, as source data for generated plots, are removed for a slim report file. More examples can be found in the supplementary information.

Mentions: The OpenMS package is available at http://openms.de/downloads. An easy to follow “getting started” guide can be found in the supplementary information. All these tools are also available from within the workflow management and data analytics system KNIME (29) through the OpenMS community nodes (30). Fig. 3 depicts an example of such a QC workflow as implemented in KNIME. The KNIME file for this workflow can be found in the Supplementary Material, along with the resulting qcML file, a pdf report and a second, more detailed QC workflow example and description. Furthermore included in the Supplementary Information is a getting started guide for qcML with OpenMS and KNIME.


qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Walzer M, Pernas LE, Nasso S, Bittremieux W, Nahnsen S, Kelchtermans P, Pichler P, van den Toorn HW, Staes A, Vandenbussche J, Mazanek M, Taus T, Scheltema RA, Kelstrup CD, Gatto L, van Breukelen B, Aiche S, Valkenborg D, Laukens K, Lilley KS, Olsen JV, Heck AJ, Mechtler K, Aebersold R, Gevaert K, Vizcaíno JA, Hermjakob H, Kohlbacher O, Martens L - Mol. Cell Proteomics (2014)

Simple QC workflow as implemented in KNIME. An input mzML file is first preprocessed (feature finding/identification with standard parameters), allowing the QCCalculator to subsequently create a basic qcML file. On top of this, the ID ratio (recorded versus identified MS2 on M/Z over RT), the mass accuracy (ppm error histogram), the fractional mass (experimentally recorded versus theoretically expected on fractional mass over nominal mass), and the TIC are all plotted. Finally, verbose or redundant attachments, as source data for generated plots, are removed for a slim report file. More examples can be found in the supplementary information.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4125725&req=5

Figure 3: Simple QC workflow as implemented in KNIME. An input mzML file is first preprocessed (feature finding/identification with standard parameters), allowing the QCCalculator to subsequently create a basic qcML file. On top of this, the ID ratio (recorded versus identified MS2 on M/Z over RT), the mass accuracy (ppm error histogram), the fractional mass (experimentally recorded versus theoretically expected on fractional mass over nominal mass), and the TIC are all plotted. Finally, verbose or redundant attachments, as source data for generated plots, are removed for a slim report file. More examples can be found in the supplementary information.
Mentions: The OpenMS package is available at http://openms.de/downloads. An easy to follow “getting started” guide can be found in the supplementary information. All these tools are also available from within the workflow management and data analytics system KNIME (29) through the OpenMS community nodes (30). Fig. 3 depicts an example of such a QC workflow as implemented in KNIME. The KNIME file for this workflow can be found in the Supplementary Material, along with the resulting qcML file, a pdf report and a second, more detailed QC workflow example and description. Furthermore included in the Supplementary Information is a getting started guide for qcML with OpenMS and KNIME.

Bottom Line: We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative).In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema.We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities.

View Article: PubMed Central - PubMed

Affiliation: From the ‡Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Dept. of Computer Science, University of Tuebingen, Germany;

Show MeSH
Related in: MedlinePlus