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Maui-VIA: A User-Friendly Software for Visual Identification, Alignment, Correction, and Quantification of Gas Chromatography-Mass Spectrometry Data.

Kuich PH, Hoffmann N, Kempa S - Front Bioeng Biotechnol (2015)

Bottom Line: While there are many bioinformatics tools available to aid the initial steps of the process, their use requires both significant technical expertise and a subsequent manual validation of identifications and alignments if high data quality is desired.It provides functionality for retention index calculation, a targeted library search, the visual annotation, alignment, correction interface, and metabolite quantification, as well as the export of the final datamatrix.The high quality of data produced by Maui-VIA is illustrated by its comparison to data attained manually by an expert using vendor software on a previously published dataset concerning the response of Chlamydomonas reinhardtii to salt stress.

View Article: PubMed Central - PubMed

Affiliation: Integrative Proteomics and Metabolomics, Berlin Institute of Health , Berlin , Germany.

ABSTRACT
A current bottleneck in GC-MS metabolomics is the processing of raw machine data into a final datamatrix that contains the quantities of identified metabolites in each sample. While there are many bioinformatics tools available to aid the initial steps of the process, their use requires both significant technical expertise and a subsequent manual validation of identifications and alignments if high data quality is desired. The manual validation is tedious and time consuming, becoming prohibitively so as sample numbers increase. We have, therefore, developed Maui-VIA, a solution based on a visual interface that allows experts and non-experts to simultaneously and quickly process, inspect, and correct large numbers of GC-MS samples. It allows for the visual inspection of identifications and alignments, facilitating a unique and, due to its visualization and keyboard shortcuts, very fast interaction with the data. Therefore, Maui-Via fills an important niche by (1) providing functionality that optimizes the component of data processing that is currently most labor intensive to save time and (2) lowering the threshold of expertise required to process GC-MS data. Maui-VIA projects are initiated with baseline-corrected raw data, peaklists, and a database of metabolite spectra and retention indices used for identification. It provides functionality for retention index calculation, a targeted library search, the visual annotation, alignment, correction interface, and metabolite quantification, as well as the export of the final datamatrix. The high quality of data produced by Maui-VIA is illustrated by its comparison to data attained manually by an expert using vendor software on a previously published dataset concerning the response of Chlamydomonas reinhardtii to salt stress. In conclusion, Maui-VIA provides the opportunity for fast, confident, and high-quality data processing validation of large numbers of GC-MS samples by non-experts.

No MeSH data available.


Related in: MedlinePlus

The IAA MSView. (A) The MSView is composed of four rows of spectra and buttons that are accessible via keyboard shortcuts. The chart in the middle of the top row displays the mass spectrum of the currently selected library entry. The chart to the left shows the spectrum difference between the library entry and putative assignments in the samples. Putatively assigned metabolites in samples are displayed in the middle chart of the three rows beneath, the charts to their left and right displaying the mass spectra of the peaks to the immediate left and right, respectively. (B,C) The samples can be traversed horizontally by the “Previous” and “Next” buttons of each row as well as vertically by the “Previous Samples” and “Next Samples” buttons in the right panel of the top row. Assignments of peaks in a sample can be removed by the “Set NotFound,” and added by the “Set Found” buttons, respectively. Peaks can be fused with their right or left neighbor using the “Fuse Left” and “Fuse Right” buttons in each row. “Accept Peak” leads to the assignment of the library identifier to the currently selected peak and recalculates the similarity score. (D,E) Peaks containing a combined spectrum of co-eluting metabolites can be duplicated with the “Duplicate” button. For convenience, it is possible to add or remove peak assignments to/from all samples at once with the “Set AllFound” and “Set AllNotFound” buttons, as well as to fuse entire regions of peaks with the “Fuse Region” button. Once identifications and alignments are confirmed, “Accept Group” locks the selection from further editing and marks the identifications to be ready for export.
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Figure 2: The IAA MSView. (A) The MSView is composed of four rows of spectra and buttons that are accessible via keyboard shortcuts. The chart in the middle of the top row displays the mass spectrum of the currently selected library entry. The chart to the left shows the spectrum difference between the library entry and putative assignments in the samples. Putatively assigned metabolites in samples are displayed in the middle chart of the three rows beneath, the charts to their left and right displaying the mass spectra of the peaks to the immediate left and right, respectively. (B,C) The samples can be traversed horizontally by the “Previous” and “Next” buttons of each row as well as vertically by the “Previous Samples” and “Next Samples” buttons in the right panel of the top row. Assignments of peaks in a sample can be removed by the “Set NotFound,” and added by the “Set Found” buttons, respectively. Peaks can be fused with their right or left neighbor using the “Fuse Left” and “Fuse Right” buttons in each row. “Accept Peak” leads to the assignment of the library identifier to the currently selected peak and recalculates the similarity score. (D,E) Peaks containing a combined spectrum of co-eluting metabolites can be duplicated with the “Duplicate” button. For convenience, it is possible to add or remove peak assignments to/from all samples at once with the “Set AllFound” and “Set AllNotFound” buttons, as well as to fuse entire regions of peaks with the “Fuse Region” button. Once identifications and alignments are confirmed, “Accept Group” locks the selection from further editing and marks the identifications to be ready for export.

Mentions: The Maui-VIA IAA interface is composed of two windows, each of which displays one of the two principle pieces of information obtained from GC–MS methods to identify metabolites. The RIView window displays the distribution of peaks in a sample according to their RI and, therefore, visualizes GC retention time (Figure 1). The MSView visualizes peak spectra, therefore representing the information obtained by the MS (Figure 2A). Both the RIView and MSView visualizations are based on the Java charting library JFreeChart (http://www.jfreechart.org/).


Maui-VIA: A User-Friendly Software for Visual Identification, Alignment, Correction, and Quantification of Gas Chromatography-Mass Spectrometry Data.

Kuich PH, Hoffmann N, Kempa S - Front Bioeng Biotechnol (2015)

The IAA MSView. (A) The MSView is composed of four rows of spectra and buttons that are accessible via keyboard shortcuts. The chart in the middle of the top row displays the mass spectrum of the currently selected library entry. The chart to the left shows the spectrum difference between the library entry and putative assignments in the samples. Putatively assigned metabolites in samples are displayed in the middle chart of the three rows beneath, the charts to their left and right displaying the mass spectra of the peaks to the immediate left and right, respectively. (B,C) The samples can be traversed horizontally by the “Previous” and “Next” buttons of each row as well as vertically by the “Previous Samples” and “Next Samples” buttons in the right panel of the top row. Assignments of peaks in a sample can be removed by the “Set NotFound,” and added by the “Set Found” buttons, respectively. Peaks can be fused with their right or left neighbor using the “Fuse Left” and “Fuse Right” buttons in each row. “Accept Peak” leads to the assignment of the library identifier to the currently selected peak and recalculates the similarity score. (D,E) Peaks containing a combined spectrum of co-eluting metabolites can be duplicated with the “Duplicate” button. For convenience, it is possible to add or remove peak assignments to/from all samples at once with the “Set AllFound” and “Set AllNotFound” buttons, as well as to fuse entire regions of peaks with the “Fuse Region” button. Once identifications and alignments are confirmed, “Accept Group” locks the selection from further editing and marks the identifications to be ready for export.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4301187&req=5

Figure 2: The IAA MSView. (A) The MSView is composed of four rows of spectra and buttons that are accessible via keyboard shortcuts. The chart in the middle of the top row displays the mass spectrum of the currently selected library entry. The chart to the left shows the spectrum difference between the library entry and putative assignments in the samples. Putatively assigned metabolites in samples are displayed in the middle chart of the three rows beneath, the charts to their left and right displaying the mass spectra of the peaks to the immediate left and right, respectively. (B,C) The samples can be traversed horizontally by the “Previous” and “Next” buttons of each row as well as vertically by the “Previous Samples” and “Next Samples” buttons in the right panel of the top row. Assignments of peaks in a sample can be removed by the “Set NotFound,” and added by the “Set Found” buttons, respectively. Peaks can be fused with their right or left neighbor using the “Fuse Left” and “Fuse Right” buttons in each row. “Accept Peak” leads to the assignment of the library identifier to the currently selected peak and recalculates the similarity score. (D,E) Peaks containing a combined spectrum of co-eluting metabolites can be duplicated with the “Duplicate” button. For convenience, it is possible to add or remove peak assignments to/from all samples at once with the “Set AllFound” and “Set AllNotFound” buttons, as well as to fuse entire regions of peaks with the “Fuse Region” button. Once identifications and alignments are confirmed, “Accept Group” locks the selection from further editing and marks the identifications to be ready for export.
Mentions: The Maui-VIA IAA interface is composed of two windows, each of which displays one of the two principle pieces of information obtained from GC–MS methods to identify metabolites. The RIView window displays the distribution of peaks in a sample according to their RI and, therefore, visualizes GC retention time (Figure 1). The MSView visualizes peak spectra, therefore representing the information obtained by the MS (Figure 2A). Both the RIView and MSView visualizations are based on the Java charting library JFreeChart (http://www.jfreechart.org/).

Bottom Line: While there are many bioinformatics tools available to aid the initial steps of the process, their use requires both significant technical expertise and a subsequent manual validation of identifications and alignments if high data quality is desired.It provides functionality for retention index calculation, a targeted library search, the visual annotation, alignment, correction interface, and metabolite quantification, as well as the export of the final datamatrix.The high quality of data produced by Maui-VIA is illustrated by its comparison to data attained manually by an expert using vendor software on a previously published dataset concerning the response of Chlamydomonas reinhardtii to salt stress.

View Article: PubMed Central - PubMed

Affiliation: Integrative Proteomics and Metabolomics, Berlin Institute of Health , Berlin , Germany.

ABSTRACT
A current bottleneck in GC-MS metabolomics is the processing of raw machine data into a final datamatrix that contains the quantities of identified metabolites in each sample. While there are many bioinformatics tools available to aid the initial steps of the process, their use requires both significant technical expertise and a subsequent manual validation of identifications and alignments if high data quality is desired. The manual validation is tedious and time consuming, becoming prohibitively so as sample numbers increase. We have, therefore, developed Maui-VIA, a solution based on a visual interface that allows experts and non-experts to simultaneously and quickly process, inspect, and correct large numbers of GC-MS samples. It allows for the visual inspection of identifications and alignments, facilitating a unique and, due to its visualization and keyboard shortcuts, very fast interaction with the data. Therefore, Maui-Via fills an important niche by (1) providing functionality that optimizes the component of data processing that is currently most labor intensive to save time and (2) lowering the threshold of expertise required to process GC-MS data. Maui-VIA projects are initiated with baseline-corrected raw data, peaklists, and a database of metabolite spectra and retention indices used for identification. It provides functionality for retention index calculation, a targeted library search, the visual annotation, alignment, correction interface, and metabolite quantification, as well as the export of the final datamatrix. The high quality of data produced by Maui-VIA is illustrated by its comparison to data attained manually by an expert using vendor software on a previously published dataset concerning the response of Chlamydomonas reinhardtii to salt stress. In conclusion, Maui-VIA provides the opportunity for fast, confident, and high-quality data processing validation of large numbers of GC-MS samples by non-experts.

No MeSH data available.


Related in: MedlinePlus