Dataset Information

PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data.

ABSTRACT:

Summary

The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by 'evidence cards', which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information.

Availability and implementation

PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50 GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL.

Contact

karl.burgess@glasgow.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gloaguen Y

PROVIDER: S-EPMC5860087 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data.

Gloaguen Yoann Y Morton Fraser F Daly Rónán R Gurden Ross R Rogers Simon S Wandy Joe J Wilson David D Barrett Michael M Burgess Karl K

Bioinformatics (Oxford, England) 20171201 24

<h4>Summary</h4>The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of ...[more]

PMID: 28961954

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Project description:Data quality in global metabolomics is of great importance for biomarker discovery and system biology studies. However, comprehensive metrics and methods to evaluate and compare the data quality of global metabolomics data sets are lacking. In this work, we combine newly developed metrics, along with well-known measures, to comprehensively and quantitatively characterize the data quality across two similar liquid chromatography coupled with mass spectrometry (LC-MS) platforms, with the goal of providing an efficient and improved ability to evaluate the data quality in global metabolite profiling experiments. A pooled human serum sample was run 50 times on two high-resolution LC-QTOF-MS platforms to provide profile and centroid MS data. These data were processed using Progenesis QI software and then analyzed using five important data quality measures, including retention time drift, the number of compounds detected, missing values, and MS reproducibility (2 measures). The detected compounds were fit to a γ distribution versus compound abundance, which was normalized to allow comparison of different platforms. To evaluate missing values, characteristic curves were obtained by plotting the compound detection percentage versus extraction frequency. To characterize reproducibility, the accumulative coefficient of variation (CV) versus the percentage of total compounds detected and intraclass correlation coefficient (ICC) versus compound abundance were investigated. Key findings include significantly better performance using profile mode data compared to centroid mode as well quantitatively better performance from the newer, higher resolution instrument. A summary table of results gives a snapshot of the experimental results and provides a template to evaluate the global metabolite profiling workflow. In total, these measures give a good overall view of data quality in global profiling and allow comparisons of data acquisition strategies and platforms as well as optimization of parameters.

Dataset Information

PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data.

Summary

Availability and implementation

Contact

Supplementary information

Publications

PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data.

Similar Datasets

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