Abstract
SummaryThe 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 implementationPiMP 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.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Metabolomics aims to catalogue and quantify the complete small molecule complement of a biological system (Oliver et al, 1998)
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
Summary
Metabolomics aims to catalogue and quantify the complete small molecule complement of a biological system (Oliver et al, 1998). The processing of metabolomics data in PiMP is presented as an assisted pipeline consisting of five sequential tasks: (i) project administration, (ii) data upload, (iii) quality control, (iv) analysis parameters and (v) data interpretation. This assisted pipeline provides guidance to users analysing metabolomics data without necessarily having significant prior knowledge pertaining to metabolomics workflows or even biochemistry. Comparing the three major online platforms available (XCMS Online (Tautenhahn et al, 2012), Workflow4metabolomics (W4M) (Giacomoni et al, 2015) and MetaboAnalyst (Xia et al, 2012), W4M provides a userfriendly front end to XCMS, but is limited to default visualizations and provides no biological inference for the results and MetaboAnalyst provides extensive statistical tools and interpretation, but lacks the contextual design of PiMP. A third party module has already been developed interfacing PiMP with MetExplore (Cottret et al, 2010)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
