OpenMS

An open-source framework for mass spectrometry and TOPP – The OpenMS Proteomics Pipeline

MetaboProfiler Community Node for Compound Discoverer

 

MetaboProfiler provides functionality of multiple OpenMS 2.0 tools to the recently released Thermo Fisher Compound Discoverer (CD). It extends CD with methods for large-scale feature detection and quantification of small metabolites for label-free LC-MS data.

Download and Installation:

Data is made available at: https://sourceforge.net/projects/metaboprofiler/files/
This includes

  • An automated installer for Windows 7 (64 bit)
  • A manual including step-by-step installation instructions, description of the OpenMS node and its parameters and a tutorial guiding the user through standard CD studies containing MetaboProfiler
  • Example data for the tutorial. Provided are sample and control measurements at one time point after Prazosin was administered
  • CD workflow files of some of the pipelines used in the tutorial

MetaboProfiler for Compound Discoverer 1
MetaboProfiler for Compound Discoverer 2

Source code repository

Disclaimer: Please note that Thermo only provides the framework for the integration of third party plugins. They do neither provide any support nor take any legal responsibility for external contributions. If you have any questions, bug reports, or feature requests regarding the OpenMS MetaboProfiler node, please contact the OpenMS developer mailing list instead: open-ms-developersatlistsdotsourceforgedotnet  (open-ms-developersatlistsdotsourceforgedotnet)  

Features:

  • Detection of small analyte features by a peak-model free approach
  • Optional linear, feature-based alignment of input maps along the retention dimension
  • Linking of corresponding features
  • Visualization of MetaboProfiler results in CD along with raw data
  • Export of results for further analyses outside CD

Functionality:

The node exports input data from a spectrum source to the open standard data format mzML before running a feature quantification pipeline. For quantification, small molecule features are detected with a sensitive and robust algorithm[1]. The detection method is free of assumptions concerning chromatographic feature shapes, leading to increased sensitivity compared to methods based on peak models. Measured feature intensities have been shown to be reproducible and highly correlated to metabolite concentrations in dilution series [1].

In the case that multiple input files are provided to the CD workflow, corresponding features are linked across these input maps (data sets) as described by Weisser et al.[2]. This linking incorporates m/z and retention similarity and additional information, allowing for comparative feature quantification. The feature linking can optionally be precluded by a retention time-based map alignment[3].

The results of the OpenMS tool pipeline are automatically incorporated into the CD reporting format and can be examined inside CD. Export of result tables in the form of human readable text files is is supported by CD.

 

Use:

The functionality of our OpenMS quantification pipeline is encapsulated in a single node which follows on a previous Spectrum Source (e.g. the Spectrum Selector node). Most of the fine-grained parameters of used OpenMS tools are concealed and set to values adapted for Thermo instruments. If the user wants to visualize features linked across multiple input maps, the Peak Consolidator node has to be appended to MetaboProfiler.

Support:

If you have any questions, bug reports, or feature requests, please contact the OpenMS developer mailing list: open-ms-developersatlistsdotsourceforgedotnet  (open-ms-developersatlistsdotsourceforgedotnet)  

    How to cite:
     

  • [1] E. Kenar, H. Franken, S. Forcisi, H. Wörmann Kilia nand Häring, R. Lehmann, S. Philippe, A. Zell, and O. Kohlbacher, “Automated Label-free Quantification of Metabolites from Liquid Chromatography-Mass Spectrometry Data.,” Mol Cell Proteomics, vol. 13, iss. 1, pp. 348-359, 2014.
  • [2] H. Weisser, S. Nahnsen, J. Grossmann, L. Nilse, A. Quandt, H. Brauer, M. Sturm, E. Kenar, O. Kohlbacher, R. Aebersold, and L. Malmström, “An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics.,” J Proteome Res, 2013.
  • [3] E. Lange, C. Gröpl, O. Schulz-Trieglaff, A. Leinenbach, C. Huber, and K. Reinert, “A geometric approach for the alignment of liquid chromatography-mass spectrometry data,” Bioinformatics, vol. 23, iss. 13, pp. 273-281, 2007.