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nmrMLconvert

Version: 1.1b

Short Description

nmrML is an open mark-up language for NMR data. This project contains files that are needed to use the tools to convert RAW vendor NMR files to nmrML files.

Description

nmrML is an open mark-up language for NMR data. It is currently under heavy development and is not yet ready for public use. The development of this standard is coordinated by Workpackage 2 of the COSMOS - COordination Of Standards In MetabOlomicS Project. COSMOS is a global effort to enable free and open sharing of metabolomics data. Coordinated by Dr Christoph Steinbeck of the EMBL-European Bioinformatics Institute. COSMOS brings together European data providers to set and promote community standards that will make it easier to disseminate metabolomics data through life science e-infrastructures. This Coordination Action has been financed with €2 million by the European Commission's Seventh Framework Programme. The nmrML data standard will be approved by the Metabolomics standards Initiative and was derived from an earlier nmrML that was developed by the Metabolomics Innovation Centre (TMIC).

Key features

  • Vendor to open access standard conversion
  • Format Converter

Functionality

  • Format Converter
  • Pre-processing

Approaches

  • Metabolomics
  • Chemoinformatics

Instrument Data Types

  • NMR / 1D NMR

Tool Authors

  • Daniel Jacob (INRA)
  • nmrML Standards Group

Container Contributors

Git Repository

Website

Installation

Building the Docker image

docker build --tag="phnmnl/nmrmlconv:latest" .

Usage Instructions

This examples uses one of the RAW files included in MTBLS1, which you get when you clone the nmrML-github repository to /usr/src.

docker run -ti -v /usr/src/nmrML/examples/MTBLS1/FIDs/ADG10003u_007/10/:/src -v $PWD:/data phnmnl/nmrmlconv:latest -l create -b -z -t bruker -i /src/ -o /data/ADG10003u_007_10.nmrml

Galaxy usage

A rudimentary Galaxy node description is included as nmrmlconv.xml.

Publications

  • Rocca-Serra P, Salek RM, Arita M, Correa E, Dayalan S, Gonzalez-Beltran A, Ebbels T, Goodacre R, Hastings J, Haug K, Koulman A, Nikolski M, Oresic M, Sansone SA, Schober D, Smith J, Steinbeck C, Viant MR, Neumann S (2016): Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics 12(1): 14. doi:10.1007/s11306-015-0879-3