metaboigniter is bioinformatics pipeline for pre-processing of mass spectrometry-based metabolomics data. It can be used to perform quantification and identification based on MS1 and MS2 data. The backbone of pipeline is based on XCMS, OpenMS, CAMERA, MSnbase, MetFrag, CSIFingerID, CFM-ID, and several other customized tools to noise filtering, quantification and identification both for library and in-silico identification. Please go on to this page to learn how to use the workflow
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.
iii. Download the pipeline and test it on a minimal dataset with a single command
nextflow run nf-core/metaboigniter -profile test,<docker/singularity/conda/institute>
Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use
-profile <institute>in your command. This will enable either
singularityand set the appropriate execution settings for your local compute environment.
iv. Start running your own analysis! We highly recommend that you use the parameter file located in conf/parameters.config. Since the number of parameters is large, it's going to be a fairly complex bash command to run the workflow. Nevertheless, the parameters can always be passed to the workflow as argument using two dashes "--".
Please go on to this page to learn how to use the workflow
nextflow run nf-core/metaboigniter -profile analysis,<docker/singularity/conda/institute>
See usage docs for all of the available options when running the pipeline.
The nf-core/metaboigniter pipeline comes with documentation about the pipeline, found in the
- Pipeline configuration
- Running the pipeline
- Output and how to interpret the results
MetaboIGNITER is a comprehensive pipeline of several independent tools used to pre-process liquid chromatography-mass spectrometry (LCMS) data. We use Nextflow and nf-core to build and run the workflow but parts of this pipeline have also been implemented using Galaxy as part of PhenoMeNal and Pachyderm.
MetaboIGNITER was originally written by Payam Emami. This work has been supported by ELIXIR and PhenoMeNal.
If you would like to contribute to this pipeline, please see the contributing guidelines.
You can cite the
nf-core publication as follows:
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.