nf-core pipeline · identification, mass-spectrometry, metabolomics
nf-core/metaboigniter
Pre-processes mass spectrometry metabolomics data with MS1 quantification and MS2 identification.
What nf-core/metaboigniter is
nf-core/metaboigniter is a bioinformatics pipeline designed for mass spectrometry-based metabolomics analysis. It ingests spectral files to carry out pre-processing, quantification, and metabolite identification across multi-sample experiments.
The pipeline was initially created by Payam Emami, with the Nextflow DSL2 version developed by Axel Walter and Efi Kontou. It addresses the challenge of converting raw mass spectrometry spectra into aligned, quantified, and annotated metabolite tables while addressing missing data.
By supporting MS1 and MS2 spectral levels, the workflow helps researchers systematically discover and characterise chemical features and match tandem mass spectra to candidate compounds.
What the pipeline does
The workflow ingests mass spectrometry files in mzML format through a structured samplesheet detailing sample identifiers, data levels (MS1, MS2, or MS12), and experimental categories. It carries out centroiding to convert continuous mass spectra into discrete signals, followed by feature detection to find individual peaks. Next, it performs adduct detection to identify ions formed via ion-source interactions, aligns equivalent features across different samples, and links signals across different adducts or ionisation modes.
To address gaps across measurements, the pipeline carries out requantification to populate missing values. Compound identification is performed by passing MS2 spectral data to MS2Query and SIRIUS, ultimately producing a consolidated list of quantified and annotated metabolites.
Key facts
- Input format
- mzML
- Supported MS levels
- MS1, MS2, MS12
- Identification tools
- MS2Query, SIRIUS
- Original developer
- Payam Emami
- DSL2 developers
- Axel Walter, Efi Kontou
- Pipeline engine
- Nextflow (DSL2)
- Latest release
- 2.0.1 (2024-04-15)
What you can use it for
- MS1-based metabolite quantification
- Aligns detected spectral features across study samples and fills missing intensity values through requantification to assess relative metabolite levels.
- MS2 spectral identification
- Annotates compound identities from MS2 fragmentation spectra using MS2Query and SIRIUS.
- Combined MS1 and MS2 metabolomics profiling
- Processes joint MS12 datasets to perform feature extraction, inter-sample alignment, adduct linking, and structural identification in one workflow.
Run it here
The research agent runs nf-core/metaboigniter with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Handles MS1-only, MS2-only, and combined MS12 data within a single workflow.
- Performs requantification to resolve missing feature values across sample cohorts.
- Combines two complementary tools, MS2Query and SIRIUS, for compound identification from tandem mass spectra.
Watch out for
- Requires mass spectrometry data to be provided in mzML format.
- Parameters must be supplied via the command line or a Nextflow params file rather than custom configuration files.
Questions
Can I run nf-core/metaboigniter without my own cluster?
The research agent runs nf-core/metaboigniter with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/metaboigniter under?
MIT, like every nf-core pipeline.
What input file formats does the pipeline accept?
The pipeline accepts raw mass spectrometry data in mzML format specified in a CSV samplesheet.
How are missing measurements handled across runs?
The pipeline includes a requantification step designed to backfill missing feature values across samples.
Which software tools perform compound identification?
Metabolite identification is executed using MS2Query and SIRIUS using MS2 spectral data.
Can I supply samples that contain both MS1 and MS2 spectra?
Yes, samples containing both MS1 and MS2 data can be processed by setting the level column to 'MS12' in the samplesheet.
How to cite it
10.5281/zenodo.4743790
Written from nf-core/metaboigniter's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.