Metabolomics quaLIty coNtrol anD paramEter optimizatioN.

Introduction

metabolinden is a bioinformatics best-practise analysis pipeline for metabolomics data pre-processing and parameter tuning.

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.

Quick Start

1. Install nextflow (>=20.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command (not usable now!):

   nextflow run payamemami/nf-core-metabolinden -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

4. Start running your own analysis!

   nextflow run payamemami/nf-core-metabolinden -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input '*.mzML' --identification_input 'database.tsv' --recalibration_masses 'lock_in_masses.csv'

See usage docs for all of the available options when running the pipeline.

Pipeline Summary

The pipeline currently performs the following:

• Centroiding
• Recalibration
• Feature detection
• Alignment
• Grouping
• Identification based on internal standard
• Data exporting

Documentation

The nf-core/metabolinden pipeline comes with documentation about the pipeline: usage and output.

Credits

payamemami/nf-core-metabolinden was originally written by Payam Emami.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #metabolinden channel (you can join with this invite).

Citations

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.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

In addition, references of tools and data used in this pipeline are as follows: