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nf-core/proteomicslfq

Proteomics label-free quantification (LFQ) analysis pipeline using OpenMS and MSstats, with feature quantification, feature summarization, quality control and group-based statistical analysis..

GitHub Actions CI Status GitHub Actions Linting Status Nextflow

install with bioconda Docker Get help on Slack

Introduction

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

  2. Install either Docker or Singularity 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:

    nextflow run nf-core/proteomicslfq -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 docker or singularity and set the appropriate execution settings for your local compute environment.

  4. Start running your own analysis!

    nextflow run nf-core/proteomicslfq \
  -profile <docker/singularity/conda/institute> \
  --input '*.mzml' \
  --database 'myProteinDB.fasta' \
  --expdesign 'myDesign.tsv'

See usage docs for all of the available options when running the pipeline. Or configure the pipeline via nf-core launch from the web or the command line.

Documentation

The nf-core/proteomicslfq pipeline comes with documentation about the pipeline which you can read at https://nf-co.re/proteomicslfq or partly find in the docs/ directory.

It performs conversion to indexed mzML, database search (with multiple search engines), re-scoring (with e.g. Percolator), merging, FDR filtering, modification localization with Luciphor2 (e.g. phospho-sites), protein inference and grouping as well as label-free quantification by either spectral counting or feature-based alignment and integration. Downstream processing includes statistical post-processing with MSstats and quality control with PTXQC. For more info, see the output docs.

Credits

nf-core/proteomicslfq was originally written by Julianus Pfeuffer, Lukas Heumos, Leon Bichmann, Timo Sachsenberg, Yasset Perez-Riverol.

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 #proteomicslfq channel (you can join with this invite).

Citation

If you use nf-core/proteomicslfq for your analysis, please cite it using the following doi: 10.5281/zenodo.XXXXXX

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. ReadCube: Full Access Link

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.