nfcore/proteomicslfq repository overview

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

⁠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 (>=20.04.0)

2. Install any of Docker⁠, Singularity⁠ or Podman⁠ 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/podman/conda/institute>
   

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   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/podman/shifter/charliecloud/conda/institute> \
     --input '*.mzml' \
     --database 'myProteinDB.fasta' \
     --expdesign 'myDesign.tsv'
   

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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.

⁠Pipeline Summary

By default, the pipeline currently performs the following:

• Conversion to indexed mzML
• Peptide database search (with multiple search engines)
• Re-scoring (with e.g. Percolator)
• Merging with ConsensusID
• FDR filtering
• Modification localization with Luciphor2 (e.g. phospho-sites)
• Protein inference and grouping
• Label-free relative quantification by either spectral counting or feature-based alignment and integration
• Downstream processing includes statistical post-processing with MSstats and quality control with PTXQC

⁠Documentation

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

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).

⁠Citations

If you use nf-core/proteomicslfq for your analysis, please cite it using the following doi: 10.5281/zenodo.4106005⁠

You can cite the nf-core publication as follows:

In addition, references of tools and data used in this pipeline can be found in the CITATIONS.md⁠ file.