A Quantitative MS proteomics analysis pipeline
This workflow identifies peptides in mzML input data using MSGF+, and Percolator, quantifies isobarically labeled samples with OpenMS, and precursor peptides with Dinosaur, and processes that output to formatted peptide and protein/gene tables using Msstitch. Optional PTM data is analyzed by Luciphor2, and differential expression analyses can be performed using DEqMS.
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 / singularity containers making installation trivial and results highly reproducible.
- install Nextflow
- install Docker, Singularity, or Conda
- run pipeline:
nextflow run lehtiolab/ddamsproteomics --input /path/to/input_definition.txt --sage --tdb /path/to/proteins.fa --mods 'oxidation;carbamidomethylation' -profile standard,docker
Or for two sample sets of isobaric data you can:
nextflow run lehtiolab/ddamsproteomics --input /path/to/input_definition.txt --sage --tdb /path/to/proteins.fa --mods 'oxidation;carbamidomethylation --isobaric 'setA:tmt10plex:126 setB:tmt10plex:127N'
For more elaborate examples covering fractionation, MSGF+, PTMs, and more, the lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the docs/ directory:
The pipeline takes multiple mzML files as input and performs identification and quantification to output results and a QC report (an example can be found here, save to your computer and open it locally).
lehtiolab/ddamsproteomics was originally written by Jorrit Boekel and has been inspired by nf-core pipelines
