lehtiolab/ddamsproteomics repository overview

⁠lehtiolab/ddamsproteomics

A Quantitative MS proteomics analysis pipeline

⁠Introduction

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.

⁠How to run

• install Nextflow⁠
• install Docker⁠, Singularity⁠, or Conda⁠
• run pipeline:

nextflow run lehtiolab/ddamsproteomics --mzmls '/path/to/*.mzML' --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 --mzmls '/path/to/*.mzML' --tdb /path/to/proteins.fa --mods 'oxidation;carbamidomethylation --isobaric 'setA:tmt10plex:126 setB:tmt10plex:127N'

The lehtiolab/ddamsproteomics pipeline comes with documentation about the pipeline, found in the docs/ directory:

• Running the pipeline⁠
• Output and how to interpret the results⁠
• Troubleshooting⁠

There is more extensive documentation on the options inside the main.nf file.

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)

⁠Credits

lehtiolab/ddamsproteomics was originally written by Jorrit Boekel and tries to follow the nf-core⁠ best practices and templates.