Skip to content
A Nextflow MS DDA proteomics pipeline
Nextflow R Python HTML Dockerfile
Branch: master
Clone or download
Pull request Compare This branch is 7 commits ahead of glormph:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
assets
bin
conf
docs
tools/openms
.gitattributes
.gitignore
.travis.yml
CHANGELOG.md
CODE_OF_CONDUCT.md
Dockerfile
LICENSE
README.md
Singularity
environment.yml
main.nf
nextflow.config

README.md

lehtiolab/ddamsproteomics

A Quantitative MS proteomics analysis pipeline

Build Status Nextflow DOI

install with bioconda Docker Singularity Container available

Introduction

This workflow identifies peptides in mzML input data using MSGF+, and Percolator, quantifies isobarically labeled samples with OpenMS, and precursor peptides with Hardklor/Kronik, and processes that output to formatted peptide and protein/gene tables using Msstitch.

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

nextflow run lehtiolab/ddamsproteomics --mzmls '/path/to/*.mzML' --tdb /path/to/proteins.fa --mods /path/to/mods.txt

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)

Credits

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

You can’t perform that action at this time.