quantmsdiann

DIA proteomics Nextflow pipeline powered by DIA-NN.

quantmsdiann is a cloud-ready Nextflow pipeline for Data-Independent Acquisition (DIA) proteomics. It leverages DIA-NN as the core engine for peptide identification and quantification, with full integration into the quantms ecosystem.

DDA users: For Data-Dependent Acquisition (LFQ, TMT/iTRAQ), use the quantms pipeline.

Workflow Overview

Key Features

• DIA-NN engine: Neural network-based peptide identification
• Library-free mode: No spectral library needed
• Spectral library mode: Use existing libraries for targeted analysis
• Cloud-ready: AWS, GCP, Azure, HPC, or local execution
• SDRF metadata: Standardized experiment annotation
• QPX output: Parquet-based standardized output
• Quality control: Integrated pmultiqc reports

Quick Start

# Install Nextflow
curl -s https://get.nextflow.io | bash

# Run test profile
nextflow run bigbio/quantmsdiann \
    -profile test,docker \
    --outdir results/

# Run with your data
nextflow run bigbio/quantmsdiann \
    -profile docker \
    --input experiment.sdrf.tsv \
    --database uniprot_human.fasta \
    --outdir results/

DIA vs DDA

Feature	DIA (quantmsdiann)	DDA (quantms)
Precursor selection	All ions in window	Top-N individual ions
Reproducibility	Very high	Moderate
Missing values	Few	Common
Typical proteins	6,000-10,000	3,000-8,000
Engine	DIA-NN	Comet, MS-GF+
Best for	Large cohorts, clinical	Discovery, TMT

Citation

Dai C, Pfeuffer J, Wang H, et al. quantms: a cloud-based pipeline for quantitative proteomics. Nature Methods. 2024;21:1603-1607. DOI: 10.1038/s41592-024-02343-1

Demichev V, et al. DIA-NN: neural networks and interference correction enable deep proteome coverage. Nature Methods. 2020;17:41-44. DOI: 10.1038/s41592-019-0638-x

Ecosystem

Tool	Description
quantms	DDA proteomics pipeline
mokume	Protein quantification library
qpx	Data format conversion
pmultiqc	Interactive QC reporting
portal.quantms.org	Browse reanalyzed datasets