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nf-core/msproteomics: Quick Start

Get from raw mass spectrometry data to quantified proteins in minutes. This guide covers the three main analysis modes: DIA, DDA LFQ, and TMT Label Check.

Prerequisites

  • Nextflow >= 25.04.0
  • Docker or Singularity
  • Java 17+

Install Nextflow if you do not have it:

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

Step 1: Install the Pipeline

nextflow pull nf-core/msproteomics

Step 2: Prepare Your Samplesheet

Create a CSV file with one row per MS run. The required columns are sample and spectra; the optional columns are condition, label, and fraction.

Column Required Description
sample Yes Unique sample name (no spaces)
spectra Yes Path to spectra file (.raw, .mzML, .d, .dia)
condition No Experimental group (e.g., control, treated)
label No TMT label channel (e.g., TMT126, TMT127N)
fraction No Fraction number (positive integer)

Here is a minimal DIA or LFQ samplesheet (samplesheet.csv):

sample,spectra,condition,label,fraction
Sample_A1,/data/raw/A1.raw,control,,
Sample_A2,/data/raw/A2.raw,control,,
Sample_B1,/data/raw/B1.raw,treated,,
Sample_B2,/data/raw/B2.raw,treated,,

For a TMT samplesheet example with label annotations, see assets/samplesheet_tmt.csv.

Step 3: Run a DIA Analysis

nextflow run nf-core/msproteomics \
    --mode diann \
    --input samplesheet.csv \
    --outdir results \
    -profile docker

Without --database, the UniProt reference proteome for --organism (default Homo sapiens) is downloaded at runtime; see Database Preparation for the eight supported organisms. For any other organism, supply a FASTA database explicitly with --database /path/to/database.fasta; otherwise the pipeline stops with an error.

Step 4: Run a DDA LFQ Analysis

nextflow run nf-core/msproteomics \
    --mode fragpipe \
    --input samplesheet.csv \
    --database /path/to/database.fasta \
    --fragpipe_container your-registry/fragpipe:24.0 \
    --fragpipe_workflow LFQ-MBR.workflow \
    --outdir results \
    -profile docker

DDA LFQ mode requires a custom FragPipe container with licensed tools. See the FragPipe Docker build guide for instructions on building the container image.

Step 5: Run a TMT Label Check

nextflow run nf-core/msproteomics \
    --mode fragpipe \
    --tmt_mode labelcheck \
    --tmt_type TMT16 \
    --input samplesheet.csv \
    --database /path/to/database.fasta \
    --fragpipe_container your-registry/fragpipe:24.0 \
    --outdir results \
    -profile docker

The pipeline auto-selects the correct workflow file based on --tmt_type. Supported types: TMT6, TMT10, TMT11, TMT16, TMT18, TMTPRO.

Step 6: Check Your Results

All outputs are written under the directory specified by --outdir.

DIA results:

  • diann/report.stats.tsv — summary statistics from DIA-NN
  • multiqc/multiqc_report.html — interactive quality control report

DDA LFQ results:

  • ionquant/combined_protein.tsv — protein-level quantification (MaxLFQ)
  • philosopher_filter/ — filtered PSM and protein reports per sample

TMT Label Check results:

  • tmt_labelcheck/tmt_labelcheck_report.html — labeling efficiency report with per-channel statistics

Step 7: Resume a Failed Run

If a run fails partway through, fix the issue and resume from the cached results:

nextflow run nf-core/msproteomics \
    --mode diann \
    --input samplesheet.csv \
    --outdir results \
    -profile docker \
    -resume

The -resume flag skips tasks that completed successfully and reruns only from the point of failure.

Next Steps