** nf-core/airrflow ** is a bioinformatics best-practice pipeline to analyze B-cell or T-cell bulk repertoire sequencing data. It makes use of the Immcantation toolset and requires as input targeted amplicon sequencing data of the V, D, J and C regions of the B/T-cell receptor with multiplex PCR or 5’ RACE protocol.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

By default, the pipeline currently performs the following steps:

  • Raw read quality control (FastQC)
  • Pre-processing (pRESTO)
    • Filtering sequences by sequencing quality.
    • Masking amplicon primers.
    • Pairing read mates.
    • Cluster sequences according to similarity, it helps identify if the UMI barcode diversity was not high enough.
    • Building consensus of sequences with the same UMI barcode.
    • Re-pairing read mates.
    • Assembling R1 and R2 read mates.
    • Removing and annotating read duplicates with different UMI barcodes.
    • Filtering out sequences that do not have at least 2 duplicates.
  • Assigning gene segment alleles with IgBlast using the IMGT database (Change-O).
  • Finding the Hamming distance threshold for clone definition (SHazaM).
  • Clonal assignment: defining clonal lineages of the B-cell / T-cell populations (Change-O).
  • Reconstructing gene calls of germline sequences (Change-O).
  • Generating clonal trees (Alakazam).
  • Repertoire analysis: calculation of clonal diversity and abundance (Alakazam).
  • Aggregating QC reports (MultiQC).

Quick Start

  1. Install Nextflow (>=21.10.3)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

    nextflow run nf-core/airrflow -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute> --outdir <OUTDIR>

    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

    • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
    • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
    • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
    • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.
  4. Start running your own analysis!

nextflow run nf-core/airrflow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--input samplesheet.tsv \
--outdir ./results \
--protocol pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12

See usage docs for all of the available options when running the pipeline.


The nf-core/airrflow pipeline comes with documentation about the pipeline usage, parameters and output.


nf-core/airrflow was originally written by Gisela Gabernet, Simon Heumos, Alexander Peltzer.

Further contributors to the pipeline are:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #airrflow channel (you can join with this invite).


If you use nf-core/airrflow for your analysis, please cite it using the following DOI: 10.5281/zenodo.2642009

An extensive list of references for the tools used by the pipeline can be found in the file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.