** nf-core/airrflow ** is a bioinformatics best-practice pipeline to analyze B-cell or T-cell repertoire sequencing data. It makes use of the Immcantation toolset. The input data can be targeted amplicon bulk sequencing data of the V, D, J and C regions of the B/T-cell receptor with multiplex PCR or 5' RACE protocol, or assembled reads (bulk or single cell).

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.

nf-core/airrflow allows the end-to-end processing of BCR and TCR bulk and single cell targeted sequencing data. Several protocols are supported, please see the usage documenation for more details on the supported protocols.

1. QC and sequence assembly (bulk only)

• Raw read quality control, adapter trimming and clipping (Fastp).
• Filter sequences by base quality (pRESTO FilterSeq).
• Mask amplicon primers (pRESTO MaskPrimers).
• Pair read mates (pRESTO PairSeq).
• For UMI-based sequencing:
  • Cluster sequences according to similarity (optional for insufficient UMI diversity) (pRESTO ClusterSets).
  • Build consensus of sequences with the same UMI barcode (pRESTO BuildConsensus).
• Assemble R1 and R2 read mates (pRESTO AssemblePairs).
• Remove and annotate read duplicates (pRESTO CollapseSeq).
• Filter out sequences that do not have at least 2 duplicates (pRESTO SplitSeq).

2. V(D)J annotation and filtering (bulk and single-cell)

• Assign gene segments with IgBlast using the IMGT database (Change-O AssignGenes).
• Annotate alignments in AIRR format (Change-O MakeDB)
• Filter by alignment quality (locus matching v_call chain, min 200 informative positions, max 10% N nucleotides)
• Filter productive sequences (Change-O ParseDB split)
• Filter junction length multiple of 3
• Annotate metadata (EnchantR)

3. QC filtering (bulk and single-cell)

• Bulk sequencing filtering:
  • Remove chimeric sequences (optional) (SHazaM, EnchantR)
  • Detect cross-contamination (optional) (EnchantR)
  • Collapse duplicates (Alakazam, EnchantR)
• Single-cell QC filtering (EnchantR)
  • Remove cells without heavy chains.
  • Remove cells with multiple heavy chains.
  • Remove sequences in different samples that share the same cell_id and nucleotide sequence.
  • Modify cell_ids to ensure they are unique in the project.

4. Clonal analysis (bulk and single-cell)

• Find threshold for clone definition (SHazaM, EnchantR).
• Create germlines and define clones, repertoire analysis (Change-O, EnchantR).
• Build lineage trees (SCOPer, IgphyML, EnchantR).

5. Repertoire analysis and reporting

• Custom repertoire analysis pipeline report (Alakazam).
• Aggregate QC reports (MultiQC).

Note If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, ensure that the pipeline tests run on your infrastructure:

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

To run on your data, prepare a tab-separated samplesheet with your input data. Depending on the input data type (bulk or single-cell, raw reads or assembled reads) the input samplesheet will vary. Please follow the documentation on samplesheets for more details. An example samplesheet for running the pipeline on raw BCR / TCR sequencing data looks as follows:

Each row represents a sample with fastq files (paired-end).

A typical command to run the pipeline is:

nextflow run nf-core/airrflow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--input samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12 \
--max_memory 8.GB \
--max_cpus 8 \
--outdir ./results

Warning: Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details, please refer to the usage documentation and the parameter documentation.

To see the the results of a test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

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

Further contributors to the pipeline are:

• @dladd

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 CITATIONS.md 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.