Bulk AIRR-seq tutorial

This tutorial provides a step by step introduction on how to run nf-core/airrflow on bulk AIRR-seq data.

Pre-requisites

For the purpose of running this tutorial on your local machine, we recommend a Docker installation. To install Docker, follow the instructions here. After installation Docker on Linux, don’t forget to check the post-installation steps.

Alternatively, you can run this tutorial using the Gitpod platform which has Nextflow, nf-core and Docker pre-installed. There are three ways to open the Gitpod platform. Please watch this video to set it up. If you want to know more about Gitpod, check the Gitpod overview.

Testing the pipeline with built-in tests

Once you have set up your Nextflow and container (Docker or Singularity), test nf-core/airrflow with the built-in test data.

nextflow run nf-core/airrflow -r 4.3.0 -profile test,docker --outdir test_results

If the tests run through correctly, you should see this output in your command line:

-[nf-core/airrflow] Pipeline completed successfully-
Completed at: 11-Mar-2025 11:30:35
Duration    : 5m 50s
CPU hours   : 0.6
Succeeded   : 221

Datasets

In this tutorial, we will use nf-core/airrflow to analyze bulk BCR sequencing data from two subjects with multiple sclerosis publicly available on Sequence Read Archive (PRJNA248475) from Stern et al. The first subject has 3 samples available from a cervical lymph node and 1 sample from a brain lesion while the second subject has 3 samples from the lymph node and 3 samples from a brain lesion. You don’t need to download the samples because the links to the samples are already provided in the samplesheet.

Preparing the samplesheet and configuration file

To run the pipeline on bulk BCR/TCR sequencing data, several files must be prepared in advance:

• A tab-separated samplesheet containing the information of each sample. Details on the required columns of a samplesheet are available here.
• A configuration file specifying the system’s maximum available RAM memory, CPUs and running time. This will ensure that no pipeline process requests more resources than available in the compute infrastructure where the pipeline is running. The resource configuration file is provided with the -c option. In this example we set the maximum RAM memory to 20GB, we restrict the pipeline to use 4 CPUs and to run for a maximum of 24 hours. Depending on the size of your dataset, it might be required to extend the running time. You can also remove the “time” parameter from the configuration file to allow for unlimited runtime.

process {
   resourceLimits = [cpus: 4, memory: 20.GB, time: 24.h]
}

A prepared samplesheet for this tutorial can be found here, and the configuration file is available here. Download both files to the directory where you intend to run nf-core/airrflow.

Choosing the right protocol profile

Bulk BCR and TCR targeted sequencing can be performed with a wide variety of protocols, using different library preparation methods. Different protocols usually use different amplification primers, UMI barcode lengths and position, which require different parameter settings to run nf-core/airrflow. To make it easier to run the pipeline on commonly used commercially available kits, we provide parameter presets as profiles. A full list of protocol profiles is available on the usage documentation page.

You can provide a protocol profile with the -profile parameter, followed by other profiles, such as the container engine profile in a comma separated fashion. You will then usually only need to provide the input samplesheet, resource config file and output directory path. However, if you want to override any option or add additional parameters, you can provide them to the airrflow launching command as any parameters in the launch command will override the parameters in the profile.

nextflow run nf-core/airrflow -r 4.3.0 \
-profile <protocol-profile-name>,docker \
--input samplesheet.tsv \
-c resource.config \
--outdir bulk_fastq_results \
-resume

Analyzing a dataset with a custom library preparation method

If your dataset was generated using a custom library preparation method, you can manually set the relevant parameters according to your protocol design, similar to the approach we used for the samples in this tutorial. For more examples on how to set the parameters for custom protocols check the usage documentation page.

The BCRseq dataset used in this tutorial was obtained with a multiplexed PCR protocol using custom C-region and V-region primers. We stored the sequences for the V-region primers as well as the C-region primers in AWS S3, and the links are provided in the Nextflow command which will be fetched by Nextflow automatically when executing the command. You can also provide the full path to the custom primers fasta files.

The command to launch nf-core/airrflow for the dataset in this tutorial is the following:

nextflow run nf-core/airrflow -r 4.3.0 \
-profile docker \
--mode fastq \
--input metadata_pcr_umi_airr_300.tsv \
--cprimers 's3://ngi-igenomes/test-data/airrflow/pcr_umi/cprimers.fasta' \
--vprimers 's3://ngi-igenomes/test-data/airrflow/pcr_umi/vprimers.fasta' \
--library_generation_method specific_pcr_umi \
--cprimer_position R1 \
--umi_length 15 \
--umi_start 0 \
--umi_position R1 \
-c resource.config \
--outdir bulk_fastq_results \
-resume

Of course you can wrap all your code in a bash file. We prepared one for you and it’s available here. With the bash file, it’s easy to run the pipeline with a single-line command.

bash airrflow_bulk_b_fastq.sh

If no UMI barcodes were used, set the --library_generation_method specific_pcr, and the UMI length will be set automatically to 0.

After launching the pipeline the following will be printed to the console output, followed by some the default parameters used by the pipeline and execution log of airrflow processes:

 N E X T F L O W   ~  version 24.10.5
 
WARN: It appears you have never run this project before -- Option `-resume` is ignored
Launching `https://github.com/nf-core/airrflow` [fabulous_cantor] DSL2 - revision: d91dd840f4 [4.3.0]
 
 
------------------------------------------------------
                                        ,--./,-.
        ___     __   __   __   ___     /,-._.--~'
  |\ | |__  __ /  ` /  \ |__) |__         }  {
  | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                        `._,._,'
  nf-core/airrflow 4.3.0
------------------------------------------------------

Once the pipeline has finished successfully, the following message will appear:

-[nf-core/airrflow] Pipeline completed successfully-
Completed at: 11-Mar-2025 15:01:11
Duration    : 23m 46s
CPU hours   : 5.9
Succeeded   : 271

Understanding the results

After running the pipeline, several sub-folders are available under the results folder.

Airrflow_report.html
- fastp
- fastqc
- vdj_annotation
- qc_filtering
- clonal_analysis
- repertoire_comparison
- multiqc
- parsed_logs
- report_file_size
- pipeline_info

The summary report, named Airrflow_report.html, provides an overview of the analysis results, such as an overview of the number of sequences per sample in each of the pipeline steps, the V(D)J gene assignment and QC, and V gene family usage. Additionally, it contains links to detailed reports for other specific analysis steps.

The analysis steps and their corresponding folders, where the results are stored, are briefly listed below. Detailed documentation on the pipeline output can be found on the Output documentation page.

1. Quality control

   • fastp is used to perform quality control, adapter trimming, quality filtering, per-read quality pruning of the FASTQ data. The results are stored under the folder ‘fastp’.
   • FastQC is applied to do some quality control checks on raw sequence data. The fastqc report for each fastq file is under the folder named ‘fastqc’. The aggregated QC reports for all samples can also be checked in the MultiQC report.

2. Sequence assembly

   • pRESTO is a tool part of Immcantation for processing raw reads from high-throughput sequencing of B cell and T cell repertoires. It includes features for quality control, primer masking, annotation of reads with sequence embedded barcodes, generation of unique molecular identifier (UMI) consensus sequences, assembly of paired-end reads and identification of duplicate sequences. The ‘presto’ folder contains the intermediate pRESTO steps logs.

3. V(D)J annotation and filtering

   • In this step, V(D)J gene segments are inferred using the provided germline reference and IgBLAST. Alignments are annotated in AIRR format. Non-productive sequences and sequences with low alignment quality are filtered out unless otherwise specified. The intermediate results are stored under the folder named ‘vdj_annotation’.

4. Post alignment QC and filtering

   • Duplicates detected after alignment are collapsed in this step and the results are available under folder ‘qc-filtering’.

5. Clonal analysis

   • Results of the clonal threshold determination using SHazaM should be inspected in the html report under the ‘clonal_analysis/find_threshold’ folder. If the automatic threshold is unsatisfactory, you can set the threshold manually and re-run the pipeline. (Tip: use -resume whenever running the Nextflow pipeline to avoid duplicating previous work).
   • Clonal inference is performed with SCOPer. Clonal inference results as well as clonal abundance and diversity plots can be inspected in the html report in the folder ‘clonal_analysis/define_clones’. For BCR sequencing data, mutation frequency is also computed using SHazaM at this step and plotted in the report. The repertoires subfolder contains the AIRR formatted files with the clonal assignments in a new column clone_id and mutation frequency in the column mu_freq. The tables subfolder contains the tabulated abundance and diversity computation as well as a table with the number of clones and their size. The ggplots subfolder contains the abundance and diversity plots as an RData object for loading and customization in R.
   • If lineage trees were computed using Dowser, a folder under ‘clonal_analysis/dowser_lineages’ will be present. The trees can be inspected in the html report and saved as PDF. Additionally, an RDS object with the formatted trees can also be loaded in R for customizing the lineage tree plots with Dowser.

6. Repertoire analysis

   • Comparison of several repertoire characteristics, such as V gene usage, across subjects, time points or cell populations. All associated plots and tables are available under the folder repertoire_comparison. The plots are also included in the Airrflow_report.html file. This report is generated from an R markdown Rmd file. It is possible to customize this to meet the user’s needs by editing the report and then providing the edited Rmd file with the --report_rmd parameter. Check the remaining Report parameters for further customizing the report.

7. Other reporting Additional reports are also generated, including:

   • MultiQC report: summarizes QC metrics across all samples.
   • Pipeline_info report: various reports relevant to the running and execution of the pipeline.
   • Report_file_size report: Summary of the number of sequences left after each of the most important pipeline steps.
   • parsed_logs report: Summary of the number of sequences left after each of the most important pipeline steps.

Find out more

To continue learning about how to use nf-core/airrflow please check out the following documentation:

• nf-core/airrflow usage documentation
• nf-core/airrflow parameters documentation
• FAQ page

The nf-core troubleshooting documentation will also help you troubleshoot your Nextflow errors.

• nf-core troubleshooting