# nf-core/airrflow: Usage

## :warning: Please read this documentation on the nf-core website: [https://nf-co.re/airrflow/usage](https://nf-co.re/airrflow/usage)

> _Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files._

## Introduction

The nf-core/airrflow pipeline allows processing B-cell receptor (BCR) and and T-cell receptor (TCR) sequencing data from bulk and single-cell sequencing protocols. It allows the processing of targeted bulk and single-cell adaptive immune receptor sequencing data (AIRR-seq), as well as the extraction of TCR and BCR sequences from untargeted bulk and single-cell RNA-seq data. The pipeline enables and end-to-end analysis, departing from raw reads or readily assembled sequences, and performs sequence assembly, V(D)J assignment, clonal group inference, lineage reconstruction and repertoire analysis using the [Immcantation](https://immcantation.readthedocs.io/en/stable/) framework, as well as other immune repertoire analysis tools.

In addition to this page, you can find additional information on how to use the pipeline on the following pages:

- [bulk_tutorial](usage/bulk_tutorial.md): a step by step tutorial on how to run nf-core/airrflow for bulk data.
- [single_cell_tutorial](usage/single_cell_tutorial.md): a step by step tutorial on how to run nf-core/airrflow for single-cell data.

## Running the pipeline

### Quickstart

> [!INSTALLATION]
> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set up Nextflow and a container engine needed to run this pipeline. At the moment, nf-core/airrflow does NOT support using conda virtual environments for dependency management, only containers are supported. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.

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

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

A typical command for running the pipeline for **bulk raw fastq files** using available pre-set protocol profiles is shown below. The full list of supported profiles can be found in the section [Supported protocol profiles](#supported-protocol-profiles).

```bash
nextflow run nf-core/airrflow \
-profile nebnext_umi_bcr,docker \
--input input_samplesheet.tsv \
--outdir results
```

It is also possible to process custom sequencing protocols with custom primers by manually specifying the primers, UMI length (if available) and position:

```bash
nextflow run nf-core/airrflow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12 \
--umi_position R1 \
--outdir results
```

A typical command to run the pipeline from **single cell raw fastq files** is:

```bash
nextflow run nf-core/airrflow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method sc_10x_genomics \
--reference_10x reference/refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0.tar.gz \
--outdir results
```

A typical command for running the pipeline departing from **single-cell AIRR rearrangement tables or assembled bulk sequencing fasta** data is:

```bash
nextflow run nf-core/airrflow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--input input_samplesheet.tsv \
--mode assembled \
--outdir results
```

It is also possible to reconstruct BCR and TCR sequences from untargeted bulk and single-cell sequencing data. A typical command to run the pipeline from **single-cell RNA-seq fastq files** is shown below. For more information, check the section on [supported untargeted RNA-seq based methods](#supported-untargeted-rna-seq-based-methods) below.

```bash
nextflow run nf-core/airrfow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method trust4 \
--umi_read R1 \
--cell_barcode_read R1 \
--read_format bc:0:15,um:16:27 \
--outdir results
```

Check the section [Input samplesheet](#input-samplesheet) below for instructions on how to create the samplesheet, and the [Supported library generation protocols](#supported-bulk-library-generation-methods-protocols) section below for examples on how to run the pipeline for the different bulk and single-cell sequencing protocols.
For more detailed information about all the available parameters, please refer to the [parameters documentation](https://nf-co.re/airrflow/parameters).
The command above will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

```console
work            # Directory containing the nextflow working files
<OUTDIR>         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.
```

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a `yaml` or `json` file via `-params-file <file>`.

> [!WARNING]
> Do not use `-c <file>` to specify parameters as this will result in errors. Custom config files specified with `-c` must only be used for [tuning process resource specifications](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources), other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

```bash
nextflow run nf-core/airrflow -profile docker -params-file params.yaml
```

with:

```yaml title="params.yaml"
input: './samplesheet.csv'
outdir: './results/'
<...>
```

You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-co.re/launch).

## Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

```bash
nextflow pull nf-core/airrflow
```

## Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the [nf-core/airrflow releases page](https://github.com/nf-core/airrflow/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

> [!TIP]
> If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

## Input samplesheet

### Fastq input samplesheet (bulk AIRR sequencing)

The required input file for processing raw BCR or TCR bulk targeted sequencing data is a sample sheet in TSV format (tab separated). The columns `sample_id`, `filename_R1`, `filename_R2`, `subject_id`, `species`, `tissue`, `pcr_target_locus`, `single_cell`, `sex`, `age` and `biomaterial_provider` are required. An example samplesheet is:

| sample_id | filename_R1                     | filename_R2                     | filename_I1                     | subject_id | species | pcr_target_locus | tissue | sex    | age | biomaterial_provider | single_cell | intervention   | collection_time_point_relative | cell_subset  |
| --------- | ------------------------------- | ------------------------------- | ------------------------------- | ---------- | ------- | ---------------- | ------ | ------ | --- | -------------------- | ----------- | -------------- | ------------------------------ | ------------ |
| sample01  | sample1_S8_L001_R1_001.fastq.gz | sample1_S8_L001_R2_001.fastq.gz | sample1_S8_L001_I1_001.fastq.gz | Subject02  | human   | IG               | blood  | NA     | 53  | sequencing_facility  | FALSE       | Drug_treatment | Baseline                       | plasmablasts |
| sample02  | sample2_S8_L001_R1_001.fastq.gz | sample2_S8_L001_R2_001.fastq.gz | sample2_S8_L001_I1_001.fastq.gz | Subject02  | human   | TR               | blood  | female | 78  | sequencing_facility  | FALSE       | Drug_treatment | Baseline                       | plasmablasts |

- `sample_id`: Sample ID assigned by submitter, unique within study.
- `filename_R1`: path to fastq file with first mates of paired-end sequencing.
- `filename_R2`: path to fastq file with second mates of paired-end sequencing.
- `filename_I1` (optional): path to fastq with illumina index and UMI (unique molecular identifier) barcode.
- `subject_id`: Subject ID assigned by submitter, unique within study.
- `species`: species from which the sample was taken. Supported species are `human` and `mouse`.
- `tissue`: tissue from which the sample was taken. E.g. `blood`, `PBMC`, `brain`.
- `pcr_target_locus`: Designation of the target locus (`IG` or `TR`).
- `biomaterial_provider`: Institution / research group that provided the samples.
- `sex`: Subject biological sex (`female`, `male`, etc.).
- `age`: Subject biological age.
- `single_cell`: TRUE or FALSE.

Other optional columns can be added. These columns will be available as metadata in the final repertoire table. It is recommended that these columns also follow the AIRR nomenclature. Examples are:

- `intervention`: Description of intervention.
- `disease_diagnosis`: Diagnosis of subject.
- `collection_time_point_relative`: Time point at which sample was taken, relative to `collection_time_point_reference` (e.g. 14d, 6 months, baseline).
- `collection_time_point_reference`: Event in the study schedule to which `Sample collection time` relates to (e.g. primary vaccination, intervention start).
- `cell_subset`: Commonly-used designation of isolated cell population.

It is possible to provide several fastq files per sample (e.g. sequenced over different chips or lanes). In this case the different fastq files per sample will be merged together prior to processing. Provide one fastq pair R1/R2 per row, and the same `sample_id` field for these rows.

### Fastq input samplesheet (single-cell AIRR sequencing)

The required input file for processing raw BCR or TCR single cell targeted sequencing data is a sample sheet in TSV format (tab separated). The columns `sample_id`, `filename_R1`, `filename_R2`, `subject_id`, `species`, `tissue`, `pcr_target_locus`, `single_cell`, `sex`, `age` and `biomaterial_provider` are required. Any other columns you add will be available in the final repertoire file as extra metadata fields. You can refer to the bulk fastq input section for documentation on the individual columns.
An example samplesheet is:

| sample_id | filename_R1                      | filename_R2                      | subject_id | species | pcr_target_locus | tissue | sex    | age | biomaterial_provider | single_cell |
| --------- | -------------------------------- | -------------------------------- | ---------- | ------- | ---------------- | ------ | ------ | --- | -------------------- | ----------- |
| sample01  | sample01_S1_L001_R1_001.fastq.gz | sample01_S1_L001_R2_001.fastq.gz | Subject02  | human   | IG               | blood  | NA     | 53  | sequencing_facility  | TRUE        |
| sample02  | sample02_S1_L001_R1_001.fastq.gz | sample02_S1_L001_R2_001.fastq.gz | Subject02  | human   | TR               | blood  | female | 78  | sequencing_facility  | TRUE        |

> FASTQ files must conform with the 10xGenomics cellranger naming conventions with the same sample name as provided in the sample*id column <br> >\*\*`[SAMPLE-NAME]`* S`[CHIP-NUMBER]`_ L00`[LANE-NUMBER]`_`[R1/R2]`\_001.fastq.gz\*\*
>
> Read type is one of
>
> - `I1`: Sample index read (optional)
> - `I2`: Sample index read (optional)
> - `R1`: Read 1
> - `R2`: Read 2

It is possible to provide several fastq files per sample (e.g. sequenced over different chips or lanes). In this case the different fastq files per sample will be provided to the same cellranger process. These rows should then have an identical `sample_id` field.

### Fastq input samplesheet (untargeted bulk or single-cell RNAseq)

When running the untargeted protocol, BCR or TCR sequences will be extracted from the untargeted bulk or single-cell RNA sequencing with tools such as [TRUST4](https://github.com/liulab-dfci/TRUST4).
The required input file is the same as for the [Fastq bulk AIRR samplesheet](#fastq-input-samplesheet-bulk-airr-sequencing) or [Fastq single-cell AIRR samplesheet](#fastq-input-samplesheet-single-cell-sequencing) depending on the input data type (bulk RNAseq or single-cell RNAseq).

### Assembled input samplesheet (bulk or single-cell sequencing)

The required input file for processing raw BCR or TCR bulk targeted sequencing data is a sample sheet in TSV format (tab separated). The columns `sample_id`, `filename`, `subject_id`, `species`, `tissue`, `single_cell`, `sex`, `age` and `biomaterial_provider` are required. All fields are explained in the previous section, with the only difference being that there is only one `filename` column for the assembled input samplesheet. The provided file will be different from assembled single-cell or bulk data:

- `filename` for single-cell assembled data: path to `airr_rearrangement.tsv` file, for example the one generated when processing the 10x Genomics scBCRseq / scTCRseq with 10x Genomics cellranger `cellranger vdj` or `cellranger multi`. The field accepts any tsv tables following the [AIRR rearrangement Schema specification](https://docs.airr-community.org/en/stable/datarep/rearrangements.html). See [here](https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/output/annotation#airr) for more details on the cellranger output.
- `filename` for bulk assembled data: path to `sequences.fasta` file, containing the assembled and error-corrected reads.

The required input file for processing raw BCR or TCR bulk targeted sequencing data is a sample sheet in TSV format (tab separated). The columns `sample_id`, `filename`, `subject_id`, `species`, `tissue`, `single_cell`, `pcr_target_locus`, `sex`, `age` and `biomaterial_provider` are required.

An example samplesheet is:

| filename                                    | species | subject_id | sample_id              | tissue | sex  | age | biomaterial_provider | pcr_target_locus | single_cell |
| ------------------------------------------- | ------- | ---------- | ---------------------- | ------ | ---- | --- | -------------------- | ---------------- | ----------- |
| sc5p_v2_hs_PBMC_1k_b_airr_rearrangement.tsv | human   | subject_x  | sc5p_v2_hs_PBMC_1k_5fb | PBMC   | NA   | NA  | 10x Genomics         | IG               | TRUE        |
| bulk-Laserson-2014.fasta                    | human   | PGP1       | PGP1                   | PBMC   | male | NA  | Laserson-2014        | IG               | FALSE       |

### Supported AIRR metadata fields

nf-core/airrflow offers full support for the [AIRR standards 1.4](https://docs.airr-community.org/en/stable/datarep/metadata.html) metadata annotation. The minimum metadata fields that are needed by the pipeline are listed in the table below. Other non-mandatory AIRR fields can be provided in the input samplesheet, which will be available for reporting and introducing comparisons among repertoires.

| AIRR field                | Type               | Parameter Name                | Description                                           |
| ------------------------- | ------------------ | ----------------------------- | ----------------------------------------------------- |
| sample_id                 | Samplesheet column |                               | Sample ID assigned by submitter, unique within study  |
| subject_id                | Samplesheet column |                               | Subject ID assigned by submitter, unique within study |
| species                   | Samplesheet column |                               | Subject species                                       |
| tissue                    | Samplesheet column |                               | Sample tissue                                         |
| pcr_target_locus          | Samplesheet column |                               | Designation of the target locus (IG or TR)            |
| sex                       | Samplesheet column |                               | Subject sex                                           |
| age                       | Samplesheet column |                               | Subject age                                           |
| biomaterial_provider      | Samplesheet column |                               | Name of sample biomaterial provider                   |
| library_generation_method | Parameter          | `--library_generation_method` | Generic type of library generation                    |

## Supported protocol profiles

### NEBNext Immune Sequencing Kit

- [New England Biolabs NEBNext Immune sequencing kit](https://www.neb.com/en-us/products/e6320-nebnext-immune-sequencing-kit-human#Product%20Information)

You can use the `nebnext_umi_bcr` or `nebnext_umi_tcr` preset defaults for analyzing bulk fastq sequencing data that was generated with the NEB Immune Profiling kit. An example using docker containers for the analysis is:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile nebnext_umi_bcr,docker \
--input input_samplesheet.tsv \
--outdir results
```

This profile executes the commands based on the pRESTO pre-set pipeline [presto-abseq.sh](https://bitbucket.org/kleinstein/immcantation/src/master/pipelines/presto-abseq.sh). A summary of the performed steps is:

- Filter sequences by base quality.
- Score and mask the provided R1 primers and R2 template switch oligo. Primer defaults are taken from the [Immcantation repository](https://bitbucket.org/kleinstein/immcantation/src/master/protocols/AbSeq/).
- Pair sequences, build UMI consensus sequence.
- Assemble read pairs with the pRESTO `AssemblePairs sequential` option.
- Align and annotate the internal C Region (for the BCR specific protocol) for a more specific isotype annotation.
- Remove duplicate sequences and filter to sequences with at least 2 supporting sources.

Please note that the default primer sequences and internal CRegion sequences are for human. If you wish to run this protocol on mouse or other species, please provide the alternative primers. Here is an example using the mouse IG primers from the Immcantation Bitbucket repository:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile nebnext_umi_bcr,docker \
--input input_samplesheet.tsv \
--cprimers https://bitbucket.org/kleinstein/immcantation/raw/354f49228a43b4c2858d67fb09886126b314e317/protocols/AbSeq/AbSeq_R1_Mouse_IG_Primers.fasta \
--internal_cregion_sequences https://bitbucket.org/kleinstein/immcantation/raw/354f49228a43b4c2858d67fb09886126b314e317/protocols/AbSeq/AbSeq_Mouse_IG_InternalCRegion.fasta \
--outdir results
```

And similarly for TCR libraries:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile nebnext_umi_tcr,docker \
--input input_samplesheet.tsv \
--cprimers https://bitbucket.org/kleinstein/immcantation/raw/354f49228a43b4c2858d67fb09886126b314e317/protocols/AbSeq/AbSeq_R1_Mouse_TR_Primers.fasta \
--internal_cregion_sequences https://bitbucket.org/kleinstein/immcantation/raw/354f49228a43b4c2858d67fb09886126b314e317/protocols/AbSeq/AbSeq_Mouse_TR_InternalCRegion.fasta \
--outdir results
```

### Clontech / Takara SMARTer Human BCR/TCR Profiling kit

- [TaKaRa SMARTer Human BCR kit](https://www.takarabio.com/products/next-generation-sequencing/immune-profiling/human-repertoire/human-bcr-profiling-kit-for-illumina-sequencing)

You can use the `clontech_umi_bcr` or `clontech_umi_tcr` preset defaults for analyzing bulk fastq sequencing data that was generated with the Takara SMARTer Human Profiling kit. An example using docker containers for the analysis is:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile clontech_umi_bcr,docker \
--input input_samplesheet.tsv \
--outdir results
```

This profile executes the sequence assembly commands based on the pRESTO pre-set pipeline [presto-clontech-umi.sh](https://bitbucket.org/kleinstein/immcantation/src/master/pipelines/presto-clontech-umi.sh). A summary of the performed steps is:

- Filter sequences by base quality.
- Align and annotate the universal C region seqeunces in the R1 reads. Defaults are taken from the [Immcantation repository](https://bitbucket.org/kleinstein/immcantation/src/master/protocols/Universal/).
- Identify the primers sequences and UMI (12 nt length) in the R2 reads.
- Pair sequences, build UMI consensus sequence.
- Assemble read pairs with the pRESTO `AssemblePairs sequential` option.
- Align and annotate the C Region sequences.
- Remove duplicate sequences and filter to sequences with at least 2 supporting sources.

After the sequence assembly steps, the remaining steps are common for all protocols.

Please note that the default primer sequences and internal CRegion sequences are for human. If you wish to run this protocol on mouse or other species, please provide the alternative primer sequences. Here is an example using the mouse IG primers from the Immcantation Bitbucket repository:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile clontech_umi_bcr,docker \
--input input_samplesheet.tsv \
--cprimers https://bitbucket.org/kleinstein/immcantation/raw/c98269b194e9c6262fe3b098be3600ba7f64b85c/protocols/Universal/Mouse_IG_CRegion_RC.fasta \
--outdir results
```

And for TCR data:

```bash
nextflow run nf-core/airrflow -r <release> \
-profile clontech_umi_tcr,docker \
--input input_samplesheet.tsv \
--cprimers https://bitbucket.org/kleinstein/immcantation/raw/c98269b194e9c6262fe3b098be3600ba7f64b85c/protocols/Universal/Mouse_TR_CRegion_RC.fasta \
--outdir results
```

## Supported custom bulk library generation methods (protocols)

For common sequencing protocols such as commercial kits please check the section above if your kit has a preset profile first, as this will greatly simplify running the pipeline. When processing bulk sequencing data departing from raw `fastq` reads, several sequencing protocols are supported which can be provided with the parameter `--library_generation_method`.
The following table matches the library generation methods as described in the [AIRR metadata annotation guidelines](https://docs.airr-community.org/en/stable/miairr/metadata_guidelines.html#library-generation-method) to the value that can be provided to the `--library_generation_method` parameter.

| Library generation methods (AIRR) | Description                                                                                | Name in pipeline |
| --------------------------------- | ------------------------------------------------------------------------------------------ | ---------------- |
| RT(RHP)+PCR                       | RT-PCR using random hexamer primers                                                        | Not supported    |
| RT(oligo-dT)+PCR                  | RT-PCR using oligo-dT primers                                                              | Not supported    |
| RT(oligo-dT)+TS+PCR               | 5’-RACE PCR (i.e. RT is followed by a template switch (TS) step) using oligo-dT primers    | dt_5p_race       |
| RT(oligo-dT)+TS(UMI)+PCR          | 5’-RACE PCR using oligo-dT primers and template switch primers containing UMI              | dt_5p_race_umi   |
| RT(specific)+PCR                  | RT-PCR using transcript-specific primers                                                   | specific_pcr     |
| RT(specific)+TS+PCR               | 5’-RACE PCR using transcript- specific primers                                             | Not supported    |
| RT(specific)+TS(UMI)+PCR          | 5’-RACE PCR using transcript- specific primers and template switch primers containing UMIs | Not supported    |
| RT(specific+UMI)+PCR              | RT-PCR using transcript-specific primers containing UMIs                                   | specific_pcr_umi |
| RT(specific+UMI)+TS+PCR           | 5’-RACE PCR using transcript- specific primers containing UMIs                             | Not supported    |
| RT(specific)+TS                   | RT-based generation of dsDNA without subsequent PCR. This is used by RNA-seq kits.         | Not supported    |

### Multiplex specific PCR (with or without UMI)

This sequencing type requires setting `--library_generation_method specific_pcr_umi` if UMI barcodes were used, or `--library_generation_method specific_pcr` if no UMI barcodes were used (sans-umi). If the option without UMI barcodes is selected, the UMI length will be set automatically to 0.

It is required to provide the sequences for the V-region primers as well as the C-region primers used in the specific PCR amplification. Some examples of UMI and barcode configurations are provided. Depending on the position of the C-region primer, V-region primers and UMI barcodes, there are several possibilities detailed in the following subsections.

#### R1 read contains C primer (and UMI barcode)

The `--cprimer_position` and `--umi_position` (if UMIs are used) parameters need to be set to R1 (this is the default).
If there are extra bases between the UMI barcode and C primer, specify the number of bases with the `--cprimer_start` parameter (default zero). Set `--cprimer_position R1` (this is the default).

![nf-core/airrflow](images/Primers_R1_UMI_C.png)

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12 \
--umi_position R1 \
--cprimer_start 0 \
--cprimer_position R1 \
--outdir ./results
```

If UMIs are not used:

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--cprimer_start 0 \
--cprimer_position R1 \
--outdir ./results
```

#### R1 read contains V primer (and UMI barcode)

The `--umi_position` parameter needs to be set to R1 (if UMIs are used), and `--cprimer_position` to `R2`.
If there are extra bases between the UMI barcode and V primer, specify the number of bases with the `--vprimer_start` parameter (default zero).

![nf-core/airrflow](images/Primers_R1_UMI_V.png)

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12 \
--umi_position R1 \
--vprimer_start 0 \
--cprimer_position R2 \
--outdir ./results
```

If UMIs are not used:

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--vprimer_start 0 \
--cprimer_position R2 \
--outdir results
```

#### R2 read contains C primer (and UMI barcode)

The `--umi_position` and `--cprimer_position` parameters need to be set to R2.
If there are extra bases between the UMI barcode and C primer, specify the number of bases with the `--cprimer_start` parameter (default zero).

![nf-core/airrflow](images/Primers_R1_V.png)

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers CPrimers.fasta \
--vprimers VPrimers.fasta \
--umi_length 12 \
--umi_position R2 \
--cprimer_start 0 \
--cprimer_position R2 \
--outdir ./results
```

#### UMI barcode is provided in the index file

If the UMI barcodes are provided in an additional index file, please provide it in the column `filename_I1` in the input samplesheet and additionally set the `--index_file` parameter. Specify the UMI barcode length with the `--umi_length` parameter. You can optionally specify the UMI start position in the index sequence with the `--umi_start` parameter (the default is 0).

For example:

```bash
nextflow run nf-core/airrflow -profile docker \
--input samplesheet.tsv \
--library_generation_method specific_pcr_umi \
--cprimers Cprimers.fasta \
--vprimers Vprimers.fasta \
--cprimer_position R1 \
--index_file \
--umi_length 12 \
--umi_start 6 \
--outdir ./results
```

## UMI barcode handling

Unique Molecular Identifiers (UMIs) enable the quantification of BCR or TCR abundance in the original sample by allowing to distinguish PCR duplicates from original sample duplicates.
The UMI indices are random nucleotide sequences of a pre-determined length that are added to the sequencing libraries before any PCR amplification steps, for example as part of the primer sequences.

The UMI barcodes are typically read from an index file but sometimes can be provided at the start of the R1 or R2 reads:

- UMIs in the index file: if the UMI barcodes are provided in an additional index file, set the `--index_file` parameter. Specify the UMI barcode length with the `--umi_length` parameter. You can optionally specify the UMI start position in the index sequence with the `--umi_start` parameter (the default is 0).

- UMIs in R1 or R2 reads: if the UMIs are contained within the R1 or R2 reads, set the `--umi_position` parameter to `R1` or `R2`, respectively. Specify the UMI barcode length with the `--umi_length` parameter.

- No UMIs in R1 or R2 reads: if no UMIs are present in the samples, specify `--umi_length 0` to use the sans-UMI subworkflow.

## Supported single cell library generation methods (protocols)

When processing single cell sequencing data departing from raw `fastq` reads, currently only a `--library_generation_method` to support 10xGenomics data is available.

| Library generation methods | Description                                                                                                 | Name in pipeline | Commercial protocols |
| -------------------------- | ----------------------------------------------------------------------------------------------------------- | ---------------- | -------------------- |
| RT(RHP)+PCR                | sequencing data produced from Chromium single cell 5'V(D)J libraries containing cellular barcodes and UMIs. | sc_10x_genomics  | 10xGenomics          |

### 10xGenomics

This sequencing type requires setting `--library_generation_method sc_10x_genomics`.
The `cellranger vdj` tool automatically uses the Chromium cell barcodes and UMIs to perform sequence assembly, paired clonotype calling and to assemble V(D)J transcripts per cell. The pipeline will then perform gene reassignment and clonotyping with the Immcantation framework unless otherwise specified.

Examples are provided below to run airrflow to process 10xGenomics raw FASTQ data.

```bash
nextflow run nf-core/airrflow -r dev \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method sc_10x_genomics \
--reference_10x reference/refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0.tar.gz \
--outdir ./results
```

#### 10xGenomics reference

10xGenomics requires a reference. This can be provided using the `--reference_10x` parameter.

- The 10xGenomics reference can be downloaded from the [download page](https://www.10xgenomics.com/support/software/cell-ranger/downloads)
- To generate a V(D)J segment fasta file as reference from IMGT one can follow the [cellranger docs](https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/advanced/references#imgt).

## Supported untargeted RNA-seq based methods

nf-core/airrflow supports untargeted bulk or single-cell RNA-seq fastq files as input. [TRUST4](https://github.com/liulab-dfci/TRUST4) is used to extract TCR/BCR sequences from these files. The resulting AIRR tables are then fed into airrflow's Immcantation based workflow. <br>
To use untargeted RNA-seq based input, specify `--library_generation_method trust4`.

### Bulk RNA-seq

A typical command to run the pipeline from **bulk RNA-seq fastq files** is:

```bash
nextflow run nf-core/airrfow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method trust4 \
--outdir results
```

### Single-cell RNA-seq

A typical command to run the pipeline from **single-cell RNA-seq fastq files** is:

```bash
nextflow run nf-core/airrfow \
-profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
--mode fastq \
--input input_samplesheet.tsv \
--library_generation_method trust4 \
--umi_read R1 \
--cell_barcode_read R1 \
--read_format bc:0:15,um:16:27 \
--outdir results
```

- If UMI's are present, the read containing them must be specified using the `--umi_read` parameter.
- The `--read_format` parameter can be used to specify the Cell Barcode and UMI position within the reads (see TRUST4 [docs](https://github.com/liulab-dfci/TRUST4?tab=readme-ov-file#10x-genomics-data-and-barcode-based-single-cell-data)). For scRNAseq with 10X Genomics the R1 read usually contains both the cell barcode (barcode) and UMI. So we specify "R1" for both `--umi_read` and `--cell_barcode_read`, and the positions of both the cell barcode and UMI with the `--read_format` parameter as in the example ("bc:0:15,um:16:27"). Then specify the R1 read in the filename_R1 column of the samplesheet, and the read containing the actual sequence (usually R2) in the filename_R2 column of the samplesheet.

## Important considerations for clonal analysis

An important step in the analysis of AIRR sequencing data is inferring B cell and T cell clones, or clonal groups, sometimes also called clonotypes. These are cells that are derived from the same progenitor cell through clonal expansion. For T cells, this definition is more strict as T cells do not undergo somatic hypermutation, so the TCRs from T cells in the same clone should be identical. For B cells, on the other hand, the BCRs from cells in the same clone can differ due to somatic hypermutation. They also can have a variety of isotypes.

There are two crucial considerations when defining clonal groups with nf-core/airrflow: across which samples should clonal groups be defined, and what should be the clonal threshold, i.e. how different can these receptors be, so that these are assigned to the same clonal group. These are discussed in detail in the following sections.

### Defining clonal groups across samples

Often times we want to analyze clonal groups from the same individual or animal model across time, different conditions or across samples extracted from different tissues. To ensure that the same clone ID (field `clone_id` in the output AIRR rearrangement file) is assigned to the same BCR / TCR clone across these conditions to be able to track the clones, the clonal inference step should be done pulling the sequences from these samples together. This is why, by default, nf-core/airrflow uses the `subject_id` column to group samples prior to defining clonal groups, so it is important to set the exact same subject ID to samples from the same individual across different conditions.

The sample grouping can also be controlled with the [`--cloneby`](https://nf-co.re/airrflow/4.2.0/parameters/#cloneby) parameter, by providing the name of the column containing the group information that should be used to pull the samples together before defining clonal groups (samples or rows with the same string in this column will be grouped together). You can create a new column if you wish for this purpose.

### Clonal inference method

nf-core/airrflow utilizes the Hierarchical clustering method in the [SCOPer](https://scoper.readthedocs.io/) Immcantation tool to infer clonal groups by default, which initially partitions the BCR / TCR sequences according to V gene, J gene and junction length. Then, it defines clonal groups within each partition by performing hierarchical clustering of the sequences within a partition and cutting the clusters according to an automatically detected or user-defined threshold. More details about this method can be found on the respective SCOPer [vignette](https://scoper.readthedocs.io/en/stable/vignettes/Scoper-Vignette/#). Details on how to determine the clonal threshold can be found in the next section.

### Setting a clonal threshold

The clonal threshold can also be customized through the `--clonal_threshold` parameter. The clonal threshold specifies how different two BCRs can be so that are assigned to the same clonal group. The value is specified in length-normalized hamming distance across the BCR junction regions. By default, `--clonal_threshold` is set to be 'auto', allowing the clonal threshold to be determined automatically using a method included in the [SHazaM](https://shazam.readthedocs.io/) Immcantation tool. You can read more details about the method in the SHazaM [vignette](https://shazam.readthedocs.io/en/stable/vignettes/DistToNearest-Vignette/).

For BCR data, we recommend using this default setting initially. After running the pipeline, you can review the automatically calculated threshold in the `find_threshold` report to make sure it is fitting the data appropriately. If the threshold is unsatisfactory, you can re-run the pipeline with a manually specified threshold (e.g. `--clonal_threshold 0.1`) that is appropriate for your data. For a low number of sequences that are insufficient to satisfactorily determine a threshold with this method, we generally recommend a threshold of 0.1 (length-normalized Hamming distance of nearest neighbors) for human BCR data.

Since TCRs do not undergo somatic hypermutation, TCR clones are defined strictly by identical junction regions. For this reason, the `--clonal_threshold` parameter should be set to 0 for TCR data.

### Including BCR lineage tree computation

BCR lineage tree computation is performed using the [Dowser](https://dowser.readthedocs.io/) Immcantation package. This step is skipped by default because it can be time-consuming depending on the size of the input data and the size of the clonal groups. To enable lineage tree computation, add the `--lineage_trees` parameter set to true. You can easily add lineage tree computation to a previous analysis by re-running the pipeline with the `-resume` so all the previous analysis steps are cached and not recomputed.

Dowser supports different methods for the lineage tree computation, `raxml` is the default but you can set other methods with the `--lineage_tree_builder` parameter, and provide the software executable with the `--lineage_tree_exec` parameter.

## Core Nextflow arguments

> [!NOTE]
> These options are part of Nextflow and use a _single_ hyphen (pipeline parameters use a double-hyphen)

### `-profile`

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

> [!IMPORTANT]
> We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility. Conda is not supported for this pipeline.

The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation).

Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important!
They are loaded in sequence, so later profiles can overwrite earlier profiles.

If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer environment.

- `test`
  - A profile with a complete configuration for automated testing
  - Includes links to test data so needs no other parameters
- `docker`
  - A generic configuration profile to be used with [Docker](https://docker.com/)
- `singularity`
  - A generic configuration profile to be used with [Singularity](https://sylabs.io/docs/)
- `podman`
  - A generic configuration profile to be used with [Podman](https://podman.io/)
- `shifter`
  - A generic configuration profile to be used with [Shifter](https://nersc.gitlab.io/development/shifter/how-to-use/)
- `charliecloud`
  - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/)
- `apptainer`
  - A generic configuration profile to be used with [Apptainer](https://apptainer.org/)
- `wave`
  - A generic configuration profile to enable [Wave](https://seqera.io/wave/) containers. Use together with one of the above (requires Nextflow ` 24.03.0-edge` or later).
- `conda`
  - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

### `-resume`

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).

You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names.

### `-c`

Specify the path to a specific config file (this is a core Nextflow command). See the [nf-core website documentation](https://nf-co.re/usage/configuration) for more information.

## Custom configuration

### Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified [here](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L18) it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the [max resources](https://nf-co.re/docs/usage/configuration#max-resources) and [tuning workflow resources](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources) section of the nf-core website.

### Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.

### Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the [customising tool arguments](https://nf-co.re/docs/usage/configuration#customising-tool-arguments) section of the nf-core website.

### nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile.

See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html) for more information about creating your own configuration files.

If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack) on the [`#configs` channel](https://nfcore.slack.com/channels/configs).

## Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow `-bg` flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use `screen` / `tmux` or similar tool to create a detached session which you can log back into at a later time.
Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

## Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~./bash_profile`):

```bash
NXF_OPTS='-Xms1g -Xmx4g'
```
