pyigmap

Introduction

pyIgMap is a Nextflow-driven and Python-based pipeline for extracting and summarizing antigen receptor gene rearrangements from sequencing data.

Note

The pipeline is built upon open source components commonly used for AIRR-seq data processing. The output is provided in AIRR format enabling downstream analysis with AIRR-compliant software such as Immcantation.

Quick start

1. This pipeline requires Docker or Podman, Java 11 (or later, up to 21), Bash and Make tool.

Note

To install the Make tool execute: sudo apt install make

2. Clone the repository and go inside:

git clone https://github.com/BostonGene/pyigmap.git
cd pyigmap

Tip

If you have Ubuntu 20.10 (amd64) or higher, you can install requirements above using:
make install-docker or make install-podman
make install-java

3. Install Nextflow, build V(D)J references and Docker container images with a single command:

make

Tip

To use a Podman as a container engine, run: make ENGINE=podman

4. Start running your own analysis!

./pyigmap -profile <docker/podman> \
    --library <amplicon/rnaseq> \
    --fq1 "R1.fastq.gz" \
    --fq2 "R2.fastq.gz" \
    --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.

Pipeline summary

pyigmap allows the processing of raw BCR/TCR sequencing data from bulk and targeted sequencing protocols. For more details on the supported protocols, please refer to the usage documentation.

1. FASTQ pre-processing

• RNASeq-bulk:

  • Merging overlapping reads, joining non-overlapping reads with a selected insert size, and raw read quality control (Fastp).

• AIRR-Seq (target):

  • Extracting the UMI from the reads (PyUMI).
  • Alignment-free clustering of UMI tagged reads with subsequent consensus generation (Calib).
  • Merging overlapping reads, saving not-overlapping reads, and raw read quality control (Fastp).

2. V(D)J mapping

• RNASeq-bulk:

  • Fast detecting V(D)J junctions from FASTQ reads using a seed-based heuristic without initial alignment to database germline sequences (Vidjil).
  • Mapping previously identified junctions against IMGT reference and producing AIRR-formatted table (IgBLAST).

• AIRR-Seq (target):

  • Mapping FASTQ reads against IMGT reference and producing AIRR-formatted table (IgBLAST).

3. Cleansing and aggregating clonotypes

• RNASeq-bulk and AIRR-Seq (target):

  • Filter out chimeric clonotypes, that have different locus in V-/J-segments (except for TRA and TRD).
  • Store the best aligned V, D, J and C genes call.
  • Discard clonotypes with undefined nucleotide or amino acid in CDR3 sequence.
  • Aggregate clonotypes based on Levenstein distance of 1 and read count ratio and subsequent duplicate_count column calculation.

• Only RNASeq-bulk:

  • Compute generation probabilities (pgen column) of CDR3 amino acid sequences and remove clonotypes with pgen values less than or equal to the selected threshold (OLGA).
  • Store clonotypes with the most weighted and frequent C-gene call and V-gene alignment call.

Usage

A typical command to run the pipeline from RNASeq-bulk sequencing data is:

./pyigmap -profile <docker/podman> \
    --library rnaseq \
    --fq1 "R1.fastq.gz" \
    --fq2 "R2.fastq.gz" \
    --outdir "./results"

For common AIRR-Seq targeted sequencing protocols we provide pre-set parameters, including a parameter for specifying a UMI barcode pattern.

Here is an example command to process the data from the AIRR-Seq targeted protocol, where there is a 19-base pair UMI located between two adapters in the reverse FASTQ file:

./pyigmap -profile <docker/podman> \
    --library amplicon \
    --fq1 "R1.fastq.gz" \
    --fq2 "R2.fastq.gz" \
    --fq2_pattern "^TGGTATCAACGCAGAGTAC(UMI:N{19})TCTTGGGGG" \
    --outdir "./results"

You can also use public data from these databases by using a sample ID: GEO, SRA, EMBL-EBI, DDBJ, NIH Biosample and ENCODE:

./pyigmap -profile <docker/podman> \
    --library rnaseq \
    --sample_id SRR3743469 \
    --outdir "./results"

Alternatively, you can provide an HTTP/HTTPS/FTP link to your FASTQ files.

./pyigmap -profile <docker/podman> \
    --library amplicon \
    --fq1 https://zenodo.org/records/11103555/files/fmba_TRAB_R1.fastq.gz \
    --fq2 https://zenodo.org/records/11103555/files/fmba_TRAB_R2.fastq.gz \
    --outdir "./results"

Benchmark datasets

Datasets for testing data processing of various AIRR-seq protocols can be found here: 10.5281/zenodo.11103555

Contributing

Contributions are more than welcome. See the CONTRIBUTING.md file for details.

Citations

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

License

Pyigmap is GNU GPL3 licensed.