nf-core/hgtseq is a bioinformatics best-practice analysis pipeline built to investigate horizontal gene transfer from NGS data.

The pipeline uses metagenomic classification of paired-read alignments against a reference genome to identify the presence of non-host microbial sequences within read pairs, and to infer potential integration sites into the host genome.

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.

Functionality Overview

A graphical view of the pipeline can be seen below.

nf-core/circdna metromap

Pipeline summary

  1. Read QC (FastQC)
  2. Present QC for raw reads (MultiQC)
  3. Adapter and quality trimming (Trim Galore)
  4. Mapping reads using BWA (BWA)
  5. Sort and index alignments, extraction reads by sam flag and conversion to fastq format(SAMtools)
  6. Taxonomic classification (Kraken2)
  7. Plotting Kraken2 results (Krona)
  8. Html analysis report (RMarkDown)

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:

    • FASTQ input:
       nextflow run nf-core/hgtseq -profile test,YOURPROFILE --outdir <OUTDIR>
    • BAM input:
       nextflow run nf-core/hgtseq -profile test_bam,YOURPROFILE --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.
  1. Start running your own analysis!

    nextflow run nf-core/hgtseq \
    --input <YOURINPUT>.csv \
    --outdir <OUTDIR> \
    --genome GRCh38 \
    --taxonomy_id "TAXID" \
    -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \
    --krakendb /path/to/kraken_db \
    --kronadb /path/to/krona_db/


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


nf-core/hgtseq was originally written by Simone Carpanzano, Francesco Lescai.

We thank nf-core community, and in particular the authors of the modules used in the pipeline: Paolo Cozzi, Jose Espinosa-Carrasco, Phil Ewels, Gisela Gabernet, Maxime Garcia, Jeremy Guntoro, Friederike Hanssen, Matthias Hortenhuber, Patrick Hüther, Suzanne Jin, Felix Krueger, Harshil Patel, Alex Peltzer, Abhinav Sharma, Gregor Sturm, James Fellows Yates.

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 #hgtseq channel (you can join with this invite).


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.