# ![nf-core/hgtseq](docs/images/nf-core-hgtseq_logo_light.png#gh-light-mode-only) ![nf-core/hgtseq](docs/images/nf-core-hgtseq_logo_dark.png#gh-dark-mode-only)

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## Introduction

**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](https://www.nextflow.io), 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](https://www.nextflow.io/docs/latest/dsl2.html) 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](https://github.com/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](https://nf-co.re/hgtseq/results).

## Functionality Overview

A graphical view of the pipeline can be seen below.

<p align="center">
<img src="docs/images/hgtseq_pipeline_metromap.png" alt="nf-core/circdna metromap" width="70%">
</p>

## Pipeline summary

1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))
2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
3. Adapter and quality trimming ([`Trim Galore`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/))
4. Mapping reads using BWA ([`BWA`](http://bio-bwa.sourceforge.net))
5. Sort and index alignments, extraction reads by sam flag and conversion to fastq format([`SAMtools`](https://www.htslib.org))
6. Taxonomic classification ([`Kraken2`](https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown))
7. Plotting Kraken2 results ([`Krona`](https://hpc.nih.gov/apps/kronatools.html))
8. Html analysis report ([`RMarkDown`](https://rmarkdown.rstudio.com))

## Quick Start

1. Install [`Nextflow`](https://www.nextflow.io/docs/latest/getstarted.html#installation) (`>=21.10.3`)

2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) (you can follow [this tutorial](https://singularity-tutorial.github.io/01-installation/)), [`Podman`](https://podman.io/), [`Shifter`](https://nersc.gitlab.io/development/shifter/how-to-use/) or [`Charliecloud`](https://hpc.github.io/charliecloud/) for full pipeline reproducibility _(you can use [`Conda`](https://conda.io/miniconda.html) both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_.

3. Download the pipeline and test it on a minimal dataset with a single command:

   - FASTQ input:

   ```console
      nextflow run nf-core/hgtseq -profile test,YOURPROFILE --outdir <OUTDIR>
   ```

   - BAM input:

   ```console
      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](https://github.com/nf-core/configs#documentation) 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`](https://nf-co.re/tools/#downloading-pipelines-for-offline-use) command to download images first, before running the pipeline. Setting the [`NXF_SINGULARITY_CACHEDIR` or `singularity.cacheDir`](https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub) 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`](https://www.nextflow.io/docs/latest/conda.html) settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   ```console
   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/taxonomy.tab
   ```

## Documentation

The nf-core/hgtseq pipeline comes with documentation about the pipeline [usage](https://nf-co.re/hgtseq/usage), [parameters](https://nf-co.re/hgtseq/parameters) and [output](https://nf-co.re/hgtseq/output).

## Credits

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](.github/CONTRIBUTING.md).

For further information or help, don't hesitate to get in touch on the [Slack `#hgtseq` channel](https://nfcore.slack.com/channels/hgtseq) (you can join with [this invite](https://nf-co.re/join/slack)).

## Citations

<!-- nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->
<!-- If you use  nf-core/hgtseq for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->

An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](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](https://dx.doi.org/10.1038/s41587-020-0439-x).
