Introduction

Microbial Genome Analysis Pipeline

MGAP is a bioinformatics best-practice analysis pipeline for Microbial Genome Analysis Pipeline.

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!

This pipeline is divided in two main steps, genome assembly and genome annotation.

Genome assembly

The genome assembly workflow allows the read processing and assembly of Illumina and Oxford Nanopore data. For each sequencing technology, a different set of tools are used:

Illumina assembly

1. Read QC, clean, and filter reads. (FastP)
2. If requested, calculate coverage of genome (Mash) and reduce coverage (Seqtk.
3. If requested, check the reads with Kraken2 against a standard (small) database to evaluate possible contaminations (Kraken2)
4. Genome assembly with SPADES

ONT reads

1. Read QC with NANOQ
2. Adapter removal with PORECHOP_ABI
3. If requested, calculate coverage of genome (Mash) and reduce coverage (Seqtk
4. Genome assembly using FLYE
5. Genome polishing using MEDAKA
6. Genome reorientation using Dnaapler (skipped at the moment, due to errors when the markers are not present)

Genome annotation

With the assembled genome, the annotation steps include:

1. MLST analysis (MLST)
2. Annotation (BAKTA)
3. Antibiotic resistance prediction using AMRFinderPlus. If the organism is on the list provided by AMRFinderPlus, also point mutations are evaluated (AMRFinderPLus)
4. Prophage and plasmid search using Genomad
5. For Klebsiella, evaluation of the genome using Kleborate
6. For S. aureus, SCCmec classification using staphophia-sccmec

Quick Start

1. Install Nextflow (>=22.10.1)

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).

Currently the pipeline has been tested with Docker, but should work without issues with Singularity or Conda.

1. Download the pipeline, either cloning the repository or downloading the zip file

Running the pipeline

Sample sheet file

First you need to create a Samplesheet file, that contain the name of the samples and the location of the reads. This is an example of this file:

Illumina reads

sample,fastq_1,fastq_2 SCL10095,/home/jugalde/germlab/data/S190_R1.fastq.gz,/home/jugalde/germlab/data/S190_R2.fastq.gz

The file always has to include the header sample,fastq_1,fastq_2

ONT reads

sample,fastq_1 C19013,/home/jugalde/germlab/data/C19013.fastq.gz

The file always has to include the header sample,fastq_1,fastq_2

Pipeline parameters

The file nextflow.config contains all the parameteres used by the pipeline, including path to database files. Currently the path work in our server (Arrakis), but if you are running elsewhere, these need to be updated. Later a section on how to download and prepare the DBs needed for the analysis, will be added to this Readme file.

Starting the pipeline

Once you have everything ready, you can run the pipeline by calling the name of the repository, and adding the appropiate parameters. For example:

bash nextflow run gene2dis-mgap --input <SAMPLESHEET> --outdir <OUTDIR> -profile docker -seq_type illumina|ont

Here the parameters are: - gene2dis-mgap: The name of the repository. This could be the path to a location on the computer (e.g. /home/jugalde/pipelines/gene2dis-mgap). - --input: The samplesheet file - --outdir: The output directory for the output of the pipeline - -profile: either docker or conda (not tested yet) - --seq_type: sequencing technology used, either illumina or ont

*Notice that the pipeline parameters have two dashes (--), while parameters that are for nextflow only have one (-).

Credits

gene2dis/mgap was originally written by the Microbial Data Science Lab, Center for Bioinformatics and Integrative Biology, Universidad Andres Bello. Its development was led by Juan A. Ugalde

We thank the following people for their extensive assistance in the development of this pipeline:

• Juan A. Ugalde

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

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

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

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.