Once the pod launches, it will present a VS-Code interface and comes with Nextflow, Conda and Docker pre-installed

• To run the pipeline with test data

nextflow run main.nf -profile docker,test

Introduction

nf-core/mycosnp is a bioinformatics best-practice analysis pipeline for MycoSNP is a portable workflow for performing whole genome sequencing analysis of fungal organisms, including Candida auris. This method prepares the reference, performs quality control, and calls variants using a reference. MycoSNP generates several output files that are compatible with downstream analytic tools, such as those for used for phylogenetic tree-building and gene variant annotations..

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.

Pipeline summary

Reference Preparation

Prepares a reference FASTA file for BWA alignment and GATK variant calling by masking repeats in the reference and generating the BWA index.

• Genome repeat identification and masking (nucmer)
• BWA index generation (bwa)
• FAI and DICT file creation (Picard, Samtools)

Sample QC and Processing

Prepares samples (paired-end FASTQ files) for GATK variant calling by aligning the samples to a BWA reference index and ensuring that the BAM files are correctly formatted. This step also provides different quality reports for sample evaluation.

• Combine FASTQ file lanes if they were provided with multiple lanes.
• Filter unpaired reads from FASTQ files (SeqKit).
• Down sample FASTQ files to a desired coverage or sampling rate (SeqTK).
• Trim reads and assess quality (FaQCs).
• Generate a QC report by extracting data from FaQCs report data.
• Align FASTQ reads to a reference (BWA).
• Sort BAM files (SAMTools).
• Mark and remove duplicates in the BAM file (Picard).
• Clean the BAM file (Picard "CleanSam").
• Fix mate information in the BAM file (Picard "FixMateInformation").
• Add read groups to the BAM file (Picard "AddOrReplaceReadGroups").
• Index the BAM file (SAMTools).
• FastQC - Filtered reads QC.
• Qualimap mapping quality report.
• MultiQC - Aggregate report describing results and QC from the whole pipeline

Variant calling and analysis

Calls variants and generates a multi-FASTA file and phylogeny.

• Call variants (GATK HaplotypeCaller).
• Combine gVCF files from the HaplotypeCaller into a single VCF (GATK CombineGVCFs).
• Call genotypes using the (GATK GenotypeGVCFs).
• Filter the variants (GATK VariantFiltration) [default (but customizable) filter: 'QD < 2.0 || FS > 60.0 || MQ < 40.0 || DP < 10'].
• Run a customized VCF filtering script (Broad Institute).
• Split the filtered VCF file by sample.
• Select only SNPs from the VCF files (GATK SelectVariants).
• Split the VCF file with SNPs by sample.
• Create a consensus sequence for each sample (BCFTools, SeqTK).
• Create a multi-fasta file from the VCF SNP positions using a custom script (Broad).
• Create phylogeny from multi-fasta file (rapidNJ, FastTree2, RaxML, IQTree)

Quick Start

1. Install Nextflow (>=21.10.3)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

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

   nextflow run CDCgov/mycosnp-nf -profile test,YOURPROFILE

   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 and are persistently observing issues downloading Singularity images directly due to timeout or network issues, then you can use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, you can 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.

4. Start running your own analysis!

   nextflow run CDCgov/mycosnp-nf -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.csv --fasta c_auris.fasta

Documentation

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

Credits

nf-core/mycosnp was originally written by CDC.

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

• Michael Cipriano @mjcipriano
• Sateesh Peri @sateeshperi
• Hunter Seabolt @hseabolt
• Chris Sandlin @cssandlin
• Drewry Morris @drewry
• Lynn Dotrang @leuthrasp
• Christopher Jossart @cjjossart
• Robert A. Petit III @rpetit3

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.

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.

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