Sign inSign up

microbialgenomics/viralrecon

By microbialgenomics

•Updated about 5 years ago

Docker Hub Images for ViralRecon NF-Pipeline

Image
0

10K+

microbialgenomics/viralrecon repository overview

⁠MicrobialGenomics/viralrecon

⁠Forked from nf-core/viralrecon⁠

GitHub Actions CI Status Amplicon Protocol GitHub Actions CI Status Metagen Protocol Actions Status

Docker install with bioconda

⁠Pipeline additions

  1. Adapter trimming with Trimmomatic⁠
  2. Codon Frequency with CodFrq⁠
  3. Custom consensus with consensusSequence_v2.py⁠

⁠Introduction

nfcore/viralrecon is a bioinformatics analysis pipeline used to perform assembly and intra-host/low-frequency variant calling for viral samples. The pipeline supports short-read Illumina sequencing data from both shotgun (e.g. sequencing directly from clinical samples) and enrichment-based library preparation methods (e.g. amplicon-based: ARTIC SARS-CoV-2 enrichment protocol⁠; or probe-capture-based).

The pipeline is built using Nextflow⁠, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with Docker containers making installation trivial and results highly reproducible. Furthermore, automated continuous integration tests that run the pipeline on a full-sized dataset using AWS cloud ensure that the code is stable.

⁠Pipeline summary

  1. Download samples via SRA, ENA or GEO ids (ENA FTP⁠, parallel-fastq-dump⁠; if required)
  2. Merge re-sequenced FastQ files (cat⁠; if required)
  3. Read QC (FastQC⁠)
  4. Adapter trimming (fastp⁠ or trimmomatic⁠)
  5. Variant calling
    1. Read alignment (Bowtie 2⁠)
    2. Sort and index alignments (SAMtools⁠)
    3. Primer sequence removal (iVar⁠; amplicon data only)
    4. Duplicate read marking (picard⁠; removal optional)
    5. Alignment-level QC (picard⁠, SAMtools⁠)
    6. Genome-wide and amplicon coverage QC plots (mosdepth⁠)
    7. Choice of multiple variant calling and consensus sequence generation routes (VarScan 2⁠, BCFTools⁠, BEDTools⁠ || iVar variants and consensus⁠ || BCFTools⁠, BEDTools⁠)
    8. Intersect variants across callers (BCFTools⁠)
    9. Custom consensus (consensusSequence_v2.py⁠)
    10. Codon frequency calling (codfrq⁠)
  6. De novo assembly
    1. Primer trimming (Cutadapt⁠; amplicon data only)
    2. Removal of host reads (Kraken 2⁠)
    3. Choice of multiple assembly tools (SPAdes⁠ || metaSPAdes⁠ || Unicycler⁠ || minia⁠)
  7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC⁠)

NB: The pipeline has a number of options to allow you to run only specific aspects of the workflow if you so wish. For example, you can skip all of the assembly steps with the --skip_assembly parameter. See the usage docs⁠ for all of the available options when running the pipeline.

⁠Pipeline reporting

Numerous QC and reporting steps are included in the pipeline in order to collate a full summary of the analysis within a single MultiQC⁠ report. You can see an example MultiQC report here⁠, generated using the parameters defined in this configuration file⁠. The pipeline was run with these samples⁠, prepared from the ncov-2019 ARTIC Network V1 amplicon set⁠ and sequenced on the Illumina MiSeq platform in 301bp paired-end format.

⁠Quick Start

  1. Install nextflow⁠

  2. Install Docker⁠ 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 MicrobialGenomics/viralrecon -profile test,<docker/conda>

  4. Start running your own analysis!

    • Typical command for shotgun analysis:

      nextflow run  MicrobialGenomics/viralrecon \
    --input samplesheet.csv \
    --genome 'MN908947.3' \
    -profile <docker/conda>

    • Typical command for amplicon analysis:

      nextflow run  MicrobialGenomics/viralrecon \
    --input samplesheet.csv \
    --genome 'MN908947.3' \
    --protocol amplicon \
    --amplicon_bed ./nCoV-2019.artic.V3.bed \
    --skip_assembly \
    -profile <docker/conda>

See the usage documentation⁠ for all of the available options when running the pipeline.

⁠Documentation

The MicrobialGenomics/viralrecon pipeline comes with documentation about the pipeline, found in the docs/ directory:

  1. Installation⁠
  2. Pipeline configuration
  3. Running the pipeline⁠
  4. Output and how to interpret the results⁠
  5. Troubleshooting⁠

Tag summary

Content type

Image

Digest

Size

2 GB

Last updated

about 5 years ago

docker pull microbialgenomics/viralrecon