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yavsap YAVSAP (Yet Another Viral Subspecies Analysis Pipeline)

GitHub Actions CI Status GitHub Actions Linting Status

Nextflow run with conda run with docker run with singularity

Documentation Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. GitHub tag (latest by date) GitHub license

This project follows the semver pro forma and uses the git-flow branching model.

Introduction

yavsap is a bioinformatics best-practice analysis pipeline for identifying and analyzing viral haplotypes in metagenomic NGS reads.

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!

Pipeline summary

  1. Read QC (FastQC/NanoStat)
  2. Read trimming (Trimmomatic/NanoFilt)
  3. Host read filtering (Kraken2+krakentools)
  4. Consensus sequence generation
    1. Reference genome download (entrez-direct)
    2. Read alignment (minimap2)
    3. Variant calling (CliqueSNV/HapLink.jl)
    4. Consensus sequence generation (CliqueSNV/HapLink.jl)
  5. Strain identification (BLAST+)
  6. Variant calling
    1. Read alignment (minimap2)
    2. Variant calling (CliqueSNV/HapLink.jl)
  7. Haplotype calling (CliqueSNV/HapLink.jl)
  8. Phylogenetic tree generation
    1. Multiple sequence alignment (MAFFT)
    2. Maximum-likelihood phylogenetic trees (RAxML-ng)
  9. Output visualization 
flowchart TD
    A([--input]) --> B[Quality analysis]
    A --> C[Quality trimming]
    C --> D[Read classification]
    D --> E[Host read removal]
    E --> F[Alignment]
    F --> G[Consensus Sequence]

    REF.A([--genome]) --> REF.B[(NCBI Download)]
    REF.B --> F

    CL.A([--genome_list]) --> CL.B[(NCBI Download)]
    CL.B --> CL.C[Make BLAST database]

    G --> H[BLAST]
    CL.C --> H

    E --> I[Realignment]
    CL.B --Closest reference--> I
    H --> I

    I --> J[Variant Calling]
    J --> K[Haplotype Calling]
    K --> L[Multiple sequence alignment]
    L --> M[Phylogenetic tree]
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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:

    nextflow run ksumngs/yavsap -profile test,YOUR_PROFILE --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 (YOUR_PROFILE 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.

  4. Start running your own analysis!

    nextflow run ksumngs/yavsap -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input . --outdir <OUTDIR> --platform illumina --kraken2_db https://genome-idx.s3.amazonaws.com/kraken/k2_viral_20210517.tar.gz --keep_taxid classified

Documentation

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

Credits

nf-core/yavsap was originally written by Thomas A. Christensen II, under the supervision of Rachel Palinski at the Kansas State University Veterinary Diagnostic Laboratory.

Contributions and Support

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

Citations

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. In addition, references of tools and data used in this pipeline are as follows:

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