joon-klaps / viralgenie

Detect iSNV and construct whole viral genomes from metagenomic samples

Tip

Make sure to checkout the viralgenie website for more elaborate documentation!

Introduction

Viralgenie is a bioinformatics best-practice analysis pipeline for reconstructing consensus genomes and to identify intra-host variants from metagenomic sequencing data or enriched based sequencing data like hybrid capture.

Pipeline summary

1. Read QC (FastQC)
2. Performs optional read pre-processing
   • Adapter trimming(fastp, Trimmomatic)
   • Read UMI deduplication (HUMID)
   • Low complexity and quality filtering (bbduk)
   • Host-read removal (BowTie2)
3. Metagenomic diveristy mapping
   • Performs taxonomic classification and/or profiling using one or more of:
     • Kraken2
     • Bracken[optional]
     • Kaiju
   • Plotting Kraken2 and Kaiju (Krona)
4. Denovo assembly (SPAdes, TRINITY, megahit), combine contigs.
5. [Optional] extend the contigs with sspace_basic
6. Contig reference idententification (blastn)
   • Identify top 5 blast hits
   • Merge blast hit and all contigs of a sample
7. [Optional] Precluster contigs based on taxonomy
   • Identify taxonomy Kraken2 and\or Kaiju
   • Resolve potential inconsistencies in taxonomy & taxon filtering | simplification bin/extract_precluster.py
8. Cluster contigs (or every taxonomic bin) of samples, options are:
   • cdhitest
   • vsearch
   • mmseqs-linclust
   • mmseqs-cluster
   • vRhyme
   • Mash
9. Scaffolding of contigs to centroid (Minimap2, iVar-consensus)
10. [Optional] Annotate 0-depth regions with external reference bin/lowcov_to_reference.py.
11. [Optional] Select best reference from --mapping_constrains:
    • Mash sketch
    • Mash screen
12. Mapping filtered reads to supercontig and mapping constrains(BowTie2,BWAmem2 and BWA)
13. [Optional] Deduplicate reads (Picard or if UMI's are used UMI-tools)
14. Variant calling and filtering (BCFTools,iVar)
15. Create consensus genome (BCFTools,iVar)
16. Repeat step 12-15 multiple times for the denovo contig route
17. Consensus evaluation and annotation (QUAST,CheckV,blastn, mmseqs-search)
18. Result summary visualisation for raw read, alignment, assembly, variant calling and consensus calling results (MultiQC)

Usage

!!! Note If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

Now, you can run the pipeline using:

nextflow run Joon-Klaps/viralgenie \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

!!! Warning Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Credits

Viralgenie was originally written by Joon-Klaps.

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

• Philippe Lemey
• Liana Kafetzopoulou
• nf-core community

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.