nf-core/viralmetagenome

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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, prinseq++)
   • Host-read removal (BowTie2)
3. Metagenomic diversity 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 and filter with prinseq++
6. [Optional] Map reads to contigs for coverage estimation (BowTie2,BWAmem2 and BWA)
7. Contig reference idententification (blastn)
   • Identify top 5 blast hits
   • Merge blast hit and all contigs of a sample
8. [Optional] Precluster contigs based on taxonomy
   • Identify taxonomy Kraken2 and\or Kaiju
   • Resolve potential inconsistencies in taxonomy & taxon filtering | simplification bin/extract_precluster.py
9. Cluster contigs (or every taxonomic bin) of samples, options are:
   • cdhitest
   • vsearch
   • mmseqs-linclust
   • mmseqs-cluster
   • vRhyme
   • Mash
10. [Optional] Remove clusters with low read coverage. bin/extract_clusters.py
11. Scaffolding of contigs to centroid (Minimap2, iVar-consensus)
12. [Optional] Annotate 0-depth regions with external reference bin/nocov_to_reference.py.
13. [Optional] Select best reference from --mapping_constraints:
    • Mash sketch
    • Mash screen
14. Mapping filtered reads to supercontig and mapping constraints(BowTie2,BWAmem2 and BWA)
15. [Optional] Deduplicate reads (Picard or if UMI’s are used UMI-tools)
16. Variant calling and filtering (BCFTools,iVar)
17. Create consensus genome (BCFTools,iVar)
18. Repeat step 12-15 multiple times for the denovo contig route
19. Consensus evaluation and annotation (QUAST,CheckV,blastn,prokka mmseqs-search, MAFFT - alignment of contigs vs iterations & consensus)
20. Result summary visualisation for raw read, alignment, assembly, variant calling and consensus calling results (MultiQC)

Usage

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1,fastq_2
sample1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
sample2,AEG588A5_S5_L003_R1_001.fastq.gz,
sample3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz

Each row represents a fastq file (single-end) or a pair of fastq files (paired end).

Now, you can run the pipeline using:

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

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

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output 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.