/ontvisc

ONT-based viral screening in plants

Primary LanguageNextflowMIT LicenseMIT

ONTViSc (ONT-based Viral Screening for Biosecurity)

Introduction

eresearchqut/ontvisc is a Nextflow-based bioinformatics pipeline designed to help diagnostics of viruses and viroid pathogens for biosecurity. It takes fastq files generated from either amplicon or whole-genome sequencing using Oxford Nanopore Technologies as input.

The pipeline can either: 1) perform a direct search on the sequenced reads, 2) generate clusters, 3) assemble the reads to generate longer contigs or 4) directly map reads to a known reference.

The reads can optionally be filtered from a plant host before performing downstream analysis.

Pipeline overview

diagram pipeline

  • Data quality check (QC) and preprocessing
    • Merge fastq files (optional)
    • Raw fastq file QC (Nanoplot)
    • Trim adaptors (PoreChop ABI - optional)
    • Filter reads based on length and/or quality (Chopper - optional)
    • Reformat fastq files so read names are trimmed after the first whitespace (bbmap)
    • Processed fastq file QC (if PoreChop and/or Chopper is run) (Nanoplot)
  • Host read filtering
    • Align reads to host reference provided (Minimap2)
    • Extract reads that do not align for downstream analysis (seqtk)
  • QC report
    • Derive read counts recovered pre and post data processing and post host filtering
  • Read classification analysis mode
  • Clustering mode
    • Read clustering (Rattle)
    • Convert fastq to fasta format (seqtk)
    • Cluster scaffolding (Cap3)
    • Megablast homology search against ncbi or custom database (blast)
    • Derive top candidate viral hits
  • De novo assembly mode
    • De novo assembly (Canu or Flye)
    • Megablast homology search against ncbi or custom database or reference (blast)
    • Derive top candidate viral hits
  • Read classification mode
    • Option 1 Nucleotide-based taxonomic classification of reads (Kraken2, Braken)
    • Option 2 Protein-based taxonomic classification of reads (Kaiju, Krona)
    • Option 3 Convert fastq to fasta format (seqtk) and perform direct homology search using megablast (blast)
  • Map to reference mode
    • Align reads to reference fasta file (Minimap2) and derive bam file and alignment statistics (Samtools)

Detailed instructions can be found in wiki.

To do

  • Derive consensus sequence in blast2ref mode
  • Finalise output section wiki documentation
  • Testing docker mode
  • issue with single Chopper params definition requiring a space

Authors

Marie-Emilie Gauthier gauthiem@qut.edu.au
Craig Windell c.windell@qut.edu.au
Magdalena Antczak magdalena.antczak@qcif.edu.au
Roberto Barrero roberto.barrero@qut.edu.au