susCovONT

Pipeline to generate consensus.fasta files and identify pangolin lineage and nextstrain clade of Sars-CoV-2 genomes from ONT sequencing.

• Updates

This pipeline takes as input a folder with name <run_name> which contains the folders fast5_pass and fastq_pass and sequencing_summary*.txt from Sars-CoV-2 ONT sequencing together with a CSV-file which links barcode and sample name, and it outputs consensus.fasta files along with <run_name>_report.csv which includes pangolin lineage, nextstrain clade, mutations and QC.

Installation

Install all necessary tools and environments with susCovONT/scripts/install.sh. You need to set the path of INSTALL_DIR, which is where the repositories will be installed (including this one), and conda and docker has to be installed:

INSTALL_DIR=/home/susamr/Programs/  #Change to your install dir
cd $INSTALL_DIR
git clone https://github.com/marithetland/susCovONT 
bash ./susCovONT/scripts/install.sh $INSTALL_DIR

Basic usage

python susCovONT.py --input_dir /path/to/<run_name> --sample_names sample_names.csv

Where:

• --input_dir: Input directory <run_name> must contain fast5_pass and fastq_pass folders and sequencing_summary*.txt, with the <run_name> corresponding to your run (e.g. 20210213_1359_X5_FAO88697_5cf6e6f0)
• --sample_names: A CSV-file which connects barcodes with sample names, following the format:

barcode,sample_name
barcode01,NEGCONTROL
barcode02,E1234567_P1
NB03,V2345678_P1

The barcode column can take values following the format barcode[0-9][0-9] or NB[0-9][0-9] (as in the example above), and the sample_name column can be anything you'd like.

Note: Basecalling and demultiplexing may also be performed if not already done on GridION/MinIT.

Please see the wiki for more information:

• What does the pipeline do?
• How to run
• Installation
• QC parameters and how to change these
• What does the output look like
• How to run the commands manually

And as an extra bonus:

• Setting up MinKNOW and RAMPART
• Further exploration using the Nexctlade Web Application

Note and thanks

Please note that this script was created for use at Stavanger University Hospital, you may need to change it (specifically the scripts/config.cfg file) for it to work in your environment.

This pipeline uses tools from the Artic network's nCoV-2019 novel coronavirus bioinformatics protocol. See also the QC and parameters page and further links here.

Many thanks to the artic, pangolin and nextclade developers for creating the protocols and pipelines!

Repo name

This pipeline was created for the analysis of Sars-CoV-2 data from Oxford Nanopore Technologies (ONT) sequencing at Stavanger University Hospital (SUH/SUS). Hence the name, susCovONT: SUS + Covid-19 + ONT.

Updates

• 2021-10-14: Added option for V3 or V4 primer schemes, default is now V4. Updated nextclade. Fixed output report.
• 2021-04-01: Combined the guppy basecalling and demultiplexing commands to match command (and output file structure) used by GridION
• 2021-03-31: Updated QC thresholds (see QC and parameters) and added option for setting the --normalise value (default is --normalise 500) and also to automatically re-analyse samples that have 90-97% coverage without normalisng (--renormalise=on)
• 2021-03-12: Updated the threshold for QC PASS from >90% of bases confidently called (with 20X reads) to >97%, based on GISAID and FHI's recommendations.
• ToDo: Merge guppy basecalling and demultiplexing commands to one to use same structure as ONT devices
• ToDo: Considering increasing the normalise value and/or using medaka in workflow instead of nanopolish