Influenza Typing Workflow

Influenza A&B typing and analysis from Nanopore data.

Introduction

Influenza is a single-stranded RNA virus and contains a 13.5-14.5kb genome which is split into 8 segments encoding 10-14 proteins (dependent on strain).

The virus is classified using two proteins found on the outer surface of the viral capsid. You’ve probably heard of H1N1 Influenza for example. The H represents hemagglutinin and the N is neuraminidase.

This analysis workflow can be used with Oxford Nanopore Technologies sequencing data from amplified segments of the Influenza Type A and Type B genomes, to determine the most likely strain of Influenza to which the sequenced sample belongs.

Compute requirements

Recommended requirements:

• CPUs = 32
• Memory = 32GB

Minimum requirements:

• CPUs = 4
• Memory = 2GB

Approximate run time: 30 minutes when number of cores >= samples

ARM processor support: False

Install and run

These are instructions to install and run the workflow on command line. You can also access the workflow via the EPI2ME application.

The workflow uses nextflow to manage compute and software resources, therefore nextflow will need to be installed before attempting to run the workflow.

The workflow can currently be run using either Docker or singularity to provide isolation of the required software. Both methods are automated out-of-the-box provided either docker or singularity is installed. This is controlled by the -profile parameter as exemplified below.

It is not required to clone or download the git repository in order to run the workflow. More information on running EPI2ME workflows can be found on our website.

The following command can be used to obtain the workflow. This will pull the repository in to the assets folder of nextflow and provide a list of all parameters available for the workflow as well as an example command:

nextflow run epi2me-labs/wf-flu -help

A demo dataset is provided for testing of the workflow. It can be downloaded using:

wget https://ont-exd-int-s3-euwst1-epi2me-labs.s3.amazonaws.com/wf-flu/wf-flu-demo.tar.gz
tar -xzvf wf-flu-demo.tar.gz

The workflow can be run with the demo data using:

nextflow run epi2me-labs/wf-flu \
--fastq test_data/fastq -profile standard

For further information about running a workflow on the cmd line see https://labs.epi2me.io/wfquickstart/

Related protocols

This workflow is designed to take input sequences that have been produced from Oxford Nanopore Technologies devices using this protocol: (https://community.nanoporetech.com/docs/prepare/library_prep_protocols/ligation-sequencing-influenza-whole-genome) Samples not prepared with this protocol may work sub-optimally or fail to complete succesfully.

Input example

This workflow accepts FASTQ files as input.

The FASTQ input parameters for this workflow accept one of three cases: (i) the path to a single FASTQ file; (ii) the path to a top-level directory containing FASTQ files; (iii) the path to a directory containing one level of sub-directories which in turn contain FASTQ files. In the first and second cases (i and ii), a sample name can be supplied with --sample. In the last case (iii), the data is assumed to be multiplexed with the names of the sub-directories as barcodes. In this case, a sample sheet can be provided with --sample_sheet.

(i)                     (ii)                 (iii)    
input_reads.fastq   ─── input_directory  ─── input_directory
                        ├── reads0.fastq     ├── barcode01
                        └── reads1.fastq     │   ├── reads0.fastq
                                             │   └── reads1.fastq
                                             ├── barcode02
                                             │   ├── reads0.fastq
                                             │   ├── reads1.fastq
                                             │   └── reads2.fastq
                                             └── barcode03
                                              └── reads0.fastq

Input parameters

Input Options

Advanced Options

Miscellaneous Options

Outputs

Output files may be aggregated including information for all samples or provided per sample. Per-sample files will be prefixed with respective aliases and represented below as {{ alias }}.

Pipeline overview

1. Concatenate reads and filter out short reads < 200 bases long
2. Align reads to reference with minimap2
3. Coverage calculations with samtools)
4. Call variants using medaka (medaka blog)
5. Make a (coverage masked) consensus with bcftools
6. Typing using abricate with the insaflu database, containing the following sequences:

7. Clade and lineage assignment using nextclade

Troubleshooting

• If the workflow fails please run it with the demo data set to ensure the workflow itself is working. This will help us determine if the issue is related to the environment, input parameters or a bug.
• See how to interpret some common nextflow exit codes here.

FAQ’s

If your question is not answered here, please report any issues or suggestions on the github issues page or start a discussion on the community.

Why does the workflow fail, or the report shows very low coverage?

This can happen when users use the workflow on data that has been generate using the RBK protocol instead of the recomended Influenza whole-genome protocol, as a result of RBK’s shorter read lengths. Ensure the —rbk flag has been set to prevent over-filtering of reads.

Related blog posts

• Importing third-party workflows into EPI2ME Labs

See the EPI2ME website for lots of other resources and blog posts.