nf-core/fluseq :sneezing_face:

Introduction

This pipeline processes FASTQ files from Nanopore sequencing of Influenza A and B, generating consensus sequences and analyzing them for mutations, sequencing statistics, and drug resistance effects. The main steps include:

• Alignment and consensus sequencing with IRMA.
• Consensus sequence analysis with Nextclade.
• Mutation calling for all segments.
• Generation of a comprehensive report in CSV format.
• Output of sequences in a multiple FASTA file.

The pipeline consist of four different worflows listed bellow:

1) Human Influenza FASTQ analysis (human) Alignment of FASTQ and mutation analysis 2) Human Influenza FASTA analysis (human-fasta) (under development) Mutation analysis 3) Avian Influenza FASTQ analysis (avian) Alignment of FASTQ and mutation analysis 4) Avian Influenza FASTA analysis (avian-fasta) Mutation analysis

Compatibility

• Operating System: Linux
• Dependencies: Docker and Nextflow

Usage

Sample Sheet Preparation

Prepare a sample sheet (CSV or TSV*) in the assets folder with the following format: * TSV file is not not compulsory

PCR-PlatePosition,SequenceID,Barcode,KonsCt A1*,sampleID,barcodeID,ct-value* *not compulsory

Each row lists a sample to be analyzed. Samples not listed in the sheet will be excluded from the analysis.

Directory Structure

For FASTQ-analysis

Ensure your directory structure is as follows:

./ |-data |-barcode3 |-XXXX_pass_barcode03_XXXX.fastq.gz |-YYYY_pass_barcode03_YYYY.fastq.gz |-nf-core-fluseq |-assets |-samplesheet.csv |-samplesheet.tsv |-...

Running the Pipeline

Navigate to the nf-core-fluseq folder and execute the following command with default parameters:

Human Influenza FASTQ analysis

bash nextflow run main.nf -profile docker --runid runid_name --outdir ../outdir_name

Avian Influenza FASTQ analysis

bash nextflow run main.nf -profile docker --file avian-fastq --genotype_database database* --runid runid_name --outdir ../outdir_name * The database given as the genotyping database must be in the format given bellow: ```

DatabaseNumber|Subtype|ID|Segment|SegmentNumber|GISAIDID aa.. DatabaseNumber|Subtype|ID|Segment|SegmentNumber|GISAIDID a.. ```

Example of header: ```

21|H5N8|chicken/norway|HA|4|EPIESL_7473825 ```

The database number is used in the genotyping02.py script to identify genotypes. Either the offical database has to be obtained or this script has to be adjusted to a be compatible to a in-house genotyping database.

Avian Influenza FASTA analysis

bash nextflow run main.nf -profile docker --file avian-fasta --genotype_database database --runid runid_name --outdir ../outdir_name

Important Parameters

• --input (default: assets/samplesheet.csv): Path to the samplesheet.
• --seq_quality_threshold (default: 20): Coverage threshold for analysis of consensus sequences.
• --samplesDir (default: ../data): Directory containing the FASTQ files in the structure given above.

All parameters are detailed in the nextflow.config file.

Pipeline Output

The output includes:

• Consensus sequences.
• Mutation calls.
• Sequencing statistics (coverage, quality parameters).
• Drug resistance effects.
• A report in CSV format.
• A multiple FASTA file of sequences that passed quality filters.

Credits

fluseq was originally written by Rasmus Kopperud Riis.