usage.md

nf-flu: Usage

Introduction

--input samplesheet

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a tab-delimited (TSV) or comma-separated (CSV) file with 2 or 3 columns, for Nanopore or Illumina sequence data, respectively, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Illumina samplesheet

The sample identifiers have to be the same when you have re-sequenced the same sample more than once (e.g. to increase sequencing depth). The pipeline will concatenate the raw reads before performing any downstream analysis.

Example samplesheet where SAMPLE_1 was sequenced twice with Illumina paired-end sequencing and SAMPLE_2 was sequenced once with Illumina single-end sequencing.

sample,fastq_1,fastq_2
SAMPLE_1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
SAMPLE_1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
SAMPLE_2,AEG588A2_S4_L003_R1_001.fastq.gz,

Column	Description
sample	Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample.
fastq_1	Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".
fastq_2	Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".

Generate a samplesheet from a FASTQ directory

You can generate an input samplesheet from a directory containing Illumina FASTQ files (e.g. /path/to/illumina_run/Data/Intensities/Basecalls/) with the included Python script fastq_dir_to_samplesheet.py before you run the pipeline (requires Python 3 installed locally) e.g.

~/.nextflow/assets/CFIA-NCFAD/nf-flu/bin/fastq_dir_to_samplesheet.py \
  -i /path/to/illumina_run/Data/Intensities/Basecalls/ \
  -o samplesheet.csv

Nanopore samplesheet

The samplesheet for Nanopore sequencing data analysis must be either a comma-separated (CSV) or tab-delimited (TSV) file with 2 columns and a header with column names (names don't matter).

The sample identifiers have to be the same when you have re-sequenced the same sample more than once (e.g. to increase sequencing depth). The pipeline will concatenate the raw reads before performing any downstream analysis.

Example Nanopore samplesheet:

sample,reads
SAMPLE_1,/path/to/run1/fastq_pass/barcode01
SAMPLE_1,/path/to/ANOTHER_RUN/fastq_pass/barcode09
SAMPLE_1,/path/to/sample1.fastq.gz
SAMPLE_2,/path/to/run2/fastq_pass/barcode02

• NOTE: SAMPLE_1 has 3 entries

Column	Description
sample	Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample.
reads	Full path to FASTQ file or directory containing basecalled reads in gzipped or unzipped FASTQ file format with extension ".fastq.gz" or ".fastq".

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run CFIA-NCFAD/nf-flu --input samplesheet.csv -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work            # Directory containing the nextflow working files
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull CFIA-NCFAD/nf-flu

Reproducibility

It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the CFIA-NCFAD/nf-flu releases page and find the latest version number - numeric only (eg. 2.0.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 2.0.0.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future.

Pipeline parameters

Input/output options

Define where the pipeline should find input data and save output data.

--input

• Required
• Type: string

Sample sheet with sample names and paths to reads.

--platform

• Optional
• Type: string
• Default: illumina

Specify the platform for Illumina or Nanopore data

--ref_db

• Optional
• Type: string

Reference database in fasta file, sequence ID must be in format SequenceName_segment#_segmentName, for example 2021-FAV33-OS_segment1_PB2

--outdir

• Optional
• Type: string
• Default: ./results

The output directory where the results will be saved.

IRMA assembly options

--irma_module

• Optional
• Type: string
• Default: FLU-utr

IRMA module to use for analysis.

--keep_ref_deletions

• Optional
• Type: boolean
• Default: true

Set "DEL_TYPE=NNN" to keep deletions to reference sequence as N characters in consensus.

Variant Calling options

--variant_caller

• Optional
• Type: string
• Default: clair3

Set Variant Caller

--medaka_variant_model

• Optional
• Type: string
• Default: r941_prom_hac_variant_g507

Medaka model for final variant calling from phased reads

--medaka_snp_model

• Optional
• Type: string
• Default: r941_prom_hac_snp_g507

Medaka model for initial SNP calling from mixed reads prior to phasing

--clair3_variant_model

• Optional
• Type: string
• Default: r941_prom_sup_g5014

Specify built-in Clair3 variant calling model. See Clair3 docs for options

--clair3_user_variant_model

• Optional
• Type: string
• Default: ''

Specify custom Clair3 model, e.g. Rerio Clair3 model for R10 flowcells (--clair3_user_variant_model /path/to/rerio-models/r1041_e82_400bps_sup_g615_model)

--minor_allele_fraction

• Optional
• Type: number
• Default: 0.25

Set Minor variant allele frequency/fraction

--major_allele_fraction

• Optional
• Type: number
• Default: 0.75

Set Major variant allele frequency/fraction. Only major variant alleles are used for generating a consensus sequence

--low_coverage

• Optional
• Type: number
• Default: 10

Low coverage depth threshold. Consensus sequence positions with less than this coverage depth will be masked with N

Nanopore options

--min_sample_reads

• Optional
• Type: number
• Default: 100

Minimum number of raw reads required per sample in order to be considered for the downstream processing steps.

Mismatch Report options

--min_aln_length

• Optional
• Type: integer
• Default: 700

Minimum alignment length of nucleotide BLAST results to consider for getting mismatch report from BLAST result against reference database. We normally set this number around the smallest genome segment (Segment 8)

H/N subtyping options

Hemaglutinin and neuraminase subtype prediction options

--pident_threshold

• Optional
• Type: number
• Default: 0.85

Minimum % identity of nucleotide BLAST results to consider for determining H/N subtypes.

--max_top_blastn

• Optional
• Type: number
• Default: 3

Maximum of top blastn result reported

--ncbi_influenza_fasta

• Optional
• Type: string
• Default: https://api.figshare.com/v2/file/download/41415330

Path/URL to Zstandard compressed NCBI Influenza virus sequences FASTA file.

--ncbi_influenza_metadata

• Optional
• Type: string
• Default: https://api.figshare.com/v2/file/download/41415333

Path/URL to Zstandard compressed NCBI Influenza virus sequences metadata CSV file.

Generic options

Less common options for the pipeline, typically set in a config file.

--help

• Optional
• Type: boolean

Display help text.

--publish_dir_mode

• Optional
• Type: string
• Default: copy

Method used to save pipeline results to output directory.

NOTE: The Nextflow publishDir option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See Nextflow docs for details.

--monochrome_logs

• Optional
• Type: boolean

Do not use coloured log outputs.

NOTE: Set to disable colourful command line output and live life in monochrome.

--tracedir

• Optional
• Type: string
• Default: ${params.outdir}/pipeline_info

Directory to keep pipeline Nextflow logs and reports.

--singularity_pull_docker_container

• Optional
• Type: boolean

Instead of directly downloading Singularity images for use with Singularity, force the workflow to pull and convert Docker containers instead.

--validate_params

• Optional
• Type: boolean
• Default: True

Boolean whether to validate parameters against the schema at runtime

--show_hidden_params

• Optional
• Type: boolean

Show all params when using --help

NOTE: By default, parameters set as hidden in the schema are not shown on the command line when a user runs with --help. Specifying this option will tell the pipeline to show all parameters.

Slurm scheduler options

--slurm_queue

• Optional
• Type: string

Slurm queue/partition to submit pipeline jobs to

--slurm_queue_size

• Optional
• Type: integer
• Default: 100

Slurm queue size. Max number of jobs to queue at once.

Max job request options

Set the top limit for requested resources for any single job.

--max_cpus

• Optional
• Type: integer
• Default: 16

Maximum number of CPUs that can be requested for any single job.

NOTE: Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1

--max_memory

• Optional
• Type: string
• Default: 128.GB

Maximum amount of memory that can be requested for any single job.

NOTE: Use to set an upper-limit for the memory requirement for each process. Should be a string in the format integer-unit e.g. --max_memory '8.GB'

--max_time

• Optional
• Type: string
• Default: 240.h

Maximum amount of time that can be requested for any single job.

NOTE: Use to set an upper-limit for the time requirement for each process. Should be a string in the format integer-unit e.g. --max_time '2.h'

Core Nextflow arguments

NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Conda) - see below. When using Biocontainers, most of these software packaging methods pull Docker containers from quay.io e.g FastQC except for Singularity which directly downloads Singularity images via https hosted by the Galaxy project and Conda which downloads and installs software locally from Bioconda.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended.

• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter or Charliecloud.
• test_illumina and test_nanopore
  • Profiles for testing the Illumina and Nanopore workflows, respectively, with complete configuration for automated testing
  • Includes links to test data so needs no other parameters

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'