Usage
Try out the pipeline right now!
Make sure you have Nextflow and a container manager (for example, Docker) installed. See the installation instructions for more info.
Tip
Did your analysis fail? After fixing the issue add -resume to the command to continue from where it left off.
Input
Samples
The pipeline requires a samplesheet as input. This samplesheet should contain the name and the absolute locations of reads.
The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet (i.e. if the fastq_2 column is empty, the sample is assumed to be single-end).
An example samplesheet file consisting of both single- and paired-end data may look something like the one below.
[
{
"sample": "sample1",
"fastq1": "AEG588A1_S1_L002_R1_001.fastq.gz",
"fastq2": "AEG588A1_S1_L002_R2_001.fastq.gz",
},
{
"sample": "sample2",
"fastq1": "AEG588A5_S5_L003_R1_001.fastq.gz",
},
{
"sample": "sample3",
"fastq1": "AEG588A3_S3_L002_R1_001.fastq.gz",
"fastq2": "AEG588A3_S3_L002_R2_001.fastq.gz",
}
]
| Value | Description |
|---|---|
sample |
Custom sample name, needs to be unique |
fastq_1 |
Full path (not relative paths) 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 (not relative paths) to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". |
Mapping constrains
Viralgenie can in addition to constructing denovo consensus genomes map the sample reads to a series of references. These references are provided through the parameter --mapping_constrains.
An example mapping constrain samplesheet file consisting of 5 references, may look something like the one below.
This is for 5 references, 2 of them being a multi-fasta file, only one of the multi-fasta needs to undergo reference selection.
id species segment selection samples sequence definition
Lassa-L-dataset LASV L true LASV_L.multi.fasta Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the L segment clustered at 99.5% similarity
Lassa-S-dataset LASV S false sample1;sample3 LASV_S.multi.fasta Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the S segment clustered at 99.5% similarity
NC038709.1 HAZV L false sample1;sample2 L-NC_038709.1.fasta Hazara virus isolate JC280 segment L, complete sequence.
NC038710.1 HAZV M false M-NC_038710.1.fasta Hazara virus isolate JC280 segment M, complete sequence.
NC038711.1 HAZV S false S-NC_038711.1.fasta Hazara virus isolate JC280 segment S, complete sequence.
id,species,segment,selection,samples,sequence,definition
Lassa-L-dataset,LASV,L,true,,LASV_L.multi.fasta,"Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the L segment clustered at 99.5% similarity"
Lassa-S-dataset,LASV,S,false,"sample1;sample3",LASV_S.multi.fasta,"Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the S segment clustered at 99.5% similarity"
NC038709.1,HAZV,L,false,"sample1;sample2",L-NC_038709.1.fasta,"Hazara virus isolate JC280 segment L, complete sequence."
NC038710.1,HAZV,M,false,,M-NC_038710.1.fasta,"Hazara virus isolate JC280 segment M, complete sequence."
NC038711.1,HAZV,S,false,,S-NC_038711.1.fasta,"Hazara virus isolate JC280 segment S, complete sequence."
- id: Lassa-L-dataset
species: LASV
segment: L
selection: true
sequence: LASV_L.multi.fasta
definition: 'Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the L segment clustered at 99.5% similarity'
- id: Lassa-S-dataset
species: LASV
segment: S
selection: false
samples: sample1;sample3
sequence: LASV_S.multi.fasta
definition: 'Collection of LASV sequences used for hybrid capture bait design, all publicly availble sequences of the S segment clustered at 99.5% similarity'
- id: NC038709.1
species: HAZV
segment: L
selection: false
samples: sample1;sample2
sequence: L-NC_038709.1.fasta
definition: 'Hazara virus isolate JC280 segment L, complete sequence.'
- id: NC038710.1
species: HAZV
segment: M
selection: false
sequence: M-NC_038710.1.fasta
definition: 'Hazara virus isolate JC280 segment M, complete sequence.'
- id: NC038711.1
species: HAZV
segment: S
selection: false
sequence: S-NC_038711.1.fasta
definition: 'Hazara virus isolate JC280 segment S, complete sequence.'
Warning
JSON format is not supported for mapping constrains samplesheet.
| Column | Description |
|---|---|
id |
Reference identifier, needs to be unique' |
species |
[Optional] Species name of the reference |
segment |
[Optional] Segment name of the reference |
selection |
[Optional] Specify if the multiFasta reference file needs to undergo reference selection |
samples |
[Optional] List of samples that need to be mapped towards the reference. If empty, map all samples. |
sequence |
Full path (not relative paths) to the reference sequence file. |
definition |
[Optional] Definition of the reference sequence file. |
Tip
- The
samplescolumn is optional - if empty, all samples will be mapped towards the reference. - Multi-fasta files can be provided and all reads will mapped to all genomes but stats will not be reported separately in the final report.
Metadata
Sample metadata can be provided to the pipeline with the argument --metadata. This metadata will not affect the analysis in anyway and is only used to annotate the final report. Any metadata can be provided as long as the first value is the sample value.
- sample: sample1
sample_accession: SAMN14154201
secondary_sample_accession: SRS6189918
study_accession: PRJNA607948
run_alias: vero76_Illumina.fastq
library_layout: PAIRED
- sample: sample2
sample_accession: SAMN14154205
secondary_sample_accession: SRS6189924
study_accession: PRJNA607948
run_alias: veroSTAT-1KO_Illumina.fastq
library_layout: PAIRED
Warning
JSON format is not supported for metadata samplesheet.
Running the pipeline
The typical command for running the pipeline is as follows:
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:
-
Directory containing the nextflow working files
-
Finished results in specified location (defined with --outdir)
-
Log file from Nextflow
-
Other nextflow hidden files, eg. history of pipeline runs and old logs.
If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.
Pipeline settings can be provided in a yaml or json file via -params-file <file>.
Warning
Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).
The above pipeline run specified with a params file in yaml format:
You can also generate such YAML/JSON files via nf-core launch if nf-core is installed.
Tip
Use nf-core launch if it is the first time running the pipeline to explore all its features and options in an accesible way.
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:
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 Joon-Klaps/viralgenie releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.
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. For example, at the bottom of the MultiQC reports.
To further assist in reproducibility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
Tip
If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
Core Nextflow arguments
Note
These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).
The -profile parameter
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, Apptainer, Conda) - see below.
Info
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, since it can lead to different results on different machines dependent on the computer enviroment.
test- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
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
apptainer- A generic configuration profile to be used with Apptainer
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, Charliecloud, or Apptainer.
-resume
Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see this blog post.
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.
Custom configuration
Resource requests
Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customize the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.
To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.
Custom Containers
In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default Viralgenie uses containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.
To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.
Custom Tool Arguments
A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, Viralgenie provides some freedom to users to insert additional parameters that the pipeline does not include by default.
To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.
nf-core/configs
In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running Viralgenie regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.
See the main Nextflow documentation for more information about creating your own configuration files.
If you have any questions or issues please send us a message on Slack on the #configs channel.
Azure Resource Requests
To be used with the azurebatch profile by specifying the -profile azurebatch.
We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.
Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.
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):