Nanopore demultiplexing, QC and alignment pipeline
You will need to create a file with information about the samples in your experiment/run before executing the pipeline. Use the
--input parameter to specify its location. It has to be a comma-separated file with 6 columns and a header row:
|Group identifier for sample. This will be identical for replicate samples from the same experimental group.
|Integer representing replicate number. Must start from
1..<number of replicates>.
|Barcode identifier attributed to that sample during multiplexing. Must be an integer.
|Full path to FastQ file if previously demultiplexed, BAM file if previously aligned, or a path to a directory with subdirectories containing fastq or fast5 files. FastQ file has to be zipped and have the extension “.fastq.gz” or “.fq.gz”. BAM file has to have the extension “.bam”.
|Genome fasta file or transcriptome fasta file for alignment. This can either be a local path, or the appropriate key for a genome available in iGenomes config file. Must have the extension “.fasta”, “.fasta.gz”, “.fa” or “.fa.gz”.
|Annotation gtf file for transcript discovery and quantification and RNA modification detection. This can either be blank or a local path. Must have the extension “.gtf”.
As shown in the examples below, the accepted format of the file is slightly different if you would like to run the pipeline with or without basecalling/demultiplexing.
With basecalling and demultiplexing
samplesheet.csv for barcoded fast5 inputs
Example command for barcoded fast5 inputs
With basecalling but not demultiplexing
samplesheet.csv for non-barcoded fast5 inputs
Only a single sample can be specified if you would like to skip demultiplexing
Example command for non-barcoded fast5 inputs
With demultiplexing but not basecalling
samplesheet.csv for non-demultiplexed fastq inputs
Example command for non-demultiplexed fastq inputs
Without both basecalling and demultiplexing
samplesheet.csv for demultiplexed fastq inputs
Example command for demultiplexed fastq inputs
Without basecalling, demultiplexing, and alignment
samplesheet.csv for BAM inputs
Example command for BAM inputs
RNA modification detection (please run basecalling prior)
samplesheet.csv for FAST5 and FASTQ input directories
Each of the FAST5 and FASTQ input directory should have the following structure:
Example command for RNA modification detection
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:
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:
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 nf-core/nanoseq releases page and find the latest version number - numeric only (eg.
1.3.1). Then specify this when running the pipeline with
-r (one hyphen) - eg.
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.
Core Nextflow arguments
NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).
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.
-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.
- A generic configuration profile to be used with Docker
- A generic configuration profile to be used with Singularity
- A generic configuration profile to be used with Podman
- A generic configuration profile to be used with Shifter
- A generic configuration profile to be used with Charliecloud
- 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.
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
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.
Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.
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 customise 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.
For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the
STAR_ALIGN process due to an exit code of
137 this would indicate that there is an out of memory issue:
To bypass this error you would need to find exactly which resources are set by the
STAR_ALIGN process. The quickest way is to search for
process STAR_ALIGN in the nf-core/rnaseq Github repo.
We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the
modules/ directory and so, based on the search results, the file we want is
If you click on the link to that file you will notice that there is a
label directive at the top of the module that is set to
label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements.
The default values for the
process_high label are set in the pipeline’s
base.config which in this case is defined as 72GB.
Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the
STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB.
The custom config below can then be provided to the pipeline via the
-c parameter as highlighted in previous sections.
NB: We specify the full process name i.e.
NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGNin the config file because this takes priority over the short name (
STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden.
If you get a warning suggesting that the process selector isn’t recognised check that the process name has been specified correctly.
The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the
process name and override the Nextflow
container definition for that process using the
withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn’t make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via
Check the default version used by the pipeline in the module file for Pangolin
Find the latest version of the Biocontainer available on Quay.io
Create the custom config accordingly:
NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the
work/directory otherwise the
-resumeability of the pipeline will be compromised and it will restart from scratch.
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 nf-core pipelines 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.
Running in the background
Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.
-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
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