Analysis pipeline for CUT&RUN and CUT&TAG experiments that includes QC, support for spike-ins, IgG controls, peak calling and downstream analysis.
nf-core/cutandrun is a best-practice bioinformatic analysis pipeline for CUT&Run and CUT&Tag experimental protocols that where developed to study protein-DNA interactions and epigenomic profiling.
You will need to create a samplesheet file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with the correct data structure as shown in the examples below.
An example sample sheet structure is shown below. This defines two target experimental groups for the histone marks h3k27me3 and h3k4me3 with two biological replicates per group. Each antibody target also had an IgG control performed alongside. The two IgG experiments are configured as biological replicates with the
igg group keyword with a unique control group assignment. The target experiments are then assigned to the igg control group using the
|Group identifier for sample. This will be identical for replicate samples from the same experimental group.
|Integer representing replicate number.
|Integer representing the IgG control group the target is assigned to.
|Full path to FastQ file for read 1. File has to be zipped and have the extension “.fastq.gz” or “.fq.gz”.
|Full path to FastQ file for read 2. File has to be zipped and have the extension “.fastq.gz” or “.fq.gz”.
An example samplesheet has been provided with the pipeline.
Multiple replicates and IgG controls
To assign biological replicates to the same group use the same group identifier but increment the
replicate column appropriately. To merge multiple fastq files from the same library or to merge technical replicate data, use identical
replicate column identifiers. The pipeline will automatically merge these samples and treat them as one biological replicate. An example of this type of sample sheet configuration is shown below where each biological replicate has two fastq files:
To add any IgG controls that were processed, use the
igg keyword in the
group column and increment as with the target samples. The IgG control will be used to normalising your experimental CUT&Run (OR CUT&Tag) data. It is recommended to have an IgG control for normalising your experimental data and this is the default action for the pipeline. However, if you run the pipeline without IgG control data you must supply
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/cutandrun 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 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 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 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.
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
modules/nf-core/software/star/align/main.nf. 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 process_high. The Nextflow
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 just the process name i.e.
STAR_ALIGNin the config file and not the full task name string that is printed to screen in the error message or on the terminal whilst the pipeline is running i.e.
RNASEQ:ALIGN_STAR:STAR_ALIGN. You may get a warning suggesting that the process selector isn’t recognised but you can ignore that if the process name has been specified correctly. This is something that needs to be fixed upstream in core Nextflow.
For the ultimate flexibility, we have implemented and are using Nextflow DSL2 modules in a way where it is possible for both developers and users to change tool-specific command-line arguments (e.g. providing an additional command-line argument to the
STAR_ALIGN process) as well as publishing options (e.g. saving files produced by the
STAR_ALIGN process that aren’t saved by default by the pipeline). In the majority of instances, as a user you won’t have to change the default options set by the pipeline developer(s), however, there may be edge cases where creating a simple custom config file can improve the behaviour of the pipeline if for example it is failing due to a weird error that requires setting a tool-specific parameter to deal with smaller / larger genomes.
The command-line arguments passed to STAR in the
STAR_ALIGN module are a combination of:
Mandatory arguments or those that need to be evaluated within the scope of the module, as supplied in the
scriptsection of the module file.
options.argsstring of non-mandatory parameters that is set to be empty by default in the module but can be overwritten when including the module in the sub-workflow / workflow context via the
The nf-core/rnaseq pipeline has a sub-workflow (see terminology) specifically to align reads with STAR and to sort, index and generate some basic stats on the resulting BAM files using SAMtools. At the top of this file we import the
STAR_ALIGN module via the Nextflow
include keyword and by default the options passed to the module via the
addParams option are set as an empty Groovy map here; this in turn means
options.args will be set to empty by default in the module file too. This is an intentional design choice and allows us to implement well-written sub-workflows composed of a chain of tools that by default run with the bare minimum parameter set for any given tool in order to make it much easier to share across pipelines and to provide the flexibility for users and developers to customise any non-mandatory arguments.
When including the sub-workflow above in the main pipeline workflow we use the same
include statement, however, we now have the ability to overwrite options for each of the tools in the sub-workflow including the
align_options variable that will be used specifically to overwrite the optional arguments passed to the
STAR_ALIGN module. In this case, the options to be provided to
STAR_ALIGN have been assigned sensible defaults by the developer(s) in the pipeline’s
modules.config and can be accessed and customised in the workflow context too before eventually passing them to the sub-workflow as a Groovy map called
star_align_options. These options will then be propagated from
workflow -> sub-workflow -> module.
As mentioned at the beginning of this section it may also be necessary for users to overwrite the options passed to modules to be able to customise specific aspects of the way in which a particular tool is executed by the pipeline. Given that all of the default module options are stored in the pipeline’s
modules.config as a
params variable it is also possible to overwrite any of these options via a custom config file.
Say for example we want to append an additional, non-mandatory parameter (i.e.
--outFilterMismatchNmax 16) to the arguments passed to the
STAR_ALIGN module. Firstly, we need to copy across the default
args specified in the
modules.config and create a custom config file that is a composite of the default
args as well as the additional options you would like to provide. This is very important because Nextflow will overwrite the default value of
args that you provide via the custom config.
As you will see in the example below, we have:
--outFilterMismatchNmax 16to the default
argsused by the module.
- changed the default
publish_dirvalue to where the files will eventually be published in the main results directory.
'bam':''to the default value of
publish_filesso that the BAM files generated by the process will also be saved in the top-level results directory for the module. Note:
'out':'log'means any file/directory ending in
outwill now be saved in a separate directory called
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