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.
NOTE: This pipeline does not support single-end reads
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 has an IgG control. The two IgG experiments are configured as biological replicates in the same group named
igg_ctrl. They are assigned as controls to the two other groups using the last
control column. If there are an equal number of replicates assigned to the samples from the control group as is the case below, the IgG controls will automatically be assigned to the same replicate number. If there is a mismatch then the first replicate of the control group will be assigned to all.
|Group identifier for sample. This will be identical for replicate samples from the same experimental group.
|Integer representing replicate number.
|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”.
|String representing the control group in the
group column to which this replicate is assigned to.
An example samplesheet has been provided with the pipeline.
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:
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
json file via
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
JSON files via nf-core/launch.
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:
Pipeline Configuration Options
Flow and Output Configuration
There are some options detailed on the parameters page that are prefixed with
only. These are flow control options that allow for saving additional output to the results directory, skipping unwanted portions of the pipeline or running the pipeline up to a certain point, which can be useful for testing.
The easiest way to run the pipeline is by using one of the pre-configured genomes that reflect the available genomes at [iGenomes](AWS iGenomes). Assign
genome to one of the key words for iGenomes and all the available reference data will be automatically fetched. The pipeline uses the following reference data:
Target genome FASTA
Target genome Bowtie2 Index
Target genome GTF
Target genome BED (will be generated from the GTF if not supplied)
Target genome Blacklist (blacklist files for major genomes are included in the pipeline)
Spike-in genome FASTA
Spike-in genome Bowtie2 Index
genome parameter is not supplied, the user must provide all the target genome data themselves (except the gene BED file). The default spike-in genome is E. coli (identified by the string “K12-MG1655”) given that this is the natural spike-in product of the protein production process. However, it is possible to spike-in different DNA during the experimental protocol and then set the
spikein_genome to the target organism, i.e. for yeast (S. cerevisiae) set to “R64-1-1”, for fruit fly (D. melanogaster) it is “BDGP6”.
Read Filtering and Duplication
After alignment using Bowtie2, mapped reads are filtered to remove those which do not pass a minimum quality threshold. This threshold can be changed using the
minimum_alignment_q_score parameter. The mitochondrial reads can be filtered by setting
CUT&RUN and CUT&Tag both integrate adapters into the vicinity of antibody-tethered enzymes, and the exact sites of integration are affected by the accessibility of surrounding DNA. Given these experimental parameters, it is expected that there are many fragments which share common starting and end positions; thus, such duplicates are generally valid but would be filtered out by de-duplication tools. However, there will be a fraction of fragments that are present due to PCR duplication that cannot be separated.
Control samples such as those from IgG datasets have relatively high duplication rates due to non-specific interactions with the genome; therefore, it is appropriate to remove duplicates from control samples.
The default for the pipeline therefore is to only run de-duplication on control samples. If it is suspected that there is a heavy fraction of PCR duplicates present in the primary samples then the parameter
dedup_target_reads can be set using
If the assay has been modified to include a linear amplification step prior to PCR amplification, the user might want to remove the duplicates arising from the linear amplification step. This can be achieved by setting the
true. In this way, the pipeline uses a custom
.py script to filter the reads so that all read 1’s have a unique start site and always choosing the read with highest mapping quality. The removal of these kind of duplicates is an essential step of TIPseq analysis. In TIPseq, genomic DNA is cut with Tn5 loaded with T7 promoter sequence that gets inserted in the cut DNA fragment. The T7 promoter sequence is then used to perform in vitro transcription to produce RNA copies of the cut DNA fragment. These duplicates are referred to as linear duplicates. For an overview of the in vitro transcription using T7 promoter, see here.
The default mode in the pipeline is to normalise stacked reads before peak calling for epitope abundance using spike-in normalisation.
Traditionally, DNA from E. coli is carried along with bacterially-produced enzymes that are used in CUT&RUN and CUT&Tag experiments and gets tagmented non-specifically during the reaction. The fraction of total reads that map to the E. coli genome depends on the yield of epitope-targeted CUT&Tag, and so depends on the number of cells used and the abundance of that epitope in chromatin. Since a constant amount of protein is added to the reactions and brings along a fixed amount of E. coli DNA, E. coli reads can be used to normalize epitope abundance in a set of experiments.
Since the introduction of these techniques there are several factors that have reduced the usefulness of this type of normalisation in certain experimental conditions. Firstly, many commercially available kits now have very low levels of E. coli DNA in them, which therefore requires users to spike-in their own DNA for normalisation which is not always done. Secondly the normalisation approach is dependant on the cell count between samples being constant, which in our experience is quite difficult to achieve especially in tissue samples.
For these reasons we provide several other modes of normalisation based on read count; however, it should be noted that this form of normalisation is more simplistic and does not take into account epitope abundance. These normalisation modes are performed by Deeptools bamCoverage, some are more relevant than others to this type of data, we recommend using CPM with a bin size of 1 as a default.
|The default mode which normalises by E. coli DNA.
|Reads Per Kilobase per Million mapped reads. More relevant for transcript based assays.
|Counts Per Million mapped reads = number of reads per bin / number of mapped reads. Default bin size is 1
|number of reads per bin / sum of all reads per bin (in millions),
Normalisation mode can be changed by the parameter
This pipeline currently provides peak calling via
MACS2 using the
peakcaller parameter. If control samples are provided in the sample sheet by default they will be used to normalise the called peaks against non-specific background noise. Control normalisation can be disabled using
--use_control. Additionally it may be necessary to scale control samples being used as background, especially when read count normalisation methods have been used at earlier stages in the pipeline. To scale the control samples before peak calling, change the
--igg_scale_factor parameter to a number between 0-1. Multiple peak callers can be run by using comma separated values e.g.
--peakcaller SEACR,MACS2, in this mode the primary peak caller is the first in the list and will be used for downstream processing; any additional peak callers will simply output to the results directory.
After peak calling, consensus peaks will be calculated based on merging peaks within the same groups. The number of replicates required for a valid peak can be changed using
replicate_threshold. In some situations a user may which to call consensus peaks based on all samples, this can be configured by changing the
consensus_peak_mode parameter from
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 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
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 reproducbility, 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.
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
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, Apptainer, Conda) - see below.
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, since it can lead to different results on different machines dependent on the computer enviroment.
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
- 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 Apptainer
- 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.
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.
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 nf-core pipelines use 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, nf-core pipelines provide 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.
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.
Azure Resource Requests
To be used with the
azurebatch profile by specifying the
We recommend providing a compute
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.
-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