nf-core/circrna
circRNA quantification, differential expression analysis and miRNA target prediction of RNA-Seq data
Pipeline parameters
Please provide pipeline parameters via the CLI or Nextflow -params-file
option. Custom config files including those provided by the -c
Nextflow option can be used to provide any configuration except for parameters; see docs.
Samplesheet input
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 comma-separated file with 4 columns, and a header row as shown in the examples below.
--input '[path to samplesheet file]'
Multiple runs of the same sample
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. Below is an example for the same sample sequenced across 3 lanes. If you set the strandedness value to auto
the pipeline will use the tool-defaults throughout the pipeline.
sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,auto
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,auto
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,auto
Full samplesheet
The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 4 columns to match those defined in the table below.
A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3
has been sequenced twice.
sample,fastq_1,fastq_2,strandedness
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,forward
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,forward
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz,forward
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,,reverse
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,,reverse
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,,reverse
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,,reverse
Column | Description |
---|---|
sample | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_ ). |
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”. |
strandedness | Sample strand-specificity. Must be one of unstranded , forward , reverse or auto . |
An example samplesheet has been provided with the pipeline.
BSJ detection
This part of the pipeline is responsible for the detection of back-splice junctions (BSJs) in the input data. The following tools are currently supported:
CIRCexplorer2
circRNA finder
CIRIquant
DCC
find circ
MapSplice
Segemehl
The tools to be used can be specified using the tools
parameter.
Each of the tools also quantifies how many reads support each BSJ. You can specify a cutoff for the minimum number of reads supporting a BSJ using the bsj_reads
parameter.
Additionally, the parameter tool_filter
can be used to specify how many tools a BSJ has to be detected by to be considered as a valid hit.
For instructions on how to interpret the output of this section, please check out the output documentation.
Annotation
The annotation is generally based on the reference GTF file. It can also utilize BED files that are provided by the various circRNA databases.
The GTF-based annotation allows setting the parameter exon_boundary
to specify a window around exons. If the BSJ is within this window, it will be annotated as a circRNA - otherwise, it will be annotated as an exon-intron circRNA (EI-circRNA). The default value is 0
.
For the database-based annotation, an additional sample sheet is required:
name,file,min_overlap
db1,db1.bed,0.9
db2,db2.bed,0.8
Column | Description |
---|---|
name | Name of the database. This will be used as a prefix for the region names in the output files. |
file | Path to the BED file. The file has to be a valid BED6 file. |
min_overlap | Minimum bidirectional overlap required between the BSJ and the region in the BED file. |
The output of the annotation step will be bundled with the outputs of the BSJ detection step.
miRNA prediction
This section allows looking for miRNA binding sites in the circRNAs. The following tools are currently supported:
miRanda
TargetScan
This section will only be executed if the mature
parameter is provided.
The parameter mature
should point to a FASTA file containing mature miRNA sequences.
By providing a TSV file containing the miRNA expression of all samples via mirna_expression
, this
sub-workflow will perform additional normalization and filtering of mirna_expression
and mature
before
executing the miRNA binding size prediction.
To view the outputs of the module, please see the output documentation.
Statistical tests
Currently, only CircTest is supported for the statistical analysis of the circRNA expression data. The phenotype
parameter is required for this step.
A valid example of a phenotype.csv
file (matching the “Full samplesheet”) is shown here:
sample,condition
CONTROL_REP1,control
CONTROL_REP2,control
CONTROL_REP3,control
TREATMENT_REP1,treatment
TREATMENT_REP2,treatment
TREATMENT_REP3,treatment
Note that TREATMENT_REP3
only has one entry in the phenotype.csv
file, even though it has two entries in the samplesheet.csv
file.
If the phenotype
parameter is provided, the phenotype information will also be added to the SummarizedExperiment
object, that results from the “Quantification” step.
Running the pipeline
The typical command for running the pipeline is as follows:
nextflow run \
nf-core/circrna \
--input <SAMPLESHEET> \
--outdir <OUTDIR> \
--gtf <GENOME GTF> \
--fasta <GENOME FASTA> \
--igenomes_ignore \
--genome null \
-profile docker
NB: Loading iGenomes configuration remains the default for reasons of consistency with other workflows, but should be disabled when not using iGenomes, applying the recommended usage above.
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
<OUTDIR> # Finished results in specified location (defined with --outdir)
.nextflow_log # 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>
.
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:
nextflow run nf-core/circrna -profile docker -params-file params.yaml
with params.yaml
containing:
input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>
You can also generate such YAML
/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:
nextflow pull nf-core/circrna
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.
Reproducibility
It’s 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/circrna 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 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).
-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, 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.
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
- Pulls software from Docker Hub:
nfcore/circrna
singularity
- A generic configuration profile to be used with Singularity
- Pulls software from Docker Hub:
nfcore/circrna
podman
- A generic configuration profile to be used with Podman
- Pulls software from Docker Hub:
nfcore/circrna
shifter
- A generic configuration profile to be used with Shifter
- Pulls software from Docker Hub:
nfcore/circrna
charliecloud
- A generic configuration profile to be used with Charliecloud
- Pulls software from Docker Hub:
nfcore/circrna
apptainer
- A generic configuration profile to be used with Apptainer
wave
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
24.03.0-edge
or later).
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
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 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.
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 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.
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 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.
Command error:
.command.sh: line 9: 30 Killed STAR —genomeDir star —readFilesIn WT_REP1_trimmed.fq.gz —runThreadN 2 —outFileNamePrefix WT_REP1.
Tip: you can replicate the issue by changing to the process work dir and entering the command bash .command.run
To change the resource requests, please see the [max resources](https://nf-co.re/docs/usage/configuration#max-resources) and [tuning workflow resources](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources) section of the nf-core website.
#### For beginners
A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters `--max_cpus`, `--max_memory`, and `--max_time`. Based on the error above, you have to increase the amount of memory. Therefore you can go to the [parameter documentation of rnaseq](https://nf-co.re/rnaseq/3.9/parameters) and scroll down to the `show hidden parameter` button to get the default value for `--max_memory`. In this case 128GB, you than can try to run your pipeline again with `--max_memory 200GB -resume` to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below.
#### Advanced option on process level
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](https://github.com/nf-core/rnaseq/search?q=process+STAR_ALIGN).
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/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`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/modules/nf-core/software/star/align/main.nf#L9).
The [Nextflow `label`](https://www.nextflow.io/docs/latest/process.html#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`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L33-L37) which in this case is defined as 72GB.
Providing you haven't set any other standard nf-core parameters to **cap** the [maximum resources](https://nf-co.re/usage/configuration#max-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`](#-c) parameter as highlighted in previous sections.
```groovy
process {
withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' {
memory = 100.GB
}
}
NB: We specify the full process name i.e.
NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN
in 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.
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 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.
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 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.
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
):
NXF_OPTS='-Xms1g -Xmx4g'