This option indicates the experimental protocol used for the sample preparation. Currently supporting:

  • 'illumina': adapter (TGGAATTCTCGGGTGCCAAGG)
  • 'nextflex': adapter (TGGAATTCTCGGGTGCCAAGG), clip_r1 (4), three_prime_clip_r1 (4`)
  • 'qiaseq': adapter (AACTGTAGGCACCATCAAT)
  • 'cats': adapter (GATCGGAAGAGCACACGTCTG), clip_r1(3)
  • 'custom' (where the user can indicate the three_prime_adapter, clip_r1 and three_prime_clip_r1 manually)

⚠️ At least the custom protocol has to be specified, otherwise the pipeline won't run. In case you specify the custom protocol, ensure that the parameters above are set accordingly or the defaults will be applied. If you want to auto-detect the adapters using fastp, please set --three_prime_adapter to "".

mirtrace_species or mirgenedb_species

It should point to the 3-letter species name used by miRBase or MirGeneDB. Note the difference in case for the two databases.

Different parameters can be set for the two supported databases. By default miRBase will be used with the parameters below.

  • mirna_gtf: If not supplied by the user, then mirna_gtf will point to the latest GFF3 file in miRbase:${params.mirtrace_species}.gff3
  • mature: points to the FASTA file of mature miRNA sequences.
  • hairpin: points to the FASTA file of precursor miRNA sequences.

If MirGeneDB should be used instead it needs to be specified using --mirgenedb and use the parameters below .

  • mirgenedb_gff: The data can not be downloaded automatically (URLs are created with short term tokens in it), thus the user needs to supply the gff file for either his species, or all species downloaded from The total set will automatically be subsetted to the species specified with --mirgenedb_species.
  • mirgenedb_mature: points to the FASTA file of mature miRNA sequences. Download from
  • mirgenedb_hairpin: points to the FASTA file of precursor miRNA sequences. Download from Note that MirGeneDB does not have a dedicated hairpin file, but the Precursor sequences are to be used.


  • fasta: the reference genome FASTA file
  • bt_indices: points to the folder containing the bowtie2 indices for the genome reference specified by fasta. *Note:* if the FASTA file in fasta is not the same file used to generate the bowtie2 indices, then the pipeline will fail.

Contamination filtering

This step has, until now, only been tested for human data. Unexpected behaviour can occur when using it with a different species.

Contamination filtering of the sequencing reads is optional and can be invoked using the filter_contamination parameter. FASTA files with

  • rrna: Used to supply a FASTA file containing rRNA contamination sequence.
  • trna: Used to supply a FASTA file containing tRNA contamination sequence. e.g.
  • cdna: Used to supply a FASTA file containing cDNA contamination sequence. e.g. The FASTA file is first compared to the available miRNA sequences and overlaps are removed.
  • ncrna: Used to supply a FASTA file containing ncRNA contamination sequence. e.g. The FASTA file is first compared to the available miRNA sequences and overlaps are removed.
  • pirna: Used to supply a FASTA file containing piRNA contamination sequence. e.g. The FASTA file is first compared to the available miRNA sequences and overlaps are removed.
  • other_contamination: Used to supply an additional filtering set. The FASTA file is first compared to the available miRNA sequences and overlaps are removed.

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 2 columns ("sample" and "fastq_1"), and a header row as shown in the examples below.

If a second fastq file is provided using another column, the extra data are ignored by this pipeline. The smRNA species should be sufficiently contained in the first read, and so the second read is superfluous data in this smRNA context.

--input '[path to samplesheet file]'  

Multiple runs of the same sample

The sample identifiers should match between runs of resequenced samples. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:


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 3 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.


| 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". |

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/smrnaseq --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile docker  

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.  

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/smrnaseq  


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/smrnaseq 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. -r 1.3.1.

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.

Stand-alone scripts

The bin directory contains some scripts used by the pipeline which may also be run manually:

  • edgeR_miRBase.r: R script using for processing reads counts of mature miRNAs and miRNA precursors (hairpins).

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. Different profiles 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, e.g FastQC. The Singularity profile directly downloads Singularity images via HTTPS hosted by the Galaxy project, while the Conda profile 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 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.

  • 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
  • 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 or Charliecloud.
  • test
    • 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.

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.

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:

[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)  
Caused by:  
    Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)  
Command executed:  
    STAR \  
        --genomeDir star \  
        --readFilesIn WT_REP1_trimmed.fq.gz  \  
        --runThreadN 2 \  
        --outFileNamePrefix WT_REP1. \  
Command exit status:  
Command output:  
Command error: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>  
Work dir:  
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash`  

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/
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.

process {  
        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.

Updating containers

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 -c custom.config.

  1. Check the default version used by the pipeline in the module file for Pangolin

  2. Find the latest version of the Biocontainer available on

  3. Create the custom config accordingly:

    • For Docker:

      process {  
          withName: PANGOLIN {  
              container = ''  
    • For Singularity:

      process {  
          withName: PANGOLIN {  
              container = ''  
    • For Conda:

      process {  
          withName: PANGOLIN {  
              conda = 'bioconda::pangolin=3.0.5'  

*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 -resume ability of the pipeline will be compromised and it will restart from scratch.


In most cases, you will only need to create a custom config once. However, if multiple users within an organisation intend to run nf-core pipelines regularly under the same settings, then consider uploading your custom config file to the nf-core/configs git repository. Before uploading, ensure 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 amended 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'