RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
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.
Multiple runs of the same sample
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:
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.
|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 (
|Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
|Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
|Sample strand-specificity. Must be one of
An example samplesheet has been provided with the pipeline.
replicatecolumns were replaced with a single
samplecolumn as of v3.1 of the pipeline. The
samplecolumn is essentially a concatenation of the
replicatecolumns, however it now also offers more flexibility in instances where replicate information is not required e.g. when sequencing clinical samples. If all values of
samplehave the same number of underscores, fields defined by these underscore-separated names may be used in the PCA plots produced by the pipeline, to regain the ability to represent different groupings.
By default, the pipeline uses STAR (i.e.
--aligner star_salmon) to map the raw FastQ reads to the reference genome, project the alignments onto the transcriptome and to perform the downstream BAM-level quantification with Salmon. STAR is fast but requires a lot of memory to run, typically around 38GB for the Human GRCh37 reference genome. Since the RSEM (i.e.
--aligner star_rsem) workflow in the pipeline also uses STAR you should use the HISAT2 aligner (i.e.
--aligner hisat2) if you have memory limitations.
You also have the option to pseudo-align and quantify your data with Salmon by providing the
--pseudo_aligner salmon parameter. Salmon will then be run in addition to the standard alignment workflow defined by
--aligner, mainly because it allows you to obtain QC metrics with respect to the genomic alignments. However, you can provide the
--skip_alignment parameter if you would like to run Salmon in isolation. By default, the pipeline will use the genome fasta and gtf file to generate the transcripts fasta file, and then to build the Salmon index. You can override these parameters using the
--salmon_index parameters, respectively. The library preparation protocol (library type) used by Salmon quantification is inferred by the pipeline based on the information provided in the samplesheet, however, you can override it using the
--salmon_quant_libtype parameter. You can find the available options in the Salmon documentation.
The current options align with STAR and quantify using either Salmon (
--aligner star_salmon) / RSEM (
--aligner star_rsem). You also have the option to pseudo-align and quantify your data with Salmon by providing the
--pseudo_aligner salmon parameter.
Since v3.0 of the pipeline, featureCounts is no longer used to perform gene/transcript quantification, however it is still used to generate QC metrics based on biotype information available within GFF/GTF genome annotation files. This decision was made primarily because of the limitations of featureCounts to appropriately quantify gene expression data. Please see Zhao et al., 2015 and Soneson et al., 2015.
For similar reasons, quantification will not be performed if using
--aligner hisat2 due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments - this may change in future releases (see #822). HISAT2 has been made available for those who have a preference for the alignment, QC and other types of downstream analysis compatible with it’s output.
Reference genome files
The minimum reference genome requirements are a FASTA and GTF file, all other files required to run the pipeline can be generated from these files. However, it is more storage and compute friendly if you are able to re-use reference genome files as efficiently as possible. It is recommended to use the
--save_reference parameter if you are using the pipeline to build new indices (e.g. those unavailable on AWS iGenomes) so that you can save them somewhere locally. The index building step can be quite a time-consuming process and it permits their reuse for future runs of the pipeline to save disk space. You can then either provide the appropriate reference genome files on the command-line via the appropriate parameters (e.g.
--star_index '/path/to/STAR/index/') or via a custom config file.
--genomeis provided then the FASTA and GTF files (and existing indices) will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by
--gffis provided as input then this will be converted to a GTF file, or the latter will be used if both are provided.
--gene_bedis not provided then it will be generated from the GTF file.
--additional_fastais provided then the features in this file (e.g. ERCC spike-ins) will be automatically concatenated onto both the reference FASTA file as well as the GTF annotation before building the appropriate indices.
--aligner star_rsem, both the STAR and RSEM indices should be present in the path specified by
--rsem_index (see #568).
NB: Compressed reference files are also supported by the pipeline i.e. standard files with the
.gzextension and indices folders with the
As of v3.7 of the pipeline, if you are using a genome downloaded from AWS iGenomes and using
--aligner star_salmon (default) the version of STAR to use for the alignment will be auto-detected (see #808).
If you are using GENCODE reference genome files please specify the
--gencode parameter because the format of these files is slightly different to ENSEMBL genome files:
--gtf_group_features_typeparameter will automatically be set to
gene_typeas opposed to
- If you are running Salmon, the
--gencodeflag will also be passed to the index building step to overcome parsing issues resulting from the transcript IDs in GENCODE fasta files being separated by vertical pipes (
|) instead of spaces (see this issue).
Prokaryotic genome annotations
This pipeline uses featureCounts to generate QC metrics based on biotype information available within GFF/GTF genome annotation files. The format of these annotation files can vary significantly depending on the source of the annotation and the type of organism. The default settings in the pipeline are tailored towards Ensembl GTF annotations available for eukaryotic genomes. Prokaryotic genome annotations tend to be distributed in GFF format which are structured differently in terms of the feature naming conventions. There are a number of ways you can tune the behaviour of the pipeline to cater for differences/absence of biotype information:
--skip_biotype_qcto bypass this step altogether in case biotype information is of no interest or isn’t present in your annotation file.
--skip_rseqcsince features like splice junctions, transcription start (TSS) and ending sites (TES) are less prevalent and therefore, less informative in prokaryotes compared to eukaryotes.
--featurecounts_feature_type transcriptinstead of
--featurecounts_feature_type transcript exon(default) since entries for the latter may not contain a
--featurecounts_group_type gene_biotypeentry in the last column of the annotation. You should make sure that the value defined by
--featurecounts_feature_typeideally contain corresponding entries for
--featurecounts_feature_type 'CDS' --featurecounts_group_type 'product'to identify the number of hypothetical proteins. However, the featureCounts QC will no longer reflect the biotype information from your RNA.
Please get in touch with us on the #rnaseq channel in the nf-core Slack workspace if you are having problems or need any advice.
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/rnaseq 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