Assembly and annotation of metatranscriptomic data, both prokaryotic and eukaryotic.
Metatdenovo is a workflow primarily designed for annotation of metatranscriptomes for which reference genomes are not available. The approach is to first create an assembly, then call genes and finally quantify and annotate the genes. Since the workflow includes gene callers and annotation tools and databases both for prokaryotes and eukaryotes, the workflow should be suitable for both organism groups and mixed communities can be handled by trying different gene callers and comparing the results.
While the rationale for writing the workflow was metatranscriptomes, there is nothing in the workflow that precludes use for single organisms rather than communities nor genomes rather than transcriptomes. Instead, the workflow should be usable for any project in which a de novo assembly followed by quantification and annotation is suitable.
Running the workflow
A typical command for running the workflow is:
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 must be a comma-separated file with 3 columns, and a header row as shown in the examples below
A final samplesheet file consisting of samples taken at time 0 and 24 in triplicate may look like the one below.
|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”.
An example samplesheet has been provided with the pipeline.
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:
Filter/remove sequences from the samples (e.g. rRNA sequences with SILVA database)
The pipeline can remove potential contaminants using the BBduk program. Specify a fasta file, gzipped or not, with the —sequence_filter sequences.fasta parameter. For further documentation, see the BBduk official website.
Metatdenovo can perform “digital normalization” of the reads before the assembly.
This will reduce coverage of highly abundant sequences and remove sequences that are below a threshold, and can be useful if the data set is too large to assemble but also potentially improve an assembly.
N.B. the digital normalization is done only for the assembly and the full set of sequences will be used for quantification.
To turn on digital normalization, use the
--bbnorm parameter and, if required, adjust the
Please, check the bbnorm documentation for further information about these programs and how digital normalization works. Remember to check Parameters page for the all options that can be used for this step.
By default, the pipeline uses Megahit (
--assembler megahit) to assemble the cleaned and trimmed reads to create the reference contigs.
Megahit is fast and it does not require a lot of memory to run, making it ideal for large sets of samples.
The workflow also supports RNAspades, (
--assembler rnaspades ) as an alternative.
You can also choose to input contigs from an assembly that you made outside the pipeline using the
--assembly file.fna (where
file.fna is the name of a fasta file with contigs) option.
ORF caller options
By default, the pipeline uses prodigal (
--orf_caller prodigal ) to call genes/ORFs from the assembly.
This is suitable for prokaryotes, as is the Prokka alternative (
The latter uses Prodigal internally making it suitable for prokaryotic genes.
It also performs functional annotation of ORFs.
For eukaryotic genes, we recommend users to use Transdecoder (
--orf_caller transdecoder) to call ORFs.
Taxonomic annotation options
Metatdenovo uses EUKulele as the main program for taxonomy annotation.
EUKulele can be run with different reference datasets.
The default dataset is PhyloDB (
--eukulele_db phylodb ) which works for mixed communities of prokaryotes and eukaryotes.
Other database options for running the pipeline are MMETSP (
--eukulele_db mmetsp; for marine protists) and GTDB (
--eukulele_db gtdb; for prokarytes
- PhyloDB: default, covers both prokaryotes and eukaryotes
- MMETSP: marine protists
- GTDB: prokaryotes, both bacteria and archaea
You can also provide your own database, see the EUKulele documentation documentation.
Databases are automatically downloaded by the workflow, but if you already have them available you can use the
--eukulele_dbpath path/to/db pointing
to the root directory of the EUKulele databases.
(The default for this parameter is
Please, check the EUKulele documentation for more information about the databases.
⚠️ There is currently a bug in the EUKulele program so that some databases properly do not download properly, check EUKulele issue. Until the developers have fixed this bug, we recommend downloading the database manually. To do so, follow these steps:
- Create conda environment:
- Download the database you need:
- Fix the problematic database tables:
Functional annotation options
Besides the functional annotation that the gene caller Prokka gives (see above) there are two general purpose functional annotation programs available
in the workflow: the eggNOG-mapper and KofamScan.
Both are suitable for both prokaryotic and eukaryotic genes and both are run by default, but can be skipped using the
--skip_kofamscan options respectivelly.
The tools use large databases which are downloaded automatically but paths can be provided by the user through the
--kofam_dir dir parameters respectively.
A more targeted annotation option offered by the workflow is the possibility for the user to provide a set of
HMMER HMM profiles through the
--hmmdir dir or
hmmfiles file0.hmm,file1.hmm,...,filen.hmm parameters.
Each HMM file will be used to search the amino acid sequences of the ORF set and the results will be summarized in a tab separated file in which each
ORF-HMM combination will be ranked according to score and E-value.
Example pipeline command with some common features
In this example, we are running metatdenovo with
rnaspades as assembler,
prokka as ORF caller,
eggnog for functional annotation and EUKulele with the GTDB database for taxonomic annotation.
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:
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/metatdenovo 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 uploaded 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