⚠️ Please read this documentation on the nf-core website: https://nf-co.re/reportho/usage
Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files.
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, and a header row as shown in the examples below.
--input '[path to samplesheet file]'The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 2 columns to match those defined in the tables below.
A final samplesheet file may look something like the one below:
id,query
BicD2,Q8TD16
HBB,P68871
or the one below, if you provide the sequence of the protein in FASTA format:
id,fasta
BicD2,/home/myuser/data/bicd2.fa
HBB,/home/myuser/data/hbb.fa
| Column | Description |
|---|---|
id |
User-defined identifier. It is used to identify output files for the protein. Can be anything descriptive, as long as it does not contain spaces. |
query |
The query of the user-specified type. It should be a valid Uniprot accession. |
fasta |
It should be a valid path to a FASTA file. |
An example Uniprot samplesheet and example FASTA samplesheet has been provided with the pipeline.
The typical command for running the pipeline is as follows:
nextflow run nf-core/reportho --input ./samplesheet.csv --outdir ./results -profile dockerThis 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>.
:::warning
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/reportho -profile docker -params-file params.yamlwith params.yaml containing:
input: './samplesheet.csv'
outdir: './results/'
<...>You can also generate such YAML/JSON files via nf-core/launch.
If you want to use local database copies for the run, you must provide the required files using the appropriate params. See the parameter documentation for details. Below you can find a list of files to provide, as named by the FTP service of the respective databases.
| Parameter | File name |
|---|---|
oma_path |
oma-groups.txt.gz |
oma_uniprot_path |
oma-uniprot.txt.gz |
oma_ensembl_path |
oma-ensembl.txt.gz |
oma_refseq_path |
oma-refseq.txt.gz |
panther_path |
AllOrthologs.txt |
eggnog_path |
1_members.tsv.gz |
eggnog_idmap_path |
latest.Eukaryota.tsv.gz |
If you need reduced versions of the local databases for testing, you can find them here. Note that they were designed to work with the test samplesheet and will likely not provide any result for other queries.
With large input sizes, you might want to run the pipeline locally, without runtime access to APIs. There are two main parameters used to achieve this. If you want to use local databases, set --local_databases to true. Remember to set --use_all to false to ensure the database step is run fully offline. If your input is especially large, you can also skip the initial online identification steps by setting --offline_run to true. Note that FASTA input will not work with this option enabled, and the pipeline will be aborted if this is attempted. You can check test_offline.config to see the required options for a fully offline run. Keep in mind that the options only affect ortholog finding, and the downstream analysis still requires connection to obtain sequences and structures.
While those options allow the pipeline to run its steps offline, the pipeline requires certain configuration files and container images that are downloaded from the internet. If you wish to run the pipeline on a machine without a connection, you can pre-download the required files with nf-core download. See the nf-core tools documentation for details.
Downstream analysis (i.e. MSA and phylogeny) relies on online resources to obtain sequences and structures, and thus cannot be run offline. For your convenience, it will be automatically disabled if you enable offline_run. Note that in case some sequences or structures cannot be obtained, the corresponding ortholog will be excluded from the alignment and phylogeny. In particular, only the orthologs with both a sequence and a structure available will be retained if use_structures is enabled.
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/reporthoIt 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/reportho 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 reproducibility, 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.
:::tip 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. :::
:::note 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.
:::info 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
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
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-edgeor 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.
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.
To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.
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.
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.
If you have any questions or issues please send us a message on Slack on the #configs channel.
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.
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).
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'