Introduction

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 3 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:

sample,datatype,datafile
sample1,hic,hic.cram
sample2,illumina,illumina.cram
sample2,illumina,illumina.cram

Full 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 may look something like the one below.

sample,datatype,datafile
sample1,hic,hic.cram
sample2,illumina,illumina.cram
sample3,ont,ont.cram

An example samplesheet has been provided with the pipeline.

Support for nf-core/fetchngs

The pipeline can also accept a samplesheet generated by the nf-core/fetchngs pipeline (tested with version 1.11.0). The pipeline then needs the --fetchngs_samplesheet true option and --align true, since the data files would all be unaligned.

Getting databases ready for the pipeline

The BlobToolKit pipeline can be run in many different ways. The default way requires access to several databases:

1. NCBI taxdump database
2. NCBI nucleotide BLAST database
3. UniProt reference proteomes database
4. BUSCO database

It is a good idea to put a date suffix for each database location so you know at a glance whether you are using the latest version. We are using the YYYY_MM format as we do not expect the databases to be updated more frequently than once a month. However, feel free to use DATE=YYYY_MM_DD or a different format if you prefer.

1. NCBI taxdump database

Create the database directory and move into the directory:

DATE=2023_03
TAXDUMP=/path/to/databases/taxdump_${DATE}
mkdir -p $TAXDUMP
cd $TAXDUMP

Retrieve and decompress the NCBI taxdump:

curl -L ftp://ftp.ncbi.nih.gov/pub/taxonomy/new_taxdump/new_taxdump.tar.gz | tar xzf -

2. NCBI nucleotide BLAST database

Create the database directory and move into the directory:

DATE=2023_03
NT=/path/to/databases/nt_${DATE}
mkdir -p $NT
cd $NT

Retrieve the NCBI blast nt database (version 5) files and tar gunzip them. We are using the && syntax to ensure that each command completes without error before the next one is run:

wget "ftp://ftp.ncbi.nlm.nih.gov/blast/db/v5/nt.???.tar.gz" -P $NT/ &&
for file in $NT/*.tar.gz; do
    tar xf $file -C $NT && rm $file;
done

3. UniProt reference proteomes database

You need diamond blast installed for this step. The easiest way is probably using conda. Make sure you have the latest version of Diamond (>2.x.x) otherwise the --taxonnames argument may not work.

Create the database directory and move into the directory:

DATE=2023_03
UNIPROT=/path/to/databases/uniprot_${DATE}
mkdir -p $UNIPROT
cd $UNIPROT

The UniProt Refseq_Proteomes_YYYY_MM.tar.gz file is very large (>160 GB) and will take a long time to download. The command below looks complex because it needs to get around the problem of using wildcards with wget and curl.

wget -q -O $UNIPROT/reference_proteomes.tar.gz \
  ftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/$(curl \
    -vs ftp.ebi.ac.uk/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/ 2>&1 | \
    awk '/tar.gz/ {print $9}')
tar xf reference_proteomes.tar.gz

# Create a single fasta file with all the fasta files from each subdirectory:
touch reference_proteomes.fasta.gz
find . -mindepth 2 | grep "fasta.gz" | grep -v 'DNA' | grep -v 'additional' | xargs cat >> reference_proteomes.fasta.gz

# create the accession-to-taxid map for all reference proteome sequences:
printf "accession\taccession.version\ttaxid\tgi\n" > reference_proteomes.taxid_map
zcat */*/*.idmapping.gz | grep "NCBI_TaxID" | awk '{print $1 "\t" $1 "\t" $3 "\t" 0}' >> reference_proteomes.taxid_map

# create the taxon aware diamond blast database
diamond makedb -p 16 --in reference_proteomes.fasta.gz --taxonmap reference_proteomes.taxid_map --taxonnodes $TAXDUMP/nodes.dmp --taxonnames $TAXDUMP/names.dmp -d reference_proteomes.dmnd

4. BUSCO databases

Create the database directory and move into the directory:

DATE=2023_03
BUSCO=/path/to/databases/busco_${DATE}
mkdir -p $BUSCO
cd $BUSCO

Download BUSCO data and lineages to allow BUSCO to run in offline mode:

wget -r -nH https://busco-data.ezlab.org/v5/data/
# the trailing slash after data is important. Otherwise wget doesn't get the subdirectories

# tar gunzip all folders that have been stored as tar.gz, in the same parent directories as where they were stored:
find v5/data -name "*.tar.gz" | while read -r TAR; do tar -C `dirname $TAR` -xzf $TAR; done

If you have GNU parallel installed, you can also use the command below which will run faster as it will run the decompression commands in parallel:

find v5/data -name "*.tar.gz" | parallel "cd {//}; tar -xzf {/}"

Changes from Snakemake to Nextflow

Commands

Snakemake

# Public Assemblies
run_btk_pipeline.sh GCA_ACCESSION

# Draft Assemblies
blobtoolkit-pipeline run --config YAML --threads INT --workdir DIR

Nextflow

# Public Assemblies
nextflow run sanger-tol/blobtoolkit --input SAMPLESHEET --fasta GENOME –-accession GCA_ACCESSION --taxon TAXON_ID --taxdump TAXDUMP_DB --blastp DMND_db --blastn BLASTN_DB --blastx BLASTX_DB

# Draft Assemblies
nextflow run sanger-tol/blobtoolkit --input SAMPLESHEET --fasta GENOME --taxon TAXON_ID --taxdump TAXDUMP_DB --blastp DMND_db --blastn BLASTN_DB --blastx BLASTX_DB

The Nextflow pipeline does not support taking input from the Yaml files of the Snakemake pipeline. Instead, Nextflow has a uniform way of setting input parameters on the command-line or via a JSON / Yaml file, see https://training.nextflow.io/basic_training/config/ for some examples.

Subworkflows

Here is a full list of snakemake subworkflows and their Nextflow couterparts:

• minimap.smk
  • Implemented as minimap_alignment.nf.
  • Optimised alignment is done using the sanger-tol/readmapping pipeline.
• windowmasker.smk
  • Implemented as part of prepare_genome.nf.
  • Genomes downloaded by sanger-tol/insdcdownload are already masked.
• chunk_stats.smk
  • Modified implementation as part of coverage_stats.nf.
  • BED file and additional statistics calculated using fasta_windows.
• busco.smk
  • Implemented as busco_diamond_blastp.nf.
• cov_stats.smk
  • Implemented as part of coverage_stats.nf.
  • Combining the various tsv files is done in subworkflow collate_stats.nf.
• window_stats.smk
  • Implemented as part of collate_stats.nf.
• diamond_blastp.smk
  • Implemented as busco_diamond_blastp.nf.
• diamond.smk
  • Implemented as run_blastx.nf.
• blastn.smk
  • Implemented as run_blastn.nf.
• blobtools.smk
  • Implemented as blobtools.nf.
• view.smk
  • Implemented as view.nf.

Software dependencies

List of tools for any given dataset can be fetched from the API, for example https://blobtoolkit.genomehubs.org/api/v1/dataset/id/CAJEUD01.1/settings/software_versions.

NB: Dependency has been added if only the Nextflow version information is present. NB: Dependency has been removed if only the Snakemake version information is present. NB: Dependency has been updated if bothe the Snakemake and Nextflow version information is present.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run sanger-tol/blobtoolkit --input samplesheet.csv --outdir <OUTDIR> --fasta genome.fasta -profile docker –-accession GCA_accession --taxon "species name" --taxdump /path/to/taxdump --blastp /path/to/buscogenes.dmnd --blastn /path/to/blastn.nt --blastx /path/to/buscoregions.dmnd

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>.

::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 sanger-tol/blobtoolkit -profile docker -params-file params.yaml

with params.yaml containing:

input: './samplesheet.csv'
outdir: './results/'
fasta: 'genome.fasta'
<...>

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 sanger-tol/blobtoolkit

Reproducibility

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 sanger-tol/blobtoolkit 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.

::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.

::

Core Nextflow arguments

:::note

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.

::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
• 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 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.

-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.

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'