nf-core/detaxizer

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Introduction

nf-core/detaxizer is a pipeline to assess raw (meta)genomic data for contaminations and optionally filter reads which were classified as contamination. Default taxon classified as contamination is Homo sapiens.

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 4 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

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 4 columns to match those defined in the table below. For single-end short reads use the column short_reads_fastq_1, for long reads use the column long_reads_fastq_1.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 5 samples, showing all possible combinations of short and long reads.

sample,short_reads_fastq_1,short_reads_fastq_2,long_reads_fastq_1
SINGLE_END_SHORT,AEG588A1_S1_L002_R1_001.fastq.gz,,
PAIRED_END_SHORT,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,
SINGLE_END_LONG,,,AEG588A3_001.fastq.gz
SINGLE_END_SHORT_LONG,AEG588A4_S1_L002_R1_001.fastq.gz,,AEG588A4_001.fastq.gz
PAIRED_END_PLUS_LONG,AEG588A5_S1_L002_R1_001.fastq.gz,AEG588A5_S1_L002_R2_001.fastq.gz,AEG588A5_001.fastq.gz

An example samplesheet has been provided with the pipeline.

Databases

The databases used by detaxizer have an influence on the amount of false positives (classified as contamination although not originating from that taxon/taxa) and false negatives (not classified as the taxon/taxa defined as contamination although of such origin).

The task of decontamination has to be balanced out between false positives and false negatives depending on what is needed in your use case.

kraken2

To reduce false negatives a larger kraken2 database should be used. This comes at costs in terms of hardware requirements. For the largest kraken2 standard database (which can be found here) at least 100 GB of memory should be available, depending on the size of your data the required memory may be higher. For standard decontamination tasks the Standard-8 GB database can be used (which is the default), but it should always be kept in mind that this may lead to false negatives to some extend.

To build your own database refer to this site.

bbduk

bbduk uses a fasta file which contains sequences from the taxon/taxa classified as contamination. Default is the GRCh38 human reference genome. Provide a custom file using the fasta_bbduk parameter.

blastn

The blastn database is built from a fasta file. Default is the GRCh38 human reference genome. To decrease the amount of false negatives in this step or include different taxa, a database of several taxa can be used. The fasta containing desired sequences has to be provided to the pipeline by using the fasta_blastn parameter.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/detaxizer --input ./samplesheet.csv --outdir ./results -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.

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>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/detaxizer -profile docker -params-file params.yaml

with:

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

You can also generate such YAML/JSON files via nf-core/launch.

Before and after (if using the filter) the execution of the pipeline the headers inside the .fastq.gz files are renamed. This step is necessary to avoid difficulties with different header formats in the pipeline. The renamed headers will never be shown to you, except when looking into the work directory. Only the original read headers are shown in the results.

To change the taxon or taxonomic subtree which is classified by kraken2 as contamination use the tax2filter parameter (default Homo sapiens). The taxon has to be in the kraken2 database used, which can be specified using the kraken2db parameter.

To change what is classified by bbduk, a fasta containing the sequences of the contaminant taxon/taxa has to be provided using the fasta_bbduk parameter.

If you want to run bbduk use the --classification_bbduk flag. For running both classification steps and use the merged output for filtering, use both flags (--classification_kraken2 and --classification_bbduk).

To change the organism(s) which should be validated as contamination(s) by blasting against a database, you have to provide a fasta from which the blastn database is built using the fasta_blastn parameter. Also, if just one reference genome is needed for blastn and it is in igenomes.config use the according name (e.g. 'GRCh38') as genome parameter.

blastn can be turned on using the validation_blastn parameter.

Optionally enabling the filter can be done by using --enable_filter. There are two options for the input of the filter, either the raw reads or the preprocessed ones. The first is the default option. Also, for the definition of the reads to be filtered by their IDs two options are available. Either the default is taken, the output from the classification step (kraken2), or using the output from the blastn step.

If you want to output the removed reads, use --output_removed_reads.

Optional classification of the filtered (and removed) reads can be done using --classification_kraken2_post_filtering. This uses the kraken2 database provided by kraken2db.

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/detaxizer

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 nf-core/detaxizer 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.

Core Nextflow arguments

-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.

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-edge or later).
• 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.

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'