nf-core/taxprofiler

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Introduction

nf-core/taxprofiler is a pipeline for highly-parallelised taxonomic classification and profiling of shotgun metagenomic data across multiple tools simultaneously. In addition to multiple classification and profiling tools, at the same time it allows you to performing taxonomic classification and profiling across multiple databases and settings per tool, as well as produces standardised output tables to allow immediate cross comparison of results between tools.

To run nf-core/taxprofiler, at a minimum two you require two inputs:

• a sequencing read samplesheet
• a database samplesheet

Both contain metadata and paths to the data of your input samples and databases.

When running nf-core/taxprofiler, every step and tool is ‘opt in’. To run a given classifier or profiler you must make sure to supply both a database in your <database>.csv and supply --run_<profiler> flag to your command. Omitting either will result in the profiling tool not executing.

nf-core/taxprofiler also includes optional pre-processing (adapter clipping, merge running etc.) or post-processing (visualisation) steps. These are also opt in with a --perform_<step> flag. In some cases, the pre- and post-processing steps may also require additional files. Please check the parameters tab of this documentation for more information.

Please see the rest of this page for information about how to prepare input samplesheets and databases and how to run Nextflow pipelines. See the parameters documentation for more information about specific options the pipeline also offers.

Samplesheet inputs

nf-core/taxprofiler can accept as input raw or preprocessed single- or paired-end short-read (e.g. Illumina) FASTQ files, long-read FASTQ files (e.g. Oxford Nanopore), or FASTA sequences (available for a subset of profilers).

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 6 columns, and a header row as shown in the examples below. Furthermother, nf-core/taxprofiler also requires a second comma-separated file of 3 columns with a header row as in the examples below.

This samplesheet is then specified on the command line as follows:

--input '[path to samplesheet file]' --databases '[path to database sheet 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 different runs FASTQ files of the same sample before performing profiling, when --perform_runmerging is supplied. Below is an example for the same sample sequenced across 3 lanes:

sample,run_accession,instrument_platform,fastq_1,fastq_2,fasta
2612,run1,ILLUMINA,2612_run1_R1.fq.gz,,
2612,run2,ILLUMINA,2612_run2_R1.fq.gz,,
2612,run3,ILLUMINA,2612_run3_R1.fq.gz,2612_run3_R2.fq.gz,
 

⚠️ Runs of the same sample sequenced on Illumina platforms with a combination of single and paired-end data will not be run-wise concatenated, unless pair-merging is specified. In the example above, run3 will be profiled independently of run1 and run2 if pairs are not merged.

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 6 columns to match those defined in the table below.

A final samplesheet file consisting of both single- and paired-end data, as well as long-read FASTA files may look something like the one below. This is for 6 samples, where 2612 has been sequenced twice.

sample,run_accession,instrument_platform,fastq_1,fastq_2,fasta
2611,ERR5766174,ILLUMINA,,,/<path>/<to>/fasta/ERX5474930_ERR5766174_1.fa.gz
2612,ERR5766176,ILLUMINA,/<path>/<to>/fastq/ERX5474932_ERR5766176_1.fastq.gz,/<path>/<to>/fastq/ERX5474932_ERR5766176_2.fastq.gz,
2612,ERR5766180,ILLUMINA,/<path>/<to>/fastq/ERX5474936_ERR5766180_1.fastq.gz,,
2613,ERR5766181,ILLUMINA,/<path>/<to>/fastq/ERX5474937_ERR5766181_1.fastq.gz,/<path>/<to>/fastq/ERX5474937_ERR5766181_2.fastq.gz,
ERR3201952,ERR3201952,OXFORD_NANOPORE,/<path>/<to>/fastq/ERR3201952.fastq.gz,,

⚠️ Input FASTQ and FASTA files must be gzipped

⚠️ While one can include both short-read and long-read data in one run, we recommend that you split these across two pipeline runs and database sheets (see below). This will allow classification optimisation for each data type, and make MultiQC run-reports more readable (due to run statistics having vary large number differences).

An example samplesheet has been provided with the pipeline.

Full database sheet

nf-core/taxprofiler supports multiple databases being classified/profiled against in parallel for each tool.

Databases can be supplied either in the form of a compressed .tar.gz archive of a directory containing all relevant database files or the path to a directory on the filesystem.

⚠️ nf-core/taxprofiler does not provide any databases by default, nor does it currently generate them for you. This must be performed manually by the user. See below for more information of the expected database files.

The pipeline takes the paths and specific classification/profiling parameters of the tool of these databases as input via a four column comma-separated sheet.

⚠️ To allow user freedom, nf-core/taxprofiler does not check for mandatory or the validity of non-file database parameters for correct execution of the tool - excluding options offered via pipeline level parameters! Please validate your database parameters (cross-referencing [parameters](https://nf-co.re/taxprofiler/parameters, and the given tool documentation) before submitting the database sheet! For example, if you don’t use the default read length - Bracken will require -r <read_length> in the db_params column.

An example database sheet can look as follows, where 7 tools are being used, and malt and kraken2 will be used against two databases each.

kraken2 will be run twice even though only having a single ‘dedicated’ database because specifying bracken implies first running kraken2 on the bracken database, as required by bracken.

tool,db_name,db_params,db_path
malt,malt85,-id 85,/<path>/<to>/malt/testdb-malt/
malt,malt95,-id 90,/<path>/<to>/malt/testdb-malt.tar.gz
bracken,db1,;-r 150,/<path>/<to>/bracken/testdb-bracken.tar.gz
kraken2,db2,--quick,/<path>/<to>/kraken2/testdb-kraken2.tar.gz
krakenuniq,db3,,/<path>/<to>/krakenuniq/testdb-krakenuniq.tar.gz
centrifuge,db1,,/<path>/<to>/centrifuge/minigut_cf.tar.gz
metaphlan3,db1,,/<path>/<to>/metaphlan3/metaphlan_database/
motus,db_mOTU,,/<path>/<to>/motus/motus_database/

For Bracken, if you wish to supply any parameters to either the Kraken or Bracken step you must have a semi-colon ; list as in db_params. This is to allow to specify the Kraken2 parameters before, and Bracken parameters after the ; as Bracken is a two step process. This is particularly important if you supply a Bracken database with a non-default read length parameter. If you do not have any parameters to specify, you can leave this as empty.

Column specifications are as follows:

💡 You can also specify the same database directory/file twice (ensuring unique db_names) and specify different parameters for each database to compare the effect of different parameters during classification/profiling.

nf-core/taxprofiler will automatically decompress and extract any compressed archives for you.

The (uncompressed) database paths (db_path) for each tool are expected to contain the contents of:

• Bracken: output of the combined kraken2-build and bracken-build process.
• Centrifuge: output of centrifuge-build.
• DIAMOND: output of diamond makedb.
• Kaiju: output of kaiju-makedb.
• Kraken2: output of kraken2-build command(s).
• KrakenUniq: output of krakenuniq-build command(s).
• MALT output of malt-build.
• MetaPhlAn3: output of with metaphlan --install or downloaded from links on the MetaPhlAn3 wiki.
• mOTUs: is composed of code and database together.

Click the links in the list above for short quick-reference tutorials how to generate custom databases for each tool.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/taxprofiler --input samplesheet.csv --databases databases.csv --outdir <OUTDIR> -profile docker --run_<TOOL1> --run_<TOOL2>

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

When running nf-core/taxprofiler, every step and tool is ‘opt in’. To run a given classifier/profiler you must make sure to supply both a database in your <database>.csv and supply --run_<profiler> flag to your command. Omitting either will result in the classification/profiling tool not executing. If you wish to perform pre-processing (adapter clipping, merge running etc.) or post-processing (visualisation) steps, these are also opt in with a --perform_<step> flag. In some cases, the pre- and post-processing steps may also require additional files. Please check the parameters tab of this documentation for more information.

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.

Sequencing quality control

FastQC gives general quality metrics about your reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. nf-core taxprofiler offers falco as an drop-in replacement, with supposedly better improvement particularly for long reads.

Preprocessing Steps

nf-core/taxprofiler offers four main preprocessing steps for preprocessing raw sequencing reads:

• Read processing: adapter clipping and pair-merging.
• Complexity filtering: removal of low-sequence complexity reads.
• Host read-removal: removal of reads aligning to reference genome(s) of a host.
• Run merging: concatenation of multiple FASTQ chunks/sequencing runs/libraries of a sample.

Read Processing

Raw sequencing read processing in the form of adapter clipping and paired-end read merging can be activated via the --perform_shortread_qc or --perform_longread_qc flags.

It is highly recommended to run this on raw reads to remove artifacts from sequencing that can cause false positive identification of taxa (e.g. contaminated reference genomes) and/or skews in taxonomic abundance profiles. If you have public data, normally these should have been corrected for, however you should still check that these steps have indeed been already performed.

There are currently two options for short-read preprocessing: fastp or adapterremoval.

For adapter clipping, you can either rely on the tool’s default adapter sequences, or supply your own adapters (--shortread_qc_adapter1 and --shortread_qc_adapter2) By default, paired-end merging is not activated. In this case paired-end ‘alignment’ against the reference databases is performed where supported, and if not, supported pairs will be independently classified/profiled. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for classification/profiling (--shortread_qc_mergepairs and --shortread_qc_includeunmerged). You can also turn off clipping and only perform paired-end merging, if requested. This can be useful when processing data downloaded from the ENA, SRA, or DDBJ (--shortread_qc_skipadaptertrim). Both tools support length filtering of reads and can be tuned with --shortread_qc_minlength. Performing length filtering can be useful to remove short (often low sequencing complexity) sequences that result in unspecific classification and therefore slow down runtime during classification/profiling, with minimal gain.

There is currently one option for long-read Oxford Nanopore processing: porechop.

For both short-read and long-read preprocessing, you can optionally save the resulting processed reads with --save_preprocessed_reads.

Complexity Filtering

Complexity filtering can be activated via the --perform_shortread_complexityfilter flag.

Complexity filtering is primarily a run-time optimisation step. It is not necessary for accurate taxonomic classification/profiling, however it can speed up run-time of each tool by removing reads with low-diversity of nucleotides (e.g. with mono-nucleotide - AAAAAAAA, or di-nucleotide repeats GAGAGAGAGAGAGAG) that have a low-chance of giving an informative taxonomic ID as they can be associated with many different taxa. Removing these reads therefore saves computational time and resources.

There are currently three options for short-read complexity filtering: bbduk, prinseq++, and fastp.

There is one option for long-read quality filtering: Filtlong

The tools offer different algorithms and parameters for removing low complexity reads and quality filtering. We therefore recommend reviewing the pipeline’s parameter documentation and the documentation of the tools (see links above) to decide on optimal methods and parameters for your dataset.

You can optionally save the FASTQ output of the run merging with the --save_complexityfiltered_reads. If running with fastp, complexity filtering happens inclusively within the earlier shortread preprocessing step. Therefore there will not be an independent pipeline step for complexity filtering, and no independent FASTQ file (i.e. --save_complexityfiltered_reads will be ignored) - your complexity filtered reads will also be in the fastp/ folder in the same file(s) as the preprocessed read.

⚠️ For nanopore data: we do not recommend performing any read preprocessing or complexity filtering if you are using ONTs Guppy toolkit for basecalling and post-processing.

Host-Read Removal

Removal of possible-host reads from FASTQ files prior classification/profiling can be activated with --perform_shortread_hostremoval or --perform_longread_hostremoval.

Similarly to complexity filtering, host-removal can be useful for runtime optimisation and reduction in misclassified reads. It is not always necessary to report classification of reads from a host when you already know the host of the sample, therefore you can gain a run-time and computational advantage by removing these prior typically resource-heavy classification/profiling with more efficient methods. Furthermore, particularly with human samples, you can reduce the number of false positives during classification/profiling that occur due to host-sequence contamination in reference genomes on public databases.

nf-core/taxprofiler currently offers host-removal via alignment against a reference genome with Bowtie2 for short reads and minimap2 for long reads, and the use of the unaligned reads for downstream classification/profiling.

You can supply your reference genome in FASTA format with --hostremoval_reference. You can also optionally supply a directory containing pre-indexed Bowtie2 index files with --shortread_hostremoval_index or a minimap2 .mmi file for --longread_hostremoval_index, however nf-core/taxprofiler will generate these for you if necessary. Pre-supplying the index directory or files can greatly speed up the process, and these can be re-used.

💡 If you have multiple taxa or sequences you wish to remove (e.g., the host genome and then also PhiX - common quality-control reagent during sequencing) you can simply concatenate the FASTAs of each taxa or sequences into a single reference file.

Run Merging

For samples that may have been sequenced over multiple runs, or for FASTQ files split into multiple chunks, you can activate the ability to merge across all runs or chunks with --perform_runmerging.

For more information how to set up your input samplesheet, see Multiple runs of the same sample.

Activating this functionality will concatenate the FASTQ files with the same sample name after the optional preprocessing steps and before classification/profiling. Note that libraries with runs of different pairing types will not be merged and this will be indicated on output files with a _se or _pe suffix to the sample name accordingly.

You can optionally save the FASTQ output of the run merging with the --save_runmerged_reads.

Classification and Profiling

The following sections provide tips and suggestions for running the different taxonomic classification and profiling tools within the pipeline. For advice and/or guidance whether you should run a particular tool on your specific data, please see the documentation of each tool!

An important distinction between the different tools in included in the pipeline is classification versus profiling. Taxonomic classification is concerned with simply detecting the presence of species in a given sample. Taxonomic profiling involves additionally estimating the abundance of each species.

Note that not all taxonomic classification tools (e.g. Kraken, MALT, Kaiju) performs profiling, but all taxonomic profilers (e.g. MetaPhlAn, mOTUs, Bracken) must perform some form of classification prior to profiling.

For advice as to which tool to run in your context, please see the documentation of each tool.

🖊️ If you would like to change this behaviour, please contact us on the nf-core slack and we can discuss this.

Not all tools currently have dedicated tips, suggestions and/or recommendations, however we welcome further contributions for existing and additional tools via pull requests to the nf-core/taxprofiler repository!

Bracken

You must make sure to also activate Kraken2 to run Bracken in the pipeline.

It is unclear whether Bracken is suitable for running long reads, as it makes certain assumptions about read lengths. Furthemore, during testing we found issues where Bracken would fail on the long-read test data.

Therefore currently nf-core/taxprofiler does not run Bracken on data specified as being sequenced with OXFORD_NANOPORE in the input samplesheet.

Centrifuge

Centrifuge currently does not accept FASTA files as input, therefore no output will be produced for these input files.

DIAMOND

DIAMOND only allows output of a single file format at a time, therefore parameters such --diamond_save_reads supplied will result in only aligned reads in SAM format will be produced, no taxonomic profiles will be available. Be aware of this when setting up your pipeline runs, depending on your particular use case.

Kaiju

Currently, no specific tips or suggestions.

Kraken2

Currently, no specific tips or suggestions.

KrakenUniq

Currently, no specific tips or suggestions.

MALT

MALT does not support paired-end reads alignment (unlike other tools), therefore nf-core/taxprofiler aligns these as indepenent files if read-merging is skipped. If you skip merging, you can sum or average the results of the counts of the pairs.

Krona can only be run on MALT output if path to Krona taxonomy database supplied to --krona_taxonomy_directory. Therefore if you do not supply the a Krona directory, Krona plots will not be produced for MALT.

MetaPhlAn3

MetaPhlAn3 currently does not accept FASTA files as input, therefore no output will be produced for these input files.

mOTUs

mOTUs currently does not accept FASTA files as input, therefore no output will be produced for these input files.

Post Processing

Visualisation

nf-core/taxprofiler supports generation of Krona interactive pie chart plots for the following compatible tools.

• Kraken2
• Centrifuge
• Kaiju
• MALT

⚠️ MALT KRONA plots cannot be generated automatically, you must also specify a Krona taxonomy directory with --krona_taxonomy_directory if you wish to generate these.

Multi-Table Generation

In addition to per-sample profiles, the pipeline also supports generation of ‘native’ multi-sample taxonomic profiles (i.e., those generated by the taxonomic profiling tools themselves or additional utility scripts provided by the tool authors).

These are executed on a per-database level. I.e., you will get a multi-sample taxon table for each database you provide for each tool and will be placed in the same directory as the directories containing the per-sample profiles.

The following tools will produce multi-sample taxon tables:

• Bracken (via bracken’s combine_bracken_outputs.py script)
• Centrifuge (via KrakenTools’ combine_kreports.py script)
• Kaiju (via Kaiju’s kaiju2table tool)
• Kraken2 (via KrakenTools’ combine_kreports.py script)
• MetaPhlAn3 (via MetaPhlAn’s merge_metaphlan_tables.py script)
• mOTUs (via the motus merge command)

Note that the multi-sample tables from these folders are not inter-operable with each other as they can have different formats.

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/taxprofiler

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/taxprofiler 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.

Core Nextflow arguments

NB: 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, 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.

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
• 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 or Charliecloud.

-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.

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:

[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)'
 
Caused by:
    Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)
 
Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
        <TRUNCATED>
 
Command exit status:
    137
 
Command output:
    (empty)
 
Command error:
    .command.sh: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
    /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb
 
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run`

For beginners

A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters --max_cpus, --max_memory, and --max_time. Based on the error above, you have to increase the amount of memory. Therefore you can go to the parameter documentation of rnaseq and scroll down to the show hidden parameter button to get the default value for --max_memory. In this case 128GB, you than can try to run your pipeline again with --max_memory 200GB -resume to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below.

Advanced option on process level

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 modules/nf-core/star/align/main.nf. 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 process_high. The Nextflow 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.

process {
    withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' {
        memory = 100.GB
    }
}

NB: We specify the full process name i.e. NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN in 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.

Updating containers (advanced users)

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 -c custom.config.

1. Check the default version used by the pipeline in the module file for Pangolin

2. Find the latest version of the Biocontainer available on Quay.io

3. Create the custom config accordingly:

   • For Docker:

     process {
         withName: PANGOLIN {
             container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0'
         }
     }

   • For Singularity:

     process {
         withName: PANGOLIN {
             container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0'
         }
     }

   • For Conda:

     process {
         withName: PANGOLIN {
             conda = 'bioconda::pangolin=3.0.5'
         }
     }

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 -resume ability of the pipeline will be compromised and it will restart from scratch.

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'

Tutorials

Retrieving databases or building custom databases

Not all taxonomic profilers provide ready-made or default databases. Here we will give brief guidance on how to build custom databases for each supported taxonomic profiler.

You should always consult the documentation of each tool for more information, as here we only provide short minimal-tutorials as quick reference guides (with no guarantee they are up to date).

The following tutorials assumes you already have the tool available (e.g. installed locally, or via conda, docker etc.), and you have already downloaded the FASTA files you wish to build into a database.

Bracken custom database

Bracken does not require an independent database nor not provide any default databases for classification/profiling, but rather builds upon Kraken2 databases. See Kraken2 for more information on how to build these.

In addition to a Kraken2 database, you also need to have the (average) read lengths (in bp) of your sequencing experiment, the K-mer size used to build the Kraken2 database, and Kraken2 available on your machine.

bracken-build -d <KRAKEN_DB_DIR> -k <KRAKEN_DB_KMER_LENGTH> -l <READLENGTH>

🛈 You can speed up database construction by supplying the threads parameter (-t).

🛈 If you do not have Kraken2 in your $PATH you can point to the binary with -x /<path>/<to>/kraken2.

You can follow Bracken tutorial for more information.

Centrifuge custom database

To build a custom Centrifuge database, a user needs to download taxonomy files, make a custom seqid2taxid.map and combine the fasta files together.

In total, you need four components: a tab-separated file mapping sequence IDs to taxonomy IDs (--conversion-table), a tab-separated file mapping taxonomy IDs to their parents and rank, up to the root of the tree (--taxonomy-tree), a pipe-separated file mapping taxonomy IDs to a name (--name-table), and the reference sequences.

An example of custom seqid2taxid.map:

 NC_001133.9 4392
 NC_012920.1 9606
 NC_001134.8 4392
 NC_001135.5 4392

centrifuge-download -o taxonomy taxonomy
cat *.{fa,fna} > input-sequences.fna
centrifuge-build -p 4 --conversion-table seqid2taxid.map --taxonomy-tree taxonomy/nodes.dmp --name-table taxonomy/names.dmp input-sequences.fna taxprofiler_cf

For the Centrifuge custom database documentation, see here.

DIAMOND custom database

To create a custom database for DIAMOND, the user should download and unzip the NCBI’s taxonomy files and the input FASTA files.

The download and build steps are as follows:

wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdmp.zip
unzip taxdmp.zip
 
## warning: large file!
wget ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/accession2taxid/prot.accession2taxid.FULL.gz
 
## warning: takes a long time!
cat ../raw/*.faa | diamond makedb -d testdb-diamond --taxonmap prot.accession2taxid.FULL.gz --taxonnodes nodes.dmp --taxonnames names.dmp
 
## clean up
rm *dmp *txt *gz *prt *zip

A detailed description can be found here

Kaiju custom database

To build a kaiju database, you need three components: a FASTA file with the protein sequences ,the NCBI taxonomy dump files, and you need to define the uppercase characters of the standard 20 amino acids you wish to include.

⚠️ The headers of the protein fasta file must be numeric NCBI taxon identifiers of the protein sequences.

To download the NCBI taxonomy files, please run the following commands:

wget https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/new_taxdump/new_taxdump.zip
unzip new_taxdump.zip

To build the database, run the following command (the contents of taxdump must be in the same location where you run the command):

kaiju-mkbwt -a ACDEFGHIKLMNPQRSTVWY -o proteins proteins.faa
kaiju-mkfmi proteins

🛈 You can speed up database construction by supplying the threads parameter (-t).

For the Kaiju database construction documentation, see here.

Kraken2 custom database

To build a Kraken2 database you need two components: a taxonomy (consisting of names.dmp, nodes.dmp, and *accession2taxid) files, and the FASTA files you wish to include.

To pull the NCBI taxonomy, you can run the following:

kraken2-build --download-taxonomy --db <YOUR_DB_NAME>

You can then add your FASTA files with the following build command.

kraken2-build --add-to-library *.fna --db <YOUR_DB_NAME>

You can repeat this step multiple times to iteratively add more genomes prior building.

Once all genomes are added to the library, you can build the database (and optionally clean it up):

kraken2-build --build --db <YOUR_DB_NAME>
kraken2-build --clean --db <YOUR_DB_NAME>

You can then add the <YOUR_DB_NAME>/ path to your nf-core/taxprofiler database input sheet.

You can follow the Kraken2 tutorial for a more detailed description.

KrakenUniq custom database

For any KrakenUniq database, you require: taxonomy files, the FASTA files you wish to include, a seqid2mapid file, and a k-mer length.

First you must make a seqid2taxid.map file which is a two column text file containing the FASTA sequence header and the NCBI taxonomy ID for each sequence:

MT192765.1  2697049

Then make a directory (<DB_DIR_NAME>/), containing the seqid2taxid.map file, and your FASTA files in a subdirectory called library/ (these FASTA files can be symlinked). You must then run the taxonomy command on the <DB_DIR_NAME>/ directory, and then build it.

mkdir -p <DB_DIR_NAME>/library
mv `seqid2taxid.map` <DB_DIR_NAME>/
mv *.fna  <DB_DIR_NAME>/library
krakenuniq-download --db <DB_DIR_NAME>  taxonomy
krakenuniq-build --db <DB_DIR_NAME> --kmer-len 31

🛈 You can speed up database construction by supplying the threads parameter (--threads) to krakenuniq-build.

Please see the KrakenUniq documentation for more information.

MALT custom database

To build a MALT database, you need the FASTA files to include, and an (unzipped) MEGAN mapping ‘db’ file for your FASTA type. In addition to the input directory, output directory, and the mapping file database, you also need to specify the sequence type (DNA or Protein) with the -s flag.

malt-build -i <path>/<to>/<fasta>/*.{fna,fa,fasta} -a2t <path>/<to>/<map>.db -d <YOUR_DB_NAME>/  -s DNA

You can then add the <YOUR_DB_NAME>/ path to your nf-core/taxprofiler database input sheet.

⚠️ MALT generates very large database files and requires large amounts of RAM. You can reduce both by increasing the step size -st (with a reduction in sensitivity).

🛈 MALT-build can be multi-threaded with -t to speed up building.

See the MALT manual for more information.

MetaPhlAn3 custom database

MetaPhlAn3 does not allow (easy) construction of custom databases. Therefore we recommend to use the prebuilt database of marker genes that is provided by the developers.

To do this you need to have MetaPhlAn3 installed on your machine.

metaphlan --install --bowtie2db <YOUR_DB_NAME>/

You can then add the <YOUR_DB_NAME>/ path to your nf-core/taxprofiler database input sheet.

🛈 It is generally not recommended to modify this database yourself, thus this is currently not supported in the pipeline. However, it is possible to customise the existing database by adding your own marker genomes following the instructions here.

🖊️ If using your own database is relevant for you, please contact the nf-core/taxprofiler developers on the nf-core slack and we will investigate supporting this.

More information on the MetaPhlAn3 database can be found here.

mOTUs custom database

mOTUs does not provide the ability to construct custom databases. Therefore we recommend to use the the prebuilt database of marker genes provided by the developers.

To do this you need to have mOTUs installed on your machine.

motus downloadDB

Then supply the db_mOTU/ path to your nf-core/taxprofiler database input sheet.

⚠️ The db_mOTU/ directory may be downloaded to somewhere in your Python’s site-package directory. You will have to find this yourself as the exact location varies depends on installation method.

More information on the mOTUs database can be found here.

Troubleshooting and FAQs

I get a warning during centrifuge_kreport process with exit status 255

When a sample has insufficient hits for abundance estimation, the resulting report.txt file will be empty.

When trying to convert this to a kraken-style report, the conversion tool will exit with a status code 255, and provide a WARN.

This is not an error nor a failure of the pipeline, just your sample has no hits to the provided database when using centrifuge.