taxprofiler/docs/usage.md

nf-core/taxprofiler: Usage

⚠️ Please read this documentation on the nf-core website: https://nf-co.re/taxprofiler/usage

Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files.

Introduction

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).

⚠️ Input FASTQ and FASTA files must be gzipped

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,,

Column	Description
sample	Unique sample name [required].
run_accession	Run ID or name unique for each (pairs of) file(s) .Can also supply sample name again here, if only a single run was generated [required].
instrument_platform	Sequencing platform reads generated on, selected from the EBI ENA controlled vocabulary [required].
fastq_1	Path or URL to sequencing reads or for Illumina R1 sequencing reads in FASTQ format. GZipped compressed files accepted. Can be left empty if data in FASTA is specifed. Cannot be combined with fasta.
fastq_2	Path or URL to Illumina R2 sequencing reads in FASTQ format. GZipped compressed files accepted. Can be left empty if single end data. Cannot be combined with fasta.
fasta	Path or URL to long-reads or contigs in FASTA format. GZipped compressed files accepted. Can be left empty if data in FASTA is specifed. Cannot be combined with fastq_1 or fastq_2.

An example samplesheet has been provided with the pipeline.

Full database sheet

nf-core/taxprofiler supports multiple databases being profiled 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. The pipeline takes the locations and specific profiling parameters of the tool of these databases as input via a four column comma-separated sheet.

⚠️ 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.

An example database sheet can look as follows, where 5 tools are being used, and malt and kraken2 will be used against two databases each. This is because specifying bracken implies first running kraken2 on the same database.

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,,/<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/

Column specifications are as follows:

Column	Description
tool	Taxonomic profiling tool (supported by nf-core/taxprofiler) that the database has been indexed for [required]. Please note that bracken also implies running kraken2 on the same database.
db_name	A unique name per tool for the particular database [required]. Please note that names need to be unique across both kraken2 and bracken as well, even if re-using the same database.
db_params	Any parameters of the given taxonomic profiler that you wish to specify that the taxonomic profiling tool should use when profiling against this specific. Can be empty to use taxonomic profiler defaults. Must not be surrounded by quotes [required]. We generally do not recommend specifying parameters here that turn on/off saving of output files or specifying particular file extensions - this should be already addressed via pipeline parameters.
db_path	Path to the database. Can either be a path to a directory containing the database index files or a .tar.gz file which contains the compressed database directory with the same name as the tar archive, minus .tar.gz [required].

💡 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 profiling.

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

Expected (uncompressed) database files for each tool are as follows:

• Bracken output of a combined kraken2- and bracken-build process.
• Centrifuge output of centrifuge-build.
• DIAMOND output of diamond makedb. to generate taxonomic profile. See DIAMOND documentation.
• 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 generated with metaphlan --install or downloaded from links on the MetaPhlAn3 wiki.
• mOTUs is composed of code and database together.

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 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. 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

• 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 this is the case.

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 profiled. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for 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 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 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 Removal

Removal of possible-host reads from FASTQ files prior 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 profiling with more efficient methods. Furthermore, particularly with human samples, you can reduce the number of false positives during 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, and the use of the unaligned reads for downstream 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 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.

Profiling

The following suggestion gives you some tips and suggestions regarding running some of the different tools specifically within the pipeline. For advice as to which tool to run in your context, please see the documentation of each tool.

Bracken

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 nf-core/taxprofiler does not run Bracken on data specified as being sequenced with OXFORD_NANOPORE in the input samplesheet. If you would like to change this behaviour, please contact us on the nf-core slack and we can discuss this.

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 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.

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.

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

Here we will give brief guidance on how to build databases for each supported taxonomic profiler. You should always consult the documentation of each tool for more information, here we provide these 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 provide any default databases for profiling, but rather building 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.

Expected files in database directory

• bracken
  • hash.k2d
  • opts.k2d
  • taxo.k2d
  • database.kraken
  • database100mers.kmer_distrib
  • database100mers.kraken
  • database150mers.kmer_distrib
  • database150mers.kraken

You can follow Bracken tutorial for more information. Alternatively, you can use one of the indexes that can be found here.

Centrifuge custom database

Centrifuge allows the user to build custom databases. The user should download taxonomy files, make custom seqid2taxid.map and combine the fasta files together. You need four components: a tab-separated file mapping sequence IDs to taxonomy IDs (--conversion-table), a \t|\t-separated file mapping taxonomy IDs to their parents and rank, up to the root of the tree (--taxonomy-tree), a '|'-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

Expected files in database directory

• centrifuge
  • <database_name>.<number>.cf
  • <database_name>.<number>.cf
  • <database_name>.<number>.cf
  • <database_name>.<number>.cf

DIAMOND custom database

To create a custom database for DIAMOND, the user should download and unzip the NCBI's taxonomy files. The makedb needs to be executed afterwards. A detailed description can be found here

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

rm *dmp *txt *gz *prt *zip

Expected files in database directory

• diamond
  • <database_name>.dmnd

Kaiju custom database

It is possible to create custom databases with Kaiju. To build a kaiju database, you need three components: a FASTA file with the protein sequences (the headers are the numeric NCBI taxon identifiers of the protein sequences), number of threads and you need to define the uppercase characters of the standard 20 amino acids.

kaiju-mkbwt -n 5 -a ACDEFGHIKLMNPQRSTVWY -o proteins proteins.faa
kaiju-mkfmi proteins

Expected files in database directory

• kaiju
  • kaiju_db_*.fmi
  • nodes.dmp
  • names.dmp

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 install pulling 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.

You can also automatical download and add 'standard' libraries provided by Kraken2 (e.g. bacteria on RefSeq)

kraken2-build --download-library bacteria --db <YOUR_DB_NAME>

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.

Expected files in database directory

• kraken2
  • opts.k2d
  • hash.k2d
  • taxo.k2d

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

KrakenUniq custom database

KrakenUniq allows to re-use Kraken(2) databases (see above) however with some restrictions. KrakenUniq also provides you with the ability to auto-download and build 'standard' NCBI reference datasets.

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

To auto-download and build a 'standard' database of Bacteria and Archaea genomes from RefSeq, you can run something like:

krakenuniq-download --db <DB_DIR_NAME> taxonomy
krakenuniq-download --db <DB_DIR_NAME> --threads 10 --dust refseq/bacteria refseq/archaea
krakenuniq-build --db <DB_DIR_NAME> --kmer-len 31 --threads 10 --taxids-for-genomes --taxids-for-sequences

This will download all the required files for you and build the database.

Alternatively, if you want to build your own you first 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/. 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).

Expected files in database directory

• krakenuniq
  • opts.k2d
  • hash.k2d
  • taxo.k2d
  • database.idx
  • taxDB

Please see the KrakenUniq documentation for more information.

#### MALT custom database

MALT does not provide any default databases for profiling, therefore you must build your own.
You need FASTA files to include, and an (unzipped) [MEGAN mapping 'db' file](https://software-ab.informatik.uni-tuebingen.de/download/megan6/) 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.

```bash
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.

Expected files in database directory

• malt
  • ref.idx
  • taxonomy.idx
  • taxonomy.map
  • index0.idx
  • table0.idx
  • table0.db
  • ref.inf
  • ref.db
  • taxonomy.tre

See the MALT manual for more information.

MetaPhlAn3 custom database

MetaPhlAn3 provides a prebuilt database of marker genes. This must be downloaded by the user. 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.

Expected files in database directory

• metaphlan3
  • mpa_v30_CHOCOPhlAn_201901.pkl
  • mpa_v30_CHOCOPhlAn_201901.pkl
  • mpa_v30_CHOCOPhlAn_201901.fasta
  • mpa_v30_CHOCOPhlAn_201901.3.bt2
  • mpa_v30_CHOCOPhlAn_201901.4.bt2
  • mpa_v30_CHOCOPhlAn_201901.1.bt2
  • mpa_v30_CHOCOPhlAn_201901.2.bt2
  • mpa_v30_CHOCOPhlAn_201901.rev.1.bt2
  • mpa_v30_CHOCOPhlAn_201901.rev.2.bt2
  • mpa_latest

More information on the MetaPhlAn3 database can be found here.

mOTUs custom database

mOTUs provides a prebuilt database of marker genes.

This must be downloaded by the user. 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.

It is not possible to create a custom mOTUs database.

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.