nf-core/bactmap

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Introduction

nf-core/bactmap is a bioinformatics best-practice analysis pipeline for mapping short (Illumina) and long reads (Oxford Nanopore) from bacterial WGS to a reference sequence, creating filtered VCF files and making pseudogenomes based on high quality positions in the VCF files.

In addition to this page, you can find additional usage information on the following pages:

• Tutorials
• FAQ and Troubleshooting

General Usage

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

• a sequencing read samplesheet
• a reference genome

The samplesheet contains metadata and paths to the data of your input samples.

nf-core/bactmap includes optional pre-processing (adapter clipping, merge running etc.) or post-processing (visualisation) steps. These are 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/bactmap can accept as input raw or preprocessed single- or paired-end short-read (e.g. Illumina) FASTQ files and long-read FASTQ files (e.g. Oxford Nanopore).

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

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

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate different runs FASTQ files of the same sample before performing mapping and variant calling, 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
2612,lane1,ILLUMINA,2612_lane1_R1.fq.gz,ILLUMINA,2612_lane1_R2.fq.gz
2612,lane2,ILLUMINA,2612_lane2_R1.fq.gz,ILLUMINA,2612_lane2_R2.fq.gz
2612,lane3,ILLUMINA,2612_lane3_R1.fq.gz,

::: info Please note that the column name run_accession follows the definition of an ENA ‘run’. A ‘run’ corresponds to a single or paired-end set of demultiplexed FASTQs. Given that demultiplexing of a given library happens per lane, each sequencing pair from each lane is a ‘run’. Therefore, for each sample, you may get multiple ‘runs’ consisting of both lanes (of the same library) and sequencing libraries. Therefore ensure that each run_accession ID is unique, even if from the same sample! :::

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 5 columns to match those defined in the table below.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 5 samples, where 2612 has been sequenced twice.

sample,run_accession,instrument_platform,fastq_1,fastq_2
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,

An example samplesheet has been provided with the pipeline.

Reference genome

The reference genome must be provided in FASTA format. The pipeline will automatically index the reference genome using bwa index or bowtie2 build and samtools faidx if not already indexed. The reference genome can be provided as a local file or a URL. The pipeline will automatically download the reference genome if a URL is provided. The reference genome can be specified on the command line as follows:

--fasta '[path to reference genome]'

The reference genome can be a single FASTA file or a multi-FASTA file. The reference genome can also be a gzipped FASTA file. The pipeline will automatically unzip the file if it is gzipped.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/bactmap --input ./samplesheet.csv --outdir ./results --fasta reference.fasta -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/bactmap -profile docker -params-file params.yaml

with:

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

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

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 bactmap offers falco as an drop-in replacement, with supposedly better improvement particularly for long reads.

Preprocessing Steps

nf-core/bactmap offers two main preprocessing steps for preprocessing raw sequencing reads:

• Read processing: adapter clipping and pair-merging.
• 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. If paired-end merging is activated you can also specify whether to include unmerged reads in the reads sent for mapping/variant calling (--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.

There are currently two options for long-read Oxford Nanopore processing: porechop, porechop_abi.

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

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 mapping/variant calling. 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.

Subsampling reads

Some sequencing runs may be too large to process in a reasonable time. In these cases, you can use the --perform_subsampling flag to randomly subsample your reads to a specified depth of coverage per sample. This is done before the mapping steps. By default, this step is activated for all samples.

Read mapping

The nf-core/bactmap pipeline provides two strategies to map reads to a reference genome:

• Short-read mapping: bwa-mem2 or bowtie2
• Long-read mapping: minimap2

By default, the pipeline will use bowtie2 for short-read mapping. You can change this with the shortread_mapping_tool parameter.

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/bactmap

Reproducibility

It is a good idea to specify the 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/bactmap releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse 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 check if your system is supported, 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 environment.

• 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 pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (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 the container or conda environment used by a pipeline steps 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'