Introduction Usage Parameters Output Results Releases

Introduction

This pipeline maps short reads (usually Illumina) to a bacterial reference (usually of the same species and the closest high quality genome available). For this purpose you will require a set of paired read files (single read file version coming in future version) and a reference genome in fasta format. The read file pairs are specified in a sample sheet. Please note although the pipeline can handle multiple contigs within the reference sequence, it is recommended that plasmid records are removed leaving only the chromosomal records (usually one chromosome in most bacteria) since plasmids are often acquired horizontally and are evolving at a different rate to the chromosome.

Samplesheet input

You will need to create a samplesheet file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the example below.

--input '[path to samplesheet file]'
sample,fastq_1,fastq_2
G18582004,fastqs/G18582004_1.fastq.gz,fastqs/G18582004_2.fastq.gz
G18756254,fastqs/G18756254_1.fastq.gz,fastqs/G18756254_2.fastq.gz
G18582006,fastqs/G18582006_1.fastq.gz,fastqs/G18582006_2.fastq.gz

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/bactmap --input samplesheet.csv --reference chromosome.fasta -profile docker

Please note that although the reference is named chromosome.fasta in this example command it could be named anything BUT it is recommended that the reference only include chromosomal sequence (see note in introduction).

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
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Optional parameters

By default the pipeline does not perform the following steps but they can be enabled by adding the appropriate parameter to the command line:

  • trim reads --trim
  • remove recombiination using gubbins --remove_recombination
  • build a RapidNJ tree --rapidnj
  • build a FastTree tree --fasttree
  • build an IQ-TREE tree --iqtree
  • build a RAxML-NG tree --raxmlng

By default the pipeline subsamples reads to an estimated depth of 100x.
This can be turned off using the --subsampling_off parameter. The desired coverage depth can be changed using the subsampling_depth_cutoff parameter

The steps in the pipeline have sensible defaults but complete control of the arguments passed to the software tools can be achieved by overriding the software arguments found in the modules.config file with a custom config. For example the default args for IQ-TREE which specify using ModelFinder to find the best fit model could be overridden to specify a specific model. In the modules.config file these are specified as:

  'iqtree' {
      args = '-alrt 1000 -B 1000 -m MFP -czb'
      publish_dir = 'iqtree'
  }

These could be overridden by specifying a config file by adding an argument to the command line such as t -c user.config (user.config).
Example contents:

params {
  modules {
    'iqtree' {
      args = '-m GTR+G -czb'
    }
  }
}

This will specify that IQ-TREE should no longer provide SH-aLRT and the ultrafast bootstrap branch support values and will use the GTR+G model. The other options (build and publish_dir) will remain the same. Therefore to build an IQ-TREE, this step would either need to be turned on adding the --iqtree parameter to the Nextflow command line or by adding iqtree = true to the user config file within the params block.

The steps are described in more detail below along with their default parameters

Comprehensive description the steps

  1. Reference sequence indexing: reference sequence is indexed using bwa index

  2. Read trimming (Optional if params.trim is set): reads are trimmed using fastp. The default process configuration is found in the module.config and can be overridden as described above:

     'fastp' {
     args              = '--cut_front --cut_tail --trim_poly_x --cut_mean_quality 30 --qualified_quality_phred 30 --unqualified_percent_limit 10 --length_required 50'
     adapter_fasta     = "${baseDir}/assets/adapters.fas"
     publish_files     = ['json':'', 'html':'', 'log': 'log']
 }

    Please note that the default adapters are found in the adapters.fas file. These can be supplemented by specifying the path to a different file and updating the adapter_fasta value in a config file specified using -c as described above.

  3. Subsample reads (Optionally if params.subsampling_off is not set): reads are subsampled based on estimated genome size using mash sketch and the required depth of coverage as set by the --subsampling_depth_cutoff parameter using rasusa. The default process configurations are found in the module.config and can be overridden as described above.

       'mash_sketch' {
       args = '-k 32 -m 3'
   }
   'rasusa' {
       args = '--seed 23032021'
   }

  4. Map reads: reads are mapped to the indexed reference genome using bwa mem to produce a bam file. The default process configuration is found in the module.config and can be overridden as described above:

      'bwa_mem' {
      args = ''
      args2 = '-F 4' // samtools view options discarding unmapped reads
      publish_files = false
  }

  5. Sort reads: bam files are sorted using samtools

  6. Call variants: variants are called using bcftools mpileup. A minimum base quality of 20 is used for pre-filtering and the following fields are included in the resulting VCF file FORMAT/AD,FORMAT/ADF,FORMAT/ADR,FORMAT/DP,FORMAT/SP,INFO/AD,INFO/ADF,INFO/ADR. A haploid and multiallelic model is assumed. These defaults are found in the module.config and can be overridden as described above:

      'bcftools_mpileup' {
      args          = '--min-BQ 20 --annotate FORMAT/AD,FORMAT/ADF,FORMAT/ADR,FORMAT/DP,FORMAT/SP,INFO/AD,INFO/ADF,INFO/ADR'
      args2         = '--ploidy 1 --multiallelic-caller'
      args3         = ''
      publish_files = false
      publish_dir   = 'variants'
  }

  7. Filter variants: variants in the VCF file are filtered using bcftools filter. The default process configuration is found in the module.config and can be overridden as described above:

        'bcftools_filter' {
        args = '--soft-filter LowQual --exclude "%QUAL<25 || FORMAT/DP<10 || MAX(FORMAT/ADF)<2 || MAX(FORMAT/ADR)<2 || MAX(FORMAT/AD)/SUM(FORMAT/DP)<0.9 || MQ<30 || MQ0F>0.1" --output-type z'
        suffix = '.filtered'
        publish_dir   = 'variants'
    }

    After this process a filtered VCF file will be produced that has a row for each position in the reference genome. Each of these will either have a value in the FILTER column of either PASS or LowQual.

  8. Create pseudogenome: the filtered VCF is used to create a pseudogenome based on the reference genome using the vcf2pseudogenome.py script. The base in a sample at a position where the VCF file row that has PASS in FILTER will be either ref or alt and the appropriate base will be encoded at that position. The base in a sample at a position where the VCF file row that has LowQual in FILTER is uncertain will be encoded as a N character. Missing data will be encoded as a - character.

  9. Align pseudogenomes All samples pseudogenomes and the original reference sequence are concatenated together to produce a flush alignment where all the sequence for all samples at all positions in the original reference sequence will be one of {G,A,T,C,N,-}. Only those sequences that are high quality (based on the number of non GATC bases will be included. The threshold for this is set in the default process configuration found in the module.config and can be overridden as described above.

    'alignpseudogenomes' {
      non_GATC_threshold = 0.5
      publish_dir = 'pseudogenomes'
}

    This alignment can be used for other downstream processes such as phylogeny generation.

  10. Determine number of constant sites Optionally this alignment can be processed to produce a phylogeny as part of this pipeline. The number of constant sites in the alignment will be determined using snp-sites.

  11. Remove recombination (Optionally if params.remove_recombination is set): remove regions likely to have been acquired by horizontal transfer and recombination and therefore perturb the true phylogeny using gubbins. This should only be run on sets of samples that are closely related and not for example on a set of samples that have diversity spanning that of the entire species. The default process configuration is found in the module.config and can be overridden as described above.

    'alignpseudogenomes' {
    non_GATC_threshold = 0.5
    publish_dir = 'pseudogenomes'
}

  12. Build tree(s): Depending on the params set, run 0 - 4 tree building algorithms.

    • --rapidnj Build a neighbour-joining pylogeny using rapidnj
      The default process configuration is found in the module.config and can be overridden as described above.

              'rapidnj' {
        args = '-t d -b 1000 -n'
        publish_dir = 'rapidnj'
    }

    • --fasttree Build an approximately-maximum-likelihood phylogeny using FastTree
      The default process configuration is found in the module.config and can be overridden as described above.

      'fasttree' {
    args = '-gtr -gamma -fastest'
    publish_dir = 'fasttree'
}

    • --iqtree Build a maximum-likelihood phylogeny using IQ-TREE
      The default process configuration is found in the module.config and can be overridden as described above.

      'iqtree' {
    args = '-alrt 1000 -B 1000 -m MFP -czb'
    publish_dir = 'iqtree'
}

    • --raxmlng Build a maximum-likelihood phylogeny using RAxML Next Generation
      The default process configuration is found in the module.config and can be overridden as described above.

      'raxmlng' {
    args = '--model GTR+G --bs-trees 1000'
    publish_dir = 'raxmlng'
}

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’s 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/bactmap releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1.

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.

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, 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.

  • docker
    • A generic configuration profile to be used with Docker
    • Pulls software from Docker Hub: nfcore/bactmap
  • singularity
  • podman
    • A generic configuration profile to be used with Podman
    • Pulls software from Docker Hub: nfcore/bactmap
  • conda
    • Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity or Podman.
    • A generic configuration profile to be used with Conda
    • Pulls most software from Bioconda
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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 resource 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 an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Whilst these default requirements will hopefully work for most people with most data, you may find that you want to customise the compute resources that the pipeline requests. You can do this by creating a custom config file. For example, to give the workflow process star 32GB of memory, you could use the following config:

process {
  withName: star {
    memory = 32.GB
  }
}

See the main Nextflow documentation for more information.

If you are likely to be running nf-core pipelines 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 (see definition above). 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.

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'
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