We just added several parameters and configurations to our MRSA workflow, but we didn’t do anything about the reference genomes: those are still hard-coded. How come? Well, the current MRSA workflow is, in fact, not very well-optimised for Nextflow at all, being a refactor from the Snakemake tutorial of this course.

All of the processes are basically unchanged, excluding some minor alterations. For example, the run_fastqc rule in Snakemake used the -o flag to specify that the results should be in the current directory, followed by moving the output files to their respective output directory. The first part is not needed in Nextflow (as everything is run in its own subdirectory), and the second part is done by the publishDir directive. These are just minor alterations, though, but we can do much more if we fully utilise Nextflow’s features!

Remote files

One of these features is the ability to automatically download remote files, without needing to explicitly do so! The path input type can handle either file paths (like we’ve done so far) or a URI-supported protocol (such as http://, s3://, ftp://, etc.). This would be highly useful for e.g. the GET_GENOME_FASTA process - in fact, we don’t even need that process at all! All we need to do is to change the input to the INDEX_GENOME and ALIGN_TO_GENOME processes.

Create a new input channel using the fromPath() channel factory and the absolute path to the genome FASTA.
Make the INDEX_GENOME process use that input channel instead of the previously used output of the GET_GENOME_FASTA process.
Remove the GET_GENOME_FASTA process, as it is not needed anymore.

Re-run the workflow to see if it worked. Check the code below for an example if you’re stuck:

Click to show

# Channel creation

ch_genome_fasta = Channel.fromPath( ftp://ftp.ensemblgenomes.org/pub/bacteria/release-37/fasta/bacteria_18_collection/staphylococcus_aureus_subsp_aureus_nctc_8325/dna/Staphylococcus_aureus_subsp_aureus_nctc_8325.ASM1342v1.dna_rm.toplevel.fa.gz )

# Workflow definition
INDEX_GENOME (
    ch_genome_fasta
)

If we want to get detailed we can also change the hard-coded “NCT8325” naming in e.g. the INDEX_GENOME process and put that in another parameter, or grab the baseName() from the channel and make a [prefix, file] tuple using the map{} operator like we did previously; check below if you’re curious of how this could be done.

Click to show

// Channel definition
ch_genome_fasta = Channel
    .fromPath( ftp://ftp.ensemblgenomes.org/pub/bacteria/release-37/fasta/bacteria_18_collection/staphylococcus_aureus_subsp_aureus_nctc_8325/dna/Staphylococcus_aureus_subsp_aureus_nctc_8325.ASM1342v1.dna_rm.toplevel.fa.gz )
    .map{ file -> tuple(file.getBaseName(), file) }

// INDEX_GENOME process definition
process INDEX_GENOME {

    publishDir "results/intermediate/",
        mode: "copy"

    input:
    tuple val(fasta_name), path(fasta)

    output:
    path("*.b2t"), emit: index

    script:
    """
    # Bowtie2 cannot use .gz, so unzip to a temporary file first
    gunzip -c ${fasta} > tempfile
    bowtie2-build tempfile ${fasta_name}
    """

Subworkflows

The DSL2 allows highly modular workflow design, where a workflow may contain multiple subworkflows. A subworkflow is just like a normal workflow, but it can be called inside other workflows, similar to a process. There is thus no special difference between a subworkflow and a workflow; the only difference is how you use them in practice. Let’s take a look at a toy example:

workflow {
    ch_input = Channel.fromPath( params.input )
    SUBWORKFLOW( ch_input )
}

workflow SUBWORKFLOW {

    take:
    input_file

    main:
    ALIGN_READS( input_file )

    emit:
    bam = ALIGN_READS.out.bam
}

Here we have an unnamed, main workflow like before, plus a named subworkflow. A workflow can have inputs specified by the take directive, which is the equivalent of process input for workflows. The main part is the workflow body, which contains how to run which processes in which order. The last part, emit, also works the same as for processes, in that we name the different outputs of the workflow so that we may use them in other workflows or processes. Nextflow will run the unnamed workflow by default, unless the -entry flag is specified, like so:

nextflow run main.nf -entry SUBWORKFLOW

This will run the workflow named SUBWORKFLOW, but nothing else. You can also store subworkflows in separate files, so that everything doesn’t have to be crammed into a single main.nf file. A subworkflow named SUBWORKFLOW contained in the file subworkflow.nf can be loaded into a main.nf file like so:

include { SUBWORKFLOW } from "./subworkflow.nf"

If you have a complex workflow with several subworkflows you might thus store them in a separate directory, e.g. subworkflows. This allows you to have fine-grained control over the general architecture of your Nextflow workflows, organising them in a manner that is easy to code and maintain. A process can also be treated in the same manner, and defined separately in another file.

Now it’s your turn! Separate the RUN_FASTQC and RUN_MULTIQC processes out of the main workflow and into a subworkflow. Check below if you’re having trouble.

Click to show

// In the main workflow:
RUN_QC (
    GET_SRA_BY_ACCESSION.out
)

// A new subworkflow
workflow RUN_QC {

    take:
    fastq

    main:
    RUN_FASTQC (
        fastq
    )
    RUN_MULTIQC (
        RUN_FASTQC.out.zip.collect()
    )
}

If you want to challenge yourself, try to do the same with the INDEX_GENOME, ALIGN_TO_GENOME and SORT_BAM processes! Be careful of where you get your inputs and outputs.

Quick recap
In this section we learnt:

How to automatically download remote files

How to create and work with subworkflows