Workflow

Last updated on 2024-06-10 | Edit this page

Overview

Questions

  • How do I connect channels and processes to create a workflow?
  • How do I invoke a process inside a workflow?

Objectives

  • Create a Nextflow workflow joining multiple processes.
  • Understand how to to connect processes via their inputs and outputs within a workflow.

Workflow

Our previous episodes have shown us how to parameterise workflows using params, move data around a workflow using channels and define individual tasks using processes. In this episode we will cover how connect multiple processes to create a workflow.

Workflow definition

We can connect processes to create our pipeline inside a workflow scope. The workflow scope starts with the keyword workflow, followed by an optional name and finally the workflow body delimited by curly brackets {}.

Implicit workflow

In contrast to processes, the workflow definition in Nextflow does not require a name. In Nextflow, if you don’t give a name to a workflow, it’s considered the main/implicit starting point of your workflow program.

A named workflow is a subworkflow that can be invoked from other workflows, subworkflows are not covered in this lesson, more information can be found in the official documentation here.

Invoking processes with a workflow

As seen previously, a process is invoked as a function in the workflow scope, passing the expected input channels as arguments as it if were.

 <process_name>(<input_ch1>,<input_ch2>,...)

To combined multiple processes invoke them in the order they would appear in a workflow. When invoking a process with multiple inputs, provide them in the same order in which they are declared in the input block of the process.

For example:

GROOVY 

//workflow_01.nf



 process FASTQC {
    input:
      tuple(val(sample_id), path(reads))
    output:
      path "fastqc_${sample_id}_logs"
    script:
      """
      mkdir fastqc_${sample_id}_logs
      fastqc -o fastqc_${sample_id}_logs -f fastq -q ${reads}
      """
}

process MULTIQC {
    publishDir "results/mqc"
    input:
      path transcriptome
    output:
      path "*"
    script:
      """
      multiqc .
      """
}

workflow {
    read_pairs_ch = channel.fromFilePairs('data/yeast/reads/*_{1,2}.fq.gz',checkIfExists: true)

    //index process takes 1 input channel as a argument
    //assign process output to Nextflow variable fastqc_obj
    fastqc_obj = FASTQC(read_pairs_ch)

    //quant channel takes 1 input channel as an argument
    //We use the collect operator to gather multiple channel items into a single item
    MULTIQC(fastqc_obj.collect()).view()
}

Process outputs

In the previous example we assigned the process output to a Nextflow variable fastqc_obj.

A process output can also be accessed directly using the out attribute for the respective process object.

For example:

GROOVY 

[..truncated..]

workflow {
  read_pairs_ch = channel.fromFilePairs('data/yeast/reads/*_{1,2}.fq.gz',checkIfExists: true)

  FASTQC(read_pairs_ch)

  // process output  accessed using the `out` attribute of the process object
  MULTIQC(FASTQC.out.collect()).view()
  MULTIQC.out.view()

}

When a process defines two or more output channels, each of them can be accessed using the list element operator e.g. out[0], out[1], or using named outputs.

Process named output

It can be useful to name the output of a process, especially if there are multiple outputs.

The process output definition allows the use of the emit: option to define a named identifier that can be used to reference the channel in the external scope.

For example in the script below we name the output from the FASTQC process as fastqc_results using the emit: option. We can then reference the output as FASTQC.out.fastqc_results in the workflow scope.

GROOVY 

//workflow_02.nf


 process FASTQC {
    input:
      tuple val(sample_id), path(reads)
    output:
      path "fastqc_${sample_id}_logs", emit: fastqc_results
    script:
      """
      mkdir fastqc_${sample_id}_logs
      fastqc -o fastqc_${sample_id}_logs ${reads}
      """
}

process MULTIQC {
    publishDir "results/mqc"
    input:
      path fastqc_results
    output:
      path "*"
    script:
      """
      multiqc .
      """
}

workflow {
    read_pairs_ch = channel.fromFilePairs('data/yeast/reads/ref*_{1,2}.fq.gz',checkIfExists: true)
    
    //FASTQC process takes 1 input channel as a argument
    FASTQC(read_pairs_ch)

    //MULTIQC channel takes 1 input channels as arguments
    MULTIQC(FASTQC.out.fastqc_results.collect()).view()
}

Accessing script parameters

A workflow component can access any variable and parameter defined in the outer scope:

For example:

GROOVY 

//workflow_03.nf
[..truncated..]

params.reads = 'data/yeast/reads/*_{1,2}.fq.gz'

workflow {

  reads_ch_ = channel.fromFilePairs(params.reads)
  FASTQC(reads_ch_)
  MULTIQC(FASTQC.out.fastqc_results.collect()).view()
}

In this example params.reads, defined outside the workflow scope, can be accessed inside the workflow scope.

Workflow

Connect the output of the process FASTQC to PARSEZIP in the Nextflow script workflow_exercise.nf.

Note: You will need to pass the read_pairs_ch as an argument to FASTQC and you will need to use the collect operator to gather the items in the FASTQC channel output to a single List item.

GROOVY 

//workflow_exercise.nf

params.reads = 'data/yeast/reads/*_{1,2}.fq.gz'

process FASTQC {
 input:
 tuple val(sample_id), path(reads)

 output:
 path "fastqc_${sample_id}_logs/*.zip"

 script:
 """
 mkdir fastqc_${sample_id}_logs
 fastqc -o fastqc_${sample_id}_logs  ${reads}
 """
}

process PARSEZIP {
 publishDir "results/fqpass", mode:"copy"
 input:
 path fastqc_logs

 output:
 path 'pass_basic.txt'

 script:
 """
 for zip in *.zip; do zipgrep 'Basic Statistics' \$zip|grep 'summary.txt'; done > pass_basic.txt
 """
}
read_pairs_ch = channel.fromFilePairs(params.reads,checkIfExists: true)

workflow {
//connect process FASTQC and PARSEZIP
// remember to use the collect operator on the FASTQC output
}

GROOVY 

//workflow_exercise.nf



params.reads = 'data/yeast/reads/*_{1,2}.fq.gz'

process FASTQC {
  input:
  tuple val(sample_id), path(reads)

  output:
  path "fastqc_${sample_id}_logs/*.zip"

  script:
  """
  mkdir fastqc_${sample_id}_logs
  fastqc -o fastqc_${sample_id}_logs  ${reads}
  """
}

process PARSEZIP {
  publishDir "results/fqpass", mode:"copy"
  input:
  path fastqc_logs

  output:
  path 'pass_basic.txt'

  script:
  """
  for zip in *.zip; do zipgrep 'Basic Statistics' \$zip|grep 'summary.txt'; done > pass_basic.txt
  """
}

read_pairs_ch = channel.fromFilePairs(params.reads,checkIfExists: true)

workflow {
  PARSEZIP(FASTQC(read_pairs_ch).collect())
}

BASH 

$ nextflow run workflow_exercise.nf

BASH 

$ wc -l  results/fqpass/pass_basic.txt

OUTPUT 

18

The file results/fqpass/pass_basic.txt should have 18 lines. If you only have two lines it might mean that you did not use collect() operator on the FASTC output channel.

Key Points

  • A Nextflow workflow is defined by invoking processes inside the workflow scope.
  • A process is invoked like a function inside the workflow scope passing any required input parameters as arguments. e.g. FASTQC(reads_ch).
  • Process outputs can be accessed using the out attribute for the respective process object or assigning the output to a Nextflow variable.
  • Multiple outputs from a single process can be accessed using the list syntax [] and it’s index or by referencing the a named process output .