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CWL: Scatter-gather Method

In bioinformatics and computational biology, the vast and growing amount of data necessitates methods and tools that can process and analyze data in parallel. This demand gave birth to the scatter-gather approach, an essential pattern in creating pipelines that offers efficient data handling and parallel processing capabilities. In this tutorial, we will demonstrate how to create a CWL pipeline utilizing the scatter-gather approach. To this purpose, we will use two widely known tools: fastp and multiqc. Given the functionalities of both fastp and multiqc, their combination in a scatter-gather pipeline is incredibly useful. Individual datasets can be scattered across resources for parallel preprocessing with fastp. Subsequently, the outputs from each of these parallel tasks can be gathered and fed into multiqc, generating a consolidated quality report. This method not only accelerates the preprocessing of large datasets but also offers an aggregated perspective on data quality, ensuring that subsequent analyses are built upon a robust foundation.

Creating the tools

First, we create the two tools: fastp and multiqc. For this, we need the corresponding Docker images and CWL tool definitions. Please, look up this part of our help sites to learn more how to import a tool into ICA. In a nutshell, once the CWL tool definition is pasted into the editor, the other tabs for editing the tool will be populated. To complete the tool, the user needs to select the corresponding Docker image and to provide a tool version (could be any string).

For this demo, we will use the publicly available Docker images: quay.io/biocontainers/fastp:0.20.0--hdbcaa40_0 for fastp and docker.io/ewels/multiqc:v1.15 for multiqc. In this tutorial one can find how to import publicly available Docker images into ICA.

Furthermore, we will use the following CWL tool definitions:

#!/usr/bin/env cwl-runner

cwlVersion: v1.0
class: CommandLineTool
requirements:
- class: InlineJavascriptRequirement
label: fastp
doc: Modified from https://github.com/nigyta/bact_genome/blob/master/cwl/tool/fastp/fastp.cwl
inputs:
  fastq1:
    type: File
    inputBinding:
      prefix: -i
  fastq2:
    type:
    - File
    - 'null'
    inputBinding:
      prefix: -I
  threads:
    type:
    - int
    - 'null'
    default: 1
    inputBinding:
      prefix: --thread
  qualified_phred_quality:
    type:
    - int
    - 'null'
    default: 20
    inputBinding:
      prefix: --qualified_quality_phred
  unqualified_phred_quality:
    type:
    - int
    - 'null'
    default: 20
    inputBinding:
      prefix: --unqualified_percent_limit
  min_length_required:
    type:
    - int
    - 'null'
    default: 50
    inputBinding:
      prefix: --length_required
  force_polyg_tail_trimming:
    type:
    - boolean
    - 'null'
    inputBinding:
      prefix: --trim_poly_g
  disable_trim_poly_g:
    type:
    - boolean
    - 'null'
    default: true
    inputBinding:
      prefix: --disable_trim_poly_g
  base_correction:
    type:
    - boolean
    - 'null'
    default: true
    inputBinding:
      prefix: --correction
outputs:
  out_fastq1:
    type: File
    outputBinding:
      glob:
      - $(inputs.fastq1.nameroot).fastp.fastq
  out_fastq2:
    type:
    - File
    - 'null'
    outputBinding:
      glob:
      - $(inputs.fastq2.nameroot).fastp.fastq
  html_report:
    type: File
    outputBinding:
      glob:
      - fastp.html
  json_report:
    type: File
    outputBinding:
      glob:
      - fastp.json
arguments:
- prefix: -o
  valueFrom: $(inputs.fastq1.nameroot).fastp.fastq
- |
  ${
    if (inputs.fastq2){
      return '-O';
    } else {
      return '';
    }
  }
- |
  ${
    if (inputs.fastq2){
      return inputs.fastq2.nameroot + ".fastp.fastq";
    } else {
      return '';
    }
  }
baseCommand:
- fastp

and

Pipeline

Once the tools are created, we will create the pipeline itself using these two tools at Projects > your_project > Flow > Pipelines > CWL > Graphical:

  • On the Definition tab, go to the tool repository and drag and drop the two tools which you just created on the pipeline editor.

  • Connect the JSON output of fastp to multiqc input by hovering over the middle of the round, blue connector of the output until the icon changes to a hand and then drag the connection to the first input of multiqc. You can use the magnification symbols to make it easier to connect these tools.

  • Above the diagram, drag and drop two input FASTQ files and an output HTML file on to the pipeline editor and connect the blue markers to match the diagram below.

fastp_multiqc

Relevant aspects of the pipeline:

  • Both inputs are multivalue (as can be seen on the screenshot)

  • Ensure that the step fastp has scattering configured: it scatters on both inputs using the scatter method 'dotproduct'. This means that as many instances of this step will be executed as there are pairs of FASTQ files. To indicate that this step is executed multiple times, the icons of both inputs have doubled borders.

Important remark

Both input arrays (Read1 and Read2) must be matched. Currently an automatic sorting of input arrays is not supported. You have to take care of matching the input arrays which can be done in either one of two ways (besides the manual specification in the GUI):

  • Invoke this pipeline in CLI using Bash functionality to sort the arrays

  • Add a tool to the pipeline which will intake array of all FASTQ files, spread them on R1 and R2 suffixes, and sort them.

We will describe the second way in more detail. The tool will be based on public python Docker docker.io/python:3.10 and have the following definition. In this tool we are providing the Python script spread_script.py via Dirent feature.

Now this tool can added to the pipeline before fastp step.

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