Building channels

In Nextflow, channels are require to move data between processes. Channels act as communication links that pass data between tasks in a workflow. Using channels effectively ensures smooth and efficient execution. Let’s explore some best practices and common scenarios for building channels, and how Groovy and Nextflow operators can be used to manipulate data within them.

Relevant documentation and resources

• Nextflow documentation: channels
• Nextflow documentation: operators
• Nextflow training: channels
• Nextflow training: operators
• Nextflow patterns

How to approach channel creation

When creating a channel in Nextflow, consider the following steps to ensure an efficient and structured approach:

1. Understand the data source

Before creating a channel, identify where your data originates. This could be files, external databases, or outputs from previous processes. The data source defines the channel’s structure and how it will interact with other workflow components.

You’ll need to use channel factories to capture this information correctly. For example, if you’re processing paired fastq files, you need a channel that can handle paths to those files, e.g. fromPath or fromFilePairs.

2. Transform the data as required

To ensure your data is formatted correctly for different processes, Nextflow provides various operators for transforming data within channels.

Operators like splitCsv are useful for reading and parsing CSV files, map allows you to restructure rows into tuples, and groupTuple helps to group related data.

3. Set the Scope

Determine how the channel will be used within the workflow. Is it feeding a single process, or will it be used across multiple steps? A well-defined scope ensures the channel is built efficiently and passes only the necessary data, at the right stage, and potentially reusing the channel in different contexts.

For example, if you have a channel that feeds fastq files into FastQC process, that same channel could be reused for read alignment with BWA.

4. Proactively handle errors

When creating a channel, consider edge cases like missing or incomplete data. Use operators like ifEmpty to define fallback behavior when the channel contains no data. This avoids crashes or unexpected behavior during workflow execution and ensures a more resilient pipeline. You can also use directives like errorStrategy to define error conditions.

5. Run processes in parallel where possible

Channels enable parallel execution by distributing data across tasks. When building a channel, ensure that the data is partitioned in a way that supports concurrency.

Nextflow channels naturally support parallelism, but it’s essential to structure them appropriately so that the tasks they feed can run independently. For example, using groupTuple or map can create subsets of data that can be processed simultaneously.

Some tricky scenarios

Creating channels from samplesheets

In bioinformatics workflows, samplesheets (as CSV files) are a common way to store metadata about sequencing experiments, such as sample names, file paths, and experimental conditions. Nextflow allows you to easily create channels from these samplesheets, enabling efficient parallel processing of the associated data.

Use case: grouping fastq pairs according to their sample ID

Consider a samplesheet with the following structure:

```default
sample,tissue_type,fq1,fq2
sample1,liver,sample1_liver_1.fq,sample1_liver_2.fq
sample2,kidney,sample2_kidney_1.fq,sample2_kidney_2.fq
sample3,brain,sample3_brain_1.fq,sample3_brain_2.fq
sample4,liver,sample4_liver_1.fq,sample4_liver_2.fq
sample5,kidney,sample5_kidney_1.fq,sample5_kidney_2.fq

We need to input fq pairs according to their sample ID to bwa-mem2 for alignment to a reference assembly. We can do this with the channel:

samplesheet = Channel.fromPath('samples.csv')
              .splitCsv(header: true)
              .map { row -> tuple(row.sample, row.tissue_type, row.fq1, row.fq2) }

• splitCsv(header: true) parses the CSV with headers.
• map transforms each row into a tuple containing relevant fields

process align_reads {
    
  input:
  tuple val(sample), path(fq1), path(fq2)
  path reference_index

  output:
  path "${sample}.bam"

  script:
  """
  bwa-mem2 mem ${reference_index} ${fq1} ${fq2} \\
    -t ${task.cpus} \\
    | samtools view -Sb - \\
    > ${sample}.bam
  """
}

This allows you to process samples in parallel while maintaining consistent metadata and file associations for each sample across all workflow steps.

Dynamic input selection

In Nextflow allows workflows to intelligently handle variable inputs, making them adaptable to different scenarios. This is especially useful when your input data or conditions change across runs, for example checking if a resource like a database or index already exists. If it does, the workflow uses it directly. If not, it dynamically runs a process to generate or download the resource.

Use case: dynamically selecting to run a process

Consider a scenario where we need to check if the Kraken2 database exists and either use the existing file or download/build it if it’s missing.

if (params.kraken_db) {
    kraken2_db = path(params.kraken_db)

} else {
    process download_k2db {
      output:
      path "kraken2_db", emit: k2db

      script:
      """
      kraken2-build --download-library bacteria --db kraken2_db
      """
   }

    kraken2_db = download_kraken2_db.out.k2db
}

• if (params.kraken_db) checks if the kraken_db parameter (--kraken_db) is provided in the run command. If it is, it defines the kraken2_db channel.
• else {} block: if --kraken_db is not provided, it runs the download_k2db process and assigns the output to the kraken2_db channel.

This structure makes the workflow flexible by either using an existing resource or generating it if necessary.

Group data by a metadata field

In bioinformatics workflows, it’s common to process data based on specific attributes, such as grouping samples by tissue type, batch, or condition. This can be useful when performing differential expression analysis or organising data for downstream steps.

Use case: grouping data for batch analysis

Consider a scenario where you have RNAseq data from multiple tissues (e.g. liver, kidney, brain) listed in a CSV file. To perform differential analysis per tissue type, you can group samples by the tissue_type field and analyse each group separately.

Key Steps:

• Extract sample information from the metadata (e.g., tissue type) using a CSV.
• Use the groupBy operator to organise samples by the tissue type field.
• Run processes on each group independently, enabling parallel analysis.

Consider the theoretical samplesheet containing the fields:

• sample: The unique identifier for each sample.
• tissue_type: The tissue from which the sample was taken.
• fq1, fq2: Paths to paired-end fastq files for RNAseq data.

sample,tissue_type,fq1,fq2
sample1,liver,sample1_liver_1.fq,sample1_liver_2.fq
sample2,kidney,sample2_kidney_1.fq,sample2_kidney_2.fq
sample3,brain,sample3_brain_1.fq,sample3_brain_2.fq
sample4,liver,sample4_liver_1.fq,sample4_liver_2.fq
sample5,kidney,sample5_kidney_1.fq,sample5_kidney_2.fq

The channel for this samplesheet structure:

samplesheet = Channel.fromPath('samples.csv')
              .splitCsv(header: true)
              .map { row -> tuple(row.tissue_type, 
                            tuple(row.sample, row.fq1, row.fq2)) }
              .groupTuple()

• splitCsv(header: true) reads the CSV file with headers.
• map extracts the sample, tissue type, fq1, and fq2 file paths.
• groupTuple(by: 1) groups the samples by tissue type (index 1 refers to the second column, which is tissue_type).

This will create groups based on tissue types for downstream analysis, enabling you to process samples by their respective tissues.

process salmon_quant {

  input:
  tuple val(tissue_type), tuple val(sample), path(fq1), path(fq2)
  path(transcriptome_index)

  output:
  path "results/${tissue_type}/${sample}_quant.sf"

  script:
  """
  salmon quant \\
    -i transcriptome_index \\
    -l A \\
    -1 ${fq1} -2 ${fq2} \\
    -o ${tissue_type}/${sample}
  """
}

This ensures correct association between tissue, sample, and paired-end files for RNAseq quantification.

Skipping empty or optional data

The ifEmpty operator allows us to define a fallback channel if the original channel is empty. This is helpful when working with optional outputs (as specified in the output: block, using optional: true).

Use case: conditionally outputting results from a process

In workflows, you may need to handle cases where processes generate optional outputs. In this case, Plassembler outputs plasmid detection files, but not all samples will contain plasmids. To manage this, you can combine optional outputs with ifEmpty to control what gets passed downstream.

Key steps:

• Run plassembler to detect plasmids.
• Use ifEmpty to handle cases where plasmids .fasta is empty or missing.
• Use optional: true for downstream processes (e.g. Bakta) that depend on plasmid detection

process plassembler {
  tag "DETECTING PLASMIDS AND OTHER MOBILE ELEMENTS: ${barcode}"

  input:
  tuple val(barcode), path(trimmed_fq), path(flye_assembly)
  path plassembler_db 

  output:
  tuple val(barcode), path("plassembler/plasmids.fasta"), optional: true
  tuple val(barcode), path("plassembler/summary.tsv")

  script:
  """
  plassembler long \\
    -d ${plassembler_db} \\
    -l ${trimmed_fq} \\
    --flye_assembly ${flye_assembly} \\
    -o plassembler \\
    -t ${task.cpus}
  """
}

Now, we can define the bakta_plasmids input channel as:

bakta_plasmid = plassembler.out.plassembler_fasta.ifEmpty([])

• bakta_plasmid channel will receive the output from the Plassembler process.
• ifEmpty([]) ensures that if no plasmid .fasta file is generated, an empty list [] is passed instead.

This channel can now be used as input to downstream processes, like Bakta, with conditional execution based on whether plasmids were detected.