When to use Nextflow?

Nextflow is a workflow manager rapidly gaining popularity in the Bioinformatics community, and is highly recommended for reproducible research. But do you really need to learn it? When should it be used and when not? Let’s take a look at scenarios where Nextflow shines and where other tools might be a better fit.

Where Nextflow shines:

Scalability:

When a pipeline is written to process a few samples, it means it’s generally written in a robust enough way that it can process several more without changes to the workflow. Nextflow handles distribution of these tasks, enabling seamless scaling from a handful of samples to hundreds or even thousands without major code changes. This especially important for large-scale genomics projects or growing datasets. Imagine you’ve developed a variant calling pipeline for 10 samples; with Nextflow, running it on 1000 samples becomes a matter of potentially adjusting configuration rather than rewriting the core logic. Importantly, it allows rapid development on a few scaled-down samples, and easy application to a much larger data set.

Portability:

Nextflow workflows can run on multiple compute environments - from your local machine to High-Performance Computing (HPC) clusters or Cloud platforms like AWS, Google Cloud, and Azure - thanks to its abstraction layer. Nextflow isolates the workflow definition from the underlying infrastructure. This means you can develop and test your pipeline on a smaller scale and then deploy it to a more powerful environment without worrying about environment-specific commands or resource management details. This portability fosters collaboration and ensures your research is not tied to a specific computing infrastructure.

Parallelization:

Nextflow efficiently distributes tasks across available compute resources. It automatically schedules and executes processes, maximizing resource utilisation and reducing runtime. Instead of manually managing parallel execution with complex scripts, Nextflow simplifies this process, letting you focus on the scientific logic of your pipeline. This parallelization is a major advantage for computationally intensive bioinformatics workflows.

Heterogeneous tool environments:

With Nextflow, you can define containerized environments (e.g., using Docker or Singularity) for each process within your workflow, elegantly resolving software dependency conflicts. For instance, one step might require a specific version of Python libraries while another needs a particular version of a bioinformatics tool. By containerizing each process, Nextflow ensures that each tool runs in its isolated and correctly configured environment, leading to more reliable and reproducible results.

Feature rich Domain Specific Language:

Built on top of the Groovy programming language, Nextflow provides a powerful and expressive Domain Specific Language (DSL). This DSL allows you to model complex data flows intuitively, define dependencies between processes, and handle various data structures with ease. Furthermore, Nextflow’s extensibility through external Groovy libraries allows you to incorporate advanced functionalities and tailor the language to your specific needs. This rich DSL makes defining sophisticated bioinformatics pipelines more manageable and less error-prone compared to traditional scripting approaches.

Scripting Language flexibility:

Nextflow doesn’t restrict you to a single scripting language. It seamlessly supports Shell scripts, R, Python, Perl, or any other language that your compute environment can execute. This flexibility allows you to leverage your existing expertise and integrate tools written in different languages into a single cohesive workflow. You can use the best tool for each specific task without being constrained by the workflow manager’s limitations.

Rapid prototyping:

Nextflow supports reentrancy, allowing workflows to resume from where they failed or were interrupted. Combined with its simple task description language, this feature speeds up pipeline prototyping and debugging.

Community support:

Nextflow has a large user base, and in particular has the nf-core community based around building scalable pipelines written in Nextflow for public use. There are several forums where one can get help with implementing Nextflow and debugging issues. This allows developers to spend less time struggling with issues.

• Seqera Community Forum
• Nextflow Slack
• Nextflow Discussions
• nf-core Slack

It’s unclear whether other tools have similar communities, and the primary method of support appears to be through their Github issues and discussions, or Stack Overflow.

Community developed existing pipelines also save time by eliminating the need to reimplement solutions.

Where Nextflow is not optimal:

While Nextflow is powerful, there are scenarios where it may not be the best choice.

Interactive exploratory analyses:

For initial data exploration and interactive analyses, tools like Jupyter or Marimo notebooks or RStudio might offer a more immediate and flexible environment. These tools excel at providing a rapid feedback loop for trying out different approaches, visualizing data, and generating preliminary insights. Nextflow, with its focus on structured and automated workflows, might introduce unnecessary overhead for purely exploratory tasks.

Environment overhead:

For smaller, self-contained workflows primarily using a single language like R (where packages like targets together with renv provide excellent workflow management) or Python (where Snakemake can be a lighter alternative), the full power and complexity of Nextflow might be overkill. If your analysis involves a limited number of steps and dependencies within a single language ecosystem and doesn’t require deployment across diverse environments, a more lightweight workflow manager might be more efficient in terms of setup and execution.

Client - Server interaction:

Nextflow’s asynchronous execution model makes it less suitable for workflows that require tight, real-time client-server interactions between processes within the workflow itself. While Nextflow can certainly interact with external services (like databases or web APIs), if two processes within your Nextflow workflow need to continuously communicate and depend on each other’s immediate responses, you might encounter challenges due to the inherent asynchronous nature of task execution. In such scenarios, alternative approaches that offer more direct inter-process communication might be more appropriate. However, if one process acts as a client to an external, independently running server, Nextflow can handle this effectively. The key limitation lies in tightly coupled, synchronous client-server architectures within the Nextflow workflow.

Learning curve:

Despite its advantages, Nextflow has a learning curve that may pose challenges for new users. An arguable difficulty with Nextflow is the DSL is based on Groovy, an uncommon language in Bioinformatics. The primary issue though generally seems to be less about the language, but more the shift in thinking from linear scripts to a more declarative, dataflow-oriented approach. For example, for loops and if statements, which are basic control structures in most scripting languages are handled quite differently in Nextflow.

How to implement sequential processing

In linear scripts, data is usually processed by functions. The output can then be passed directly to another function or by assigning to a variable.

example.R

# Nested function call: We'll apply add_one to 5, and then multiply the result by 2
result <- multiply_by_two(add_one(5))
# Or sequentially
add_one_result <- add_one(5)
result <- multiply_by_two(add_one_result)

print(result) # Output: 12

The equivalent in Nextflow would be to use a process. Although process calls can be nested, they’re typically written on separate lines, and the special process.out variable is used to access the process output.

example.nf

workflow {
    ADD_ONE( Channel.of(5) )
    MULTIPLY_BY_TWO( ADD_ONE.out )
    MULTIPLY_BY_TWO.out.view()
}

For simple workflows, the alternative pipe syntax can also be used:

example.nf

workflow {
    Channel.of(5)
    | ADD_ONE
    | MULTIPLY_BY_TWO
    | view
}

Additionally, Channel factories must be used to take user input and pass it into a process.

How to implement the for control structure in Nextflow

Iteration (for/while loops) is built into Nextflow. Tasks automatically iterate over any input provided in a channel. In this example, the workflow iterates over the numbers 1 to 5. If a channel is empty, the task does not execute.

example.nf

workflow {
    TASK_A( Channel.of(1..5) )
        .view()
}

process TASK_A {
    input:
    val num

    script:
    """
    echo ${num}
    """

    output:
    stdout
}

How to implement the if control structure in Nextflow

Although one can use if statements in Nextflow code, often you want to decide the action based on the output of a process. In this case, the Channel operators like filter and branch are the solution. Here is an example of how to optionally execute a process based on the process output.

graph LR
    A -->|4,5| B --> C
    A -->|1,2,3| C

example.nf

workflow {
    TASK_A( Channel.of(1..5) )
    TASK_B( TASK_A.out.filter { num -> num.toInteger() > 3 } )
    TASK_C( TASK_A.out.filter { num -> num.toInteger() <= 3 }.mix(TASK_B.out) )
        .view()
}

process TASK_A {
    input:
    val num

    script:
    """
    echo ${num}
    """

    output:
    stdout
}

process TASK_B {
    input:
    val num

    script:
    """
    echo ${num}
    """

    output:
    stdout
}

process TASK_C {
    input:
    val num

    script:
    """
    echo ${num}
    """

    output:
    stdout
}

This is effectively like using an if within while loop. If the channel only has a single entry, then you’re effectively using just an if statement.

There is a lot of training material however to help you with learning Nextflow.

• NBIS/Elixir Tools for Reproducible Research
• Nextflow fundamental and advanced training
• nf-core bytesize

Overview

Nextflow is a versatile tool for managing bioinformatics workflows, but not everything needs it’s power.

• Quarto, Jupyter, and Marimo are better suited to interactive exploratory analyses.
• Single language workflows may benefit from packages within the language, like targets for R.
• It can be challenging to go from linear scripting to declarative dataflow-oriented programming.