Content from Getting Started with Nextflow

---

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

Estimated time: 40 minutes

Overview

Questions

• What is a workflow and what are workflow management systems?
• Why should I use a workflow management system?
• What is Nextflow?
• What are the main features of Nextflow?
• What are the main components of a Nextflow script?
• How do I run a Nextflow script?

Objectives

• Understand what a workflow management system is.
• Understand the benefits of using a workflow management system.
• Explain the benefits of using Nextflow as part of your bioinformatics workflow.
• Explain the components of a Nextflow script.
• Run a Nextflow script.

Workflows

---

Analysing data involves a sequence of tasks, including gathering, cleaning, and processing data. This sequence of tasks is called a workflow or a pipeline. These workflows typically require executing multiple software packages, sometimes running on different computing environments, such as a desktop or a compute cluster. Traditionally these workflows have been joined together in scripts using general purpose programming languages such as Bash or Python.

An example of a simple bioinformatics RNA-Seq pipeline.

However, as workflows become larger and more complex, the management of the programming logic and software becomes difficult.

Workflow management systems

---

Workflow Management Systems (WfMS) such as Snakemake, Galaxy, and Nextflow have been developed specifically to manage computational data-analysis workflows in fields such as bioinformatics, imaging, physics, and chemistry. These systems contain multiple features that simplify the development, monitoring, execution and sharing of pipelines, such as:

• Run time management
• Software management
• Portability & Interoperability
• Reproducibility
• Re-entrancy

An example of differences between running a specific analysis workflow using a traditional pipeline or a WfMS-based pipeline. Source: Wratten, L., Wilm, A. & Göke, J. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods 18, 1161–1168 (2021). https://doi.org/10.1038/s41592-021-01254-9

Nextflow core features

---

Overview of Nextflow core features.

• Fast prototyping: A simple syntax for writing pipelines that enables you to reuse existing scripts and tools for fast prototyping.

• Reproducibility: Nextflow supports several container technologies, such as Docker and Singularity, as well as the package manager Conda. This, along with the integration of the GitHub code sharing platform, allows you to write self-contained pipelines, manage versions and to reproduce any previous result when re-run, including on different computing platforms.

• Portability & interoperability: Nextflow’s syntax separates the functional logic (the steps of the workflow) from the execution settings (how the workflow is executed). This allows the pipeline to be run on multiple platforms, e.g. local compute vs. a university compute cluster or a cloud service like AWS, without changing the steps of the workflow.

• Simple parallelism: Nextflow is based on the dataflow programming model which greatly simplifies the splitting of tasks that can be run at the same time (parallelisation).

• Continuous checkpoints & re-entrancy: All the intermediate results produced during the pipeline execution are automatically tracked. This allows you to resume its execution from the last successfully executed step, no matter what the reason was for it stopping.

Processes, channels, and workflows

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Nextflow workflows have three main parts: processes, channels, and workflows.

• Processes describe a task to be run. A process script can be written in any scripting language that can be executed by the Linux platform (Bash, Perl, Ruby, Python, R, etc.). Processes spawn a task for each complete input set. Each task is executed independently and cannot interact with other tasks. The only way data can be passed between process tasks is via asynchronous queues, called channels.

• Processes define inputs and outputs for a task. Channels are then used to manipulate the flow of data from one process to the next.

• The interaction between processes, and ultimately the pipeline execution flow itself, is then explicitly defined in a workflow section.

In the following example we have a channel containing three elements, e.g., three data files. We have a process that takes the channel as input. Since the channel has three elements, three independent instances (tasks) of that process are run in parallel. Each task generates an output, which is passed to another channel and used as input for the next process.

Nextflow process flow diagram.

Workflow execution

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While a process defines what command or script has to be executed, the executor determines how that script is actually run in the target system.

If not otherwise specified, processes are executed on the local computer. The local executor is very useful for pipeline development, testing, and small-scale workflows, but for large-scale computational pipelines, a High Performance Cluster (HPC) or Cloud platform is often required.

Nextflow Executors

Nextflow provides a separation between the pipeline’s functional logic and the underlying execution platform. This makes it possible to write a pipeline once, and then run it on your computer, compute cluster, or the cloud, without modifying the workflow, by defining the target execution platform in a configuration file.

Nextflow provides out-of-the-box support for major batch schedulers and cloud platforms such as Sun Grid Engine, SLURM job scheduler, AWS Batch service and Kubernetes; a full list can be found here.

Your first script

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We are now going to look at a sample Nextflow script that counts the number of lines in a file. Create the file word_count.nf in the current directory using your favourite text editor and copy-paste the following code:

Instructor Note

The nextflow scripts for each lesson episode are available in the scripts directory created during the course setup. You should copy the script into the current directory

For example, to copy the script for this lesson episode, run the following command:

BASH

$ cp scripts/introduction/word_count.nf .

GROOVY

#!/usr/bin/env nextflow



/*
========================================================================================
    Workflow parameters are written as params.<parameter>
    and can be initialised using the `=` operator.
========================================================================================
*/

params.input = "data/yeast/reads/ref1_1.fq.gz"

/*
========================================================================================
    Input data is received through channels
========================================================================================
*/

input_ch = Channel.fromPath(params.input)

/*
========================================================================================
   Main Workflow
========================================================================================
*/

workflow {
    //  The script to execute is called by it's process name, and input is provided between brackets.

    NUM_LINES(input_ch)

    /*  Process output is accessed using the `out` channel.
        The channel operator view() is used to print process output to the terminal. */

    NUM_LINES.out.view()

}

/*
========================================================================================
    A Nextflow process block. Process names are written, by convention, in uppercase.
    This convention is used to enhance workflow readability.
========================================================================================
*/
process NUM_LINES {

    input:
    path read

    output:
    stdout

    script:
    """
    # Print file name
    printf '${read}\\t'

    # Unzip file and count number of lines
    gunzip -c ${read} | wc -l
    """
}

This is a Nextflow script, which contains the following:

1. An optional interpreter directive (“Shebang”) line, specifying the location of the Nextflow interpreter.
2. A multi-line Nextflow comment, written using C style block comments, there are more comments later in the file.
3. A pipeline parameter params.input which is given a default value, of the relative path to the location of a compressed fastq file, as a string.
4. A Nextflow channel input_ch used to read in data to the workflow.
5. An unnamed workflow execution block, which is the default workflow to run.
6. A call to the process NUM_LINES.
7. An operation on the process output, using the channel operator .view().
8. A Nextflow process block named NUM_LINES, which defines what the process does.
9. An input definition block that assigns the input to the variable read, and declares that it should be interpreted as a file path.
10. An output definition block that uses the Linux/Unix standard output stream stdout from the script block.
11. A script block that contains the bash commands printf '${read}' and gunzip -c ${read} | wc -l.

Running Nextflow scripts

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To run a Nextflow script use the command nextflow run <script_name>.

Run a Nextflow script

Run the script by entering the following command in your terminal:

BASH

$ nextflow run word_count.nf

Show me the solution

You should see output similar to the text shown below:

OUTPUT

N E X T F L O W  ~  version 21.04.3
Launching `word_count.nf` [fervent_babbage] - revision: c54a707593
executor >  local (1)
[21/b259be] process > NUM_LINES (1) [100%] 1 of 1 ✔

 ref1_1.fq.gz 58708

1. The first line shows the Nextflow version number.
2. The second line shows the run name fervent_babbage (adjective and scientist name) and revision id c54a707593.
3. The third line tells you the process has been executed locally (executor > local).
4. The next line shows the process id 21/b259be, process name, number of cpus, percentage task completion, and how many instances of the process have been run.
5. The final line is the output of the .view() operator.

Quick recap

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• A workflow is a sequence of tasks that process a set of data, and a workflow management system (WfMS) is a computational platform that provides an infrastructure for the set-up, execution and monitoring of workflows.
• Nextflow scripts comprise of channels for controlling inputs and outputs, and processes for defining workflow tasks.
• You run a Nextflow script using the nextflow run command.

Key Points

• A workflow is a sequence of tasks that process a set of data.
• A workflow management system (WfMS) is a computational platform that provides an infrastructure for the set-up, execution and monitoring of workflows.
• Nextflow is a workflow management system that comprises both a runtime environment and a domain specific language (DSL).
• Nextflow scripts comprise of channels for controlling inputs and outputs, and processes for defining workflow tasks.
• You run a Nextflow script using the nextflow run command.

Content from Workflow parameterisation

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Last updated on 2024-08-23 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• How can I change the data a workflow uses?
• How can I parameterise a workflow?
• How can I add my parameters to a file?

Objectives

• Use pipeline parameters to change the input to a workflow.
• Add a pipeline parameters to a Nextflow script.
• Understand how to create and use a parameter file.

In the first episode we ran the Nextflow script, word_count.nf, from the command line and it counted the number of lines in the file data/yeast/reads/ref1_1.fq.gz. To change the input to script we can make use of pipeline parameters.

Pipeline parameters

---

The Nextflow word_count.nf script defines a pipeline parameter params.input. Pipeline parameters enable you to change the input to the workflow at runtime, via the command line or a configuration file, so they are not hard-coded into the script.

Pipeline parameters are declared in the workflow by prepending the prefix params, separated by the dot character, to a variable name e.g., params.input.

Their value can be specified on the command line by prefixing the parameter name with a double dash character, e.g., --input.

In the script word_count.nf the pipeline parameter params.input was specified with a value of "data/yeast/reads/ref1_1.fq.gz".

To process a different file, e.g. data/yeast/reads/ref2_2.fq.gz, in the word_count.nf script we would run:

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/ref2_2.fq.gz'

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `word_count.nf` [gigantic_woese] - revision: 8acb5cb9b0
executor >  local (1)
[26/3cf986] process > NUM_LINES (1) [100%] 1 of 1 ✔
ref2_2.fq.gz 81720

We can also use wild cards to specify multiple input files (This will be covered in the channels episode). In the example below we use the * to match any sequence of characters between ref2_ and .fq.gz. Note: If you use wild card characters on the command line you must enclose the value in quotes.

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/ref2_*.fq.gz'

This runs the process NUM_LINES twice, once for each file it matches.

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `word_count.nf` [tender_lumiere] - revision: 8acb5cb9b0
executor >  local (2)
[cc/b6f793] process > NUM_LINES (1) [100%] 2 of 2 ✔
ref2_2.fq.gz 81720

ref2_1.fq.gz 81720

Change a pipeline’s input using a parameter

Re-run the Nextflow script word_count.nf by changing the pipeline input to all files in the directory data/yeast/reads/ that begin with ref and end with .fq.gz:

Show me the solution

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/ref*.fq.gz'

The string specified on the command line will override the default value of the parameter in the script. The output will look like this:

OUTPUT

N E X T F L O W  ~  version 20.10.0
Launching `word_count.nf` [soggy_miescher] - revision: c54a707593
executor >  local (6)
[d3/9ca185] process > NUM_LINES (2) [100%] 6 of 6 ✔
ref3_2.fq.gz 52592

ref2_2.fq.gz 81720

ref1_1.fq.gz 58708

ref1_2.fq.gz 58708

ref3_1.fq.gz 52592

ref2_1.fq.gz 81720

Adding a parameter to a script

---

To add a pipeline parameter to a script prepend the prefix params, separated by a dot character ., to a variable name e.g., params.input.

Let’s make a copy of the word_count.nf script as wc-params.nf and add a new input parameter.

BASH

$ cp word_count.nf wc-params.nf

To add a parameter sleep with the default value 2 to wc-params.nf we add the line:

GROOVY

params.sleep = 2

Note: You should always add a sensible default value to the pipeline parameter. We can use this parameter to add another step to our NUM_LINES process.

GROOVY

script:
 """
 sleep ${params.sleep}
 printf '${read} '
 gunzip -c ${read} | wc -l
 """

This step, sleep ${params.sleep}, will add a delay for the amount of time specified in the params.sleep variable, by default 2 seconds. To access the value inside the script block we use {variable_name} syntax e.g. ${params.sleep}.

We can now change the sleep parameter from the command line, For Example:

BASH

nextflow run wc-params.nf --sleep 10

Add a pipeline parameter

If you haven’t already make a copy of the word_count.nf as wc-params.nf.

BASH

$ cp word_count.nf wc-params.nf

Add the param sleep with a default value of 2 below the params.input line. Add the line sleep ${params.sleep} in the process NUM_LINES above the line printf ${read}.

Run the new script wc-params.nf changing the sleep input time.

What input file would it run and why?

How would you get it to process all .fq.gz files in the data/yeast/reads directory as well as changing the sleep input to 1 second?

Show me the solution

GROOVY

params.sleep=2

GROOVY

script: 
"""
sleep ${params.sleep}
printf '${read}\\t'
gunzip -c ${read} | wc -l 
"""

BASH

$ nextflow run wc-params.nf --sleep 1 

This would use 1 as a value of sleep parameter instead of default value (which is 2) and run the pipeline. The input file would be data/yeast/reads/ref1_1.fq.gz as this is the default. To run all input files we could add the param --input 'data/yeast/reads/*.fq.gz'

BASH

$ nextflow run wc-params.nf --sleep 1 --input 'data/yeast/reads/*.fq.gz' 

Parameter File

---

If we have many parameters to pass to a script it is best to create a parameters file. Parameters can be stored in JSON format. JSON is a data serialization language, that is a way of storing data objects and structures, such as the params object in a file.

The -params-file option is used to pass the parameters file to the script.

For example the file wc-params.json contains the parameters sleep and input in JSON format.

JSON

{
  "sleep": 5,
  "input": "data/yeast/reads/etoh60_1*.fq.gz"
}

Create a file called wc-params.json with the above contents.

To run the wc-params.nf script using these parameters we add the option -params-file and pass the file wc-params.json:

BASH

$ nextflow run wc-params.nf -params-file wc-params.json

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `wc-params.nf` [nostalgic_northcutt] - revision: 2f86c9ac7e
executor >  local (2)
[b4/747eaa] process > NUM_LINES (1) [100%] 2 of 2 ✔
etoh60_1_2.fq.gz 87348

etoh60_1_1.fq.gz 87348

Create and use a Parameter file.

Create a parameter file params.json for the Nextflow file wc-params.nf, and run the Nextflow script using the created parameter file, specifying:

• sleep as 10
• input as data/yeast/reads/ref3_1.fq.gz

Show me the solution

JSON

{
"sleep": 10,
"input": "data/yeast/reads/ref3_1.fq.gz"
}

BASH

$ nextflow run wc-params.nf -params-file params.json 

OUTPUT

N E X T F L O W
 version 21.04.0 Launching `wc-params.nf` [small_wiles] - revision:
f5ef7b7a01 executor \> local (1) [f3/4fa480] process \> NUM_LINES
(1) [100%] 1 of 1 ✔ ref3_1.fq.gz 52592 

Key Points

• Pipeline parameters are specified by prepending the prefix params to a variable name, separated by dot character.
• To specify a pipeline parameter on the command line for a Nextflow run use --variable_name syntax.
• You can add parameters to a JSON formatted file and pass them to the script using option -params-file.

Content from Channels

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Last updated on 2024-08-23 | Edit this page

Estimated time: 40 minutes

Overview

Questions

• How do I move data around in Nextflow?
• How do I handle different types of input, e.g. files and parameters?
• How do I create a Nextflow channel?
• How can I use pattern matching to select input files?
• How do I change the way inputs are handled?

Objectives

• Understand how Nextflow manages data using channels.
• Understand the different types of Nextflow channels.
• Create a value and queue channel using channel factory methods.
• Select files as input based on a glob pattern.
• Edit channel factory arguments to alter how data is read in.

Channels

---

Earlier we learnt that channels are the way in which Nextflow sends data around a workflow. Channels connect processes via their inputs and outputs. Channels can store multiple items, such as files (e.g., fastq files) or values. The number of items a channel stores determines how many times a process will run using that channel as input.
Note: When the process runs using one item from the input channel, we will call that run a task.

Why use Channels?

---

Channels are how Nextflow handles file management, allowing complex tasks to be split up, run in parallel, and reduces ‘admin’ required to get the right inputs to the right parts of the pipeline.

Channels are asynchronous, which means that outputs from a set of processes will not necessarily be produced in the same order as the corresponding inputs went in. However, the first element into a channel queue is the first out of the queue (First in - First out). This allows processes to run as soon as they receive input from a channel. Channels only send data in one direction, from a producer (a process/operator), to a consumer (another process/operator).

Channel types

---

Nextflow distinguishes between two different kinds of channels: queue channels and value channels.

Queue channel

Queue channels are a type of channel in which data is consumed (used up) to make input for a process/operator. Queue channels can be created in two ways:

1. As the outputs of a process.
2. Explicitly using channel factory methods such as Channel.of or Channel.fromPath.

Value channels

The second type of Nextflow channel is a value channel. A value channel is bound to a single value. A value channel can be used an unlimited number times since its content is not consumed. This is also useful for processes that need to reuse input from a channel, for example, a reference genome sequence file that is required by multiple steps within a process, or by more than one process.

Queue vs Value Channel.

What type of channel would you use to store the following?

1. Multiple values.
2. A list with one or more values.
3. A single value.

Show me the solution

1. A queue channels is used to store multiple values.
2. A value channel is used to store a single value, this can be a list with multiple values.
3. A value channel is used to store a single value.

Instructor Note

Create a nextflow script called to channel.nf for the following examples. Explain that as you are just setting up channels you do not need to add any processes or a workflow block.

Run using:

BASH

$ nextflow run channel.nf

Creating Channels using Channel factories

---

Channel factories are used to explicitly create channels. In programming, factory methods (functions) are a programming design pattern used to create different types of objects (in this case, different types of channels). They are implemented for things that represent more generalised concepts, such as a Channel.

Channel factories are called using the Channel.<method> syntax, and return a specific instance of a Channel.

The value Channel factory

The value factory method is used to create a value channel. Values are put inside parentheses () to assign them to a channel.

For example:

GROOVY

ch1 = Channel.value( 'GRCh38' )
ch2 = Channel.value( ['chr1', 'chr2', 'chr3', 'chr4', 'chr5'] )
ch3 = Channel.value( ['chr1' : 248956422, 'chr2' : 242193529, 'chr3' : 198295559] )

1. Creates a value channel and binds a string to it.
2. Creates a value channel and binds a list object to it that will be emitted as a single item.
3. Creates a value channel and binds a map object to it that will be emitted as a single item.

The value method can only take 1 argument, however, this can be a single list or map containing several elements.

Reminder:

• A List object can be defined by placing the values in square brackets [] separated by a comma.
• A Map object is similar, but with key:value pairs separated by commas.

GROOVY

myList = [1776, -1, 33, 99, 0, 928734928763]
myMap = [ p1 : "start", q2 : "end" ]

To view the contents of a value channel, use the view operator. We will learn more about channel operators in a later episode.

GROOVY

ch1 = Channel.value( 'GRCh38' )
ch2 = Channel.value( ['chr1', 'chr2', 'chr3', 'chr4', 'chr5'] )
ch3 = Channel.value( ['chr1' : 248956422, 'chr2' : 242193529, 'chr3' : 198295559] )
ch1.view()
ch2.view()
ch3.view()

Each item in the channel is printed on a separate line.

OUTPUT

GRCh38
[chr1, chr2, chr3, chr4, chr5]
[chr1:248956422, chr2:242193529, chr3:198295559]

Queue channel factory

---

Queue (consumable) channels can be created using the following channel factory methods.

• Channel.of
• Channel.fromList
• Channel.fromPath
• Channel.fromFilePairs
• Channel.fromSRA

The of Channel factory

When you want to create a channel containing multiple values you can use the channel factory Channel.of. Channel.of allows the creation of a queue channel with the values specified as arguments, separated by a ,.

GROOVY

chromosome_ch = Channel.of( 'chr1', 'chr3', 'chr5', 'chr7' )
chromosome_ch.view()

OUTPUT

chr1
chr3
chr5
chr7

The first line in this example creates a variable chromosome_ch. chromosome_ch is a queue channel containing the four values specified as arguments in the of method. The view operator will print one line per item in a list. Therefore the view operator on the second line will print four lines, one for each element in the channel:

You can specify a range of numbers as a single argument using the Groovy range operator ... This creates each value in the range (including the start and end values) as a value in the channel. The Groovy range operator can also produce ranges of dates, letters, or time. More information on the range operator can be found here.

GROOVY

chromosome_ch = Channel.of(1..22, 'X', 'Y')
chromosome_ch.view()

Arguments passed to the of method can be of varying types e.g., combinations of numbers, strings, or objects. In the above examples we have examples of both string and number data types.

Channel.from

You may see the method Channel.from in older nextflow scripts. This performs a similar function but is now deprecated (no longer used), and so Channel.of should be used instead.

Create a value and Queue and view Channel contents

1. Create a Nextflow script file called channel.nf .
2. Create a Value channel ch_vl containing the String 'GRCh38'.
3. Create a Queue channel ch_qu containing the values 1 to 4.
4. Use .view() operator on the channel objects to view the contents of the channels.
5. Run the code using

BASH

$ nextflow run channel.nf

Show me the solution

GROOVY

ch_vl = Channel.value('GRCh38')
ch_qu = Channel.of(1,2,3,4)
ch_vl.view()
ch_qu.view()

OUTPUT

 N E X T F L O W  ~  version 21.04.0
 Launching `channel.nf` [condescending_dalembert] - revision: c80908867b
 GRCh38
 1
 2
 3
 4

The fromList Channel factory

You can use the Channel.fromList method to create a queue channel from a list object.

GROOVY

aligner_list = ['salmon', 'kallisto']

aligner_ch = Channel.fromList(aligner_list)

aligner_ch.view()

This would produce two lines.

OUTPUT

salmon
kallisto

Channel.fromList vs Channel.of

In the above example, the channel has two elements. If you has used the Channel.of(aligner_list) it would have contained only 1 element [salmon, kallisto] and any operator or process using the channel would run once.

Creating channels from a list

Write a Nextflow script that creates both a queue and value channel for the list

GROOVY

ids = ['ERR908507', 'ERR908506', 'ERR908505']

Then print the contents of the channels using the view operator. How many lines does the queue and value channel print?

Hint: Use the fromList() and value() Channel factory methods.

Show me the solution

GROOVY

ids = ['ERR908507', 'ERR908506', 'ERR908505']

queue_ch = Channel.fromList(ids)
value_ch = Channel.value(ids)
queue_ch.view()
value_ch.view()

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `channel_fromList.nf` [wise_hodgkin] - revision: 22d76be151
ERR908507
ERR908506
ERR908505
[ERR908507, ERR908506, ERR908505]

The queue channel queue_ch will print three lines.

The value channel value_ch will print one line.

The fromPath Channel factory

The previous channel factory methods dealt with sending general values in a channel. A special channel factory method fromPath is used when wanting to pass files.

The fromPath factory method creates a queue channel containing one or more files matching a file path.

The file path (written as a quoted string) can be the location of a single file or a “glob pattern” that matches multiple files or directories.

The file path can be a relative path (path to the file from the current directory), or an absolute path (path to the file from the system root directory - starts with /).

The script below creates a queue channel with a single file as its content.

GROOVY

read_ch = Channel.fromPath( 'data/yeast/reads/ref1_2.fq.gz' )
read_ch.view()

OUTPUT

data/yeast/reads/ref1_2.fq.gz

You can also use glob syntax to specify pattern-matching behaviour for files. A glob pattern is specified as a string and is matched against directory or file names.

• An asterisk, *, matches any number of characters (including none).
• Two asterisks, **, works like * but will also search sub directories. This syntax is generally used for matching complete paths.
• Braces {} specify a collection of subpatterns. For example: {bam,bai} matches “bam” or “bai”

For example the script below uses the *.fq.gz pattern to create a queue channel that contains as many items as there are files with .fq.gz extension in the data/yeast/reads folder.

GROOVY

read_ch = Channel.fromPath( 'data/yeast/reads/*.fq.gz' )
read_ch.view()

OUTPUT

data/yeast/reads/ref1_2.fq.gz
data/yeast/reads/etoh60_3_2.fq.gz
data/yeast/reads/temp33_1_2.fq.gz
data/yeast/reads/temp33_2_1.fq.gz
data/yeast/reads/ref2_1.fq.gz
data/yeast/reads/temp33_3_1.fq.gz
[..truncated..]

Note The pattern must contain at least a star wildcard character.

You can change the behaviour of Channel.fromPath method by changing its options. A list of .fromPath options is shown below.

Available fromPath options:

Name	Description
glob	When true, the characters *, ?, [] and {} are interpreted as glob wildcards, otherwise they are treated as literal characters (default: true)
type	The type of file paths matched by the string, either file, dir or any (default: file)
hidden	When true, hidden files are included in the resulting paths (default: false)
maxDepth	Maximum number of directory levels to visit (default: no limit)
followLinks	When true, symbolic links are followed during directory tree traversal, otherwise they are managed as files (default: true)
relative	When true returned paths are relative to the top-most common directory (default: false)
checkIfExists	When true throws an exception if the specified path does not exist in the file system (default: false)

We can change the default options for the fromPath method to give an error if the file doesn’t exist using the checkIfExists parameter. In Nextflow, method parameters are separated by a , and parameter values specified with a colon :.

If we execute a Nextflow script with the contents below, it will run and not produce an output, or an error message that the file does not exist. This is likely not what we want.

GROOVY

read_ch = Channel.fromPath( 'data/chicken/reads/*.fq.gz' )
read_ch.view()

OUTPUT

N E X T F L O W   ~  version 20.10.0

Launching `channels.nf` [scruffy_swartz] DSL2 - revision: 2c8f18ab48

Add the argument checkIfExists with the value true.

GROOVY

read_ch = Channel.fromPath( 'data/chicken/reads/*.fq.gz', checkIfExists: true )
read_ch.view()

This will give an error as there is no data/chicken directory.

OUTPUT

N E X T F L O W  ~  version 20.10.0
Launching `channels.nf` [intergalactic_mcclintock] - revision: d2c138894b
No files match pattern `*.fq.gz` at path: data/chicken/reads/

Using Channel.fromPath

1. Create a Nextflow script channel_fromPath.nf
2. Use the Channel.fromPath method to create a channel containing all files in the data/yeast/ directory, including the subdirectories.
3. Add the parameter to include any hidden files.
4. Then print all file names using the view operator.

Hint: You need two asterisks, i.e. **, to search subdirectories.

Show me the solution

GROOVY

all_files_ch = Channel.fromPath('data/yeast/**', hidden: true)
all_files_ch.view()

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `channel_fromPath.nf` [reverent_mclean] - revision: cf02269bcb
data/yeast/samples.csv
data/yeast/reads/etoh60_3_2.fq.gz
data/yeast/reads/temp33_1_2.fq.gz
data/yeast/reads/temp33_2_1.fq.gz
[..truncated..]

The fromFilePairs Channel factory

We have seen how to process files individually using fromPath. In Bioinformatics we often want to process files in pairs or larger groups, such as read pairs in sequencing.

For example is the data/yeast/reads directory we have nine groups of read pairs.

Sample group	read1	read2
ref1	data/yeast/reads/ref1_1.fq.gz	data/yeast/reads/ref1_2.fq.gz
ref2	data/yeast/reads/ref2_1.fq.gz	data/yeast/reads/ref2_2.fq.gz
ref3	data/yeast/reads/ref3_1.fq.gz	data/yeast/reads/ref3_2.fq.gz
temp33_1	data/yeast/reads/temp33_1_1.fq.gz	data/yeast/reads/temp33_1_2.fq.gz
temp33_2	data/yeast/reads/temp33_2_1.fq.gz	data/yeast/reads/temp33_2_2.fq.gz
temp33_3	data/yeast/reads/temp33_3_1.fq.gz	data/yeast/reads/temp33_3_2.fq.gz
etoh60_1	data/yeast/reads/etoh60_1_1.fq.gz	data/yeast/reads/etoh60_1_2.fq.gz
etoh60_2	data/yeast/reads/etoh60_2_1.fq.gz	data/yeast/reads/etoh60_2_2.fq.gz
etoh60_3	data/yeast/reads/etoh60_3_1.fq.gz	data/yeast/reads/etoh60_3_2.fq.gz

Nextflow provides a convenient Channel factory method for this common bioinformatics use case. The fromFilePairs method creates a queue channel containing a tuple for every set of files matching a specific glob pattern (e.g., /path/to/*_{1,2}.fq.gz).

A tuple is a grouping of data, represented as a Groovy List.

1. The first element of the tuple emitted from fromFilePairs is a string based on the shared part of the filenames (i.e., the * part of the glob pattern).
2. The second element is the list of files matching the remaining part of the glob pattern (i.e., the <string>_{1,2}.fq.gz pattern). This will include any files ending _1.fq.gz or _2.fq.gz.

GROOVY

read_pair_ch = Channel.fromFilePairs('data/yeast/reads/*_{1,2}.fq.gz')
read_pair_ch.view()

OUTPUT

[etoh60_3, [data/yeast/reads/etoh60_3_1.fq.gz, data/yeast/reads/etoh60_3_2.fq.gz]]
[temp33_1, [data/yeast/reads/temp33_1_1.fq.gz, data/yeast/reads/temp33_1_2.fq.gz]]
[ref1, [data/yeast/reads/ref1_1.fq.gz, data/yeast/reads/ref1_2.fq.gz]]
[ref2, [data/yeast/reads/ref2_1.fq.gz, data/yeast/reads/ref2_2.fq.gz]]
[temp33_2, [data/yeast/reads/temp33_2_1.fq.gz, data/yeast/reads/temp33_2_2.fq.gz]]
[ref3, [data/yeast/reads/ref3_1.fq.gz, data/yeast/reads/ref3_2.fq.gz]]
[temp33_3, [data/yeast/reads/temp33_3_1.fq.gz, data/yeast/reads/temp33_3_2.fq.gz]]
[etoh60_1, [data/yeast/reads/etoh60_1_1.fq.gz, data/yeast/reads/etoh60_1_2.fq.gz]]
[etoh60_2, [data/yeast/reads/etoh60_2_1.fq.gz, data/yeast/reads/etoh60_2_2.fq.gz]]

This will produce a queue channel, read_pair_ch , containing nine elements.

Each element is a tuple that has;

1. string value (the file prefix matched, e.g temp33_1)
2. and a list with the two files e,g. [data/yeast/reads/temp33_1_1.fq.gz, data/yeast/reads/temp33_1_2.fq.gz] .

The asterisk character *, matches any number of characters (including none), and the {} braces specify a collection of subpatterns. Therefore the *_{1,2}.fq.gz pattern matches any file name ending in _1.fq.gz or _2.fq.gz .

What if you want to capture more than a pair?

If you want to capture more than two files for a pattern you will need to change the default size argument (the default value is 2) to the number of expected matching files.

For example in the directory data/yeast/reads there are six files with the prefix ref. If we want to group (create a tuple) for all of these files we could write;

GROOVY

read_group_ch = Channel.fromFilePairs('data/yeast/reads/ref{1,2,3}*',size:6)
read_group_ch.view()

The code above will create a queue channel containing one element. The element is a tuple of which contains a string value, that is the pattern ref, and a list of six files matching the pattern.

OUTPUT

[ref, [data/yeast/reads/ref1_1.fq.gz, data/yeast/reads/ref1_2.fq.gz, data/yeast/reads/ref2_1.fq.gz, data/yeast/reads/ref2_2.fq.gz, data/yeast/reads/ref3_1.fq.gz, data/yeast/reads/ref3_2.fq.gz]]

See more information about the channel factory fromFilePairs here

More complex patterns

If you need to match more complex patterns you should create a sample sheet specifying the files and create a channel from that. This will be covered in the operator episode.

Create a channel containing groups of files

1. Create a Nextflow script file channel_fromFilePairs.nf .
2. Use the fromFilePairs method to create a channel containing three tuples. Each tuple will contain the pairs of fastq reads for the three temp33 samples in the data/yeast/reads directory

Show me the solution

GROOVY

pairs_ch = Channel.fromFilePairs('data/yeast/reads/temp33*_{1,2}.fq.gz')
pairs_ch.view()

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `channels.nf` [stupefied_lumiere] - revision: a3741edde2
[temp33_1, [data/yeast/reads/temp33_1_1.fq.gz, data/yeast/reads/temp33_1_2.fq.gz]]
[temp33_3, [data/yeast/reads/temp33_3_1.fq.gz, data/yeast/reads/temp33_3_2.fq.gz]]
[temp33_2, [data/yeast/reads/temp33_2_1.fq.gz, data/yeast/reads/temp33_2_2.fq.gz]]

Key Points

• Channels must be used to import data into Nextflow.
• Nextflow has two different kinds of channels: queue channels and value channels.
• Data in value channels can be used multiple times in workflow.
• Data in queue channels are consumed when they are used by a process or an operator.
• Channel factory methods, such as Channel.of, are used to create channels.
• Channel factory methods have optional parameters e.g., checkIfExists, that can be used to alter the creation and behaviour of a channel.

Content from Processes

---

Last updated on 2024-08-23 | Edit this page

Estimated time: 45 minutes

Overview

Questions

• How do I run tasks/processes in Nextflow?
• How do I get data, files and values, into a processes?

Objectives

• Understand how Nextflow uses processes to execute tasks.
• Create a Nextflow process.
• Define inputs to a process.

Processes

---

We now know how to create and use Channels to send data around a workflow. We will now see how to run tasks within a workflow using processes.

A process is the way Nextflow executes commands you would run on the command line or custom scripts.

A process can be thought of as a particular step in a workflow, e.g. an alignment step in RNA-seq analysis. Processes are independent of each other (don’t require any another process to execute) and can not communicate/write to each other. Data is passed between processes via input and output Channels.

For example, below is the command you would run to count the number of sequence records in a FASTA format file such as the yeast transcriptome:

FASTA format

FASTA format is a text-based format for representing either nucleotide sequences or peptide sequences. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (“>”) symbol in the first column.

BASH

>YBR024W_mRNA cdna chromosome:R64-1-1:II:289445:290350:1 gene:YBR024W gene_biotype:protein_coding transcript_biotype:protein_coding gene_symbol:SCO2 description:Protein anchored to mitochondrial inner membrane; may have a redundant function with Sco1p in delivery of copper to cytochrome c oxidase; interacts with Cox2p; SCO2 has a paralog, SCO1, that arose from the whole genome duplication [Source:SGD;Acc:S000000228]
ATGTTGAATAGTTCAAGAAAATATGCTTGTCGTTCCCTATTCAGACAAGCGAACGTCTCA
ATAAAAGGACTCTTTTATAATGGAGGCGCATATCGAAGAGGGTTTTCAACGGGATGTTGT

zgrep -c ‘^>’

The command zgrep -c '^>' data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz is used in Unix-like systems for a specific purpose: it counts the number of sequences in a compressed FASTA file. The tool zgrep combines the functionalities of ‘grep’ for pattern searching and ‘gzip’ for handling compressed files. The -c option modifies this command to count the occurrences of lines matching the pattern, instead of displaying them. The pattern '^>' is designed to find lines that start with ‘>’, which in FASTA files, denotes the beginning of a new sequence. Thus, this command efficiently counts how many sequences are contained within the specified compressed FASTA file.

BASH

$ zgrep -c '^>' data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz

OUTPUT

6612

Now we will show how to convert this into a simple Nextflow process.

Process definition

---

The process definition starts with keyword process, followed by process name, in this case NUMSEQ, and finally the process body delimited by curly brackets {}. The process body must contain a string which represents the command or, more generally, a script that is executed by it.

GROOVY

process NUMSEQ {
  script:
  "zgrep -c '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
}

This process would run once.

Implicit variables

We use the Nextflow implicit variable ${projectDir} to specify the directory where the main script is located. This is important as Nextflow scripts are executed in a separate working directory. A full list of implicit variables can be found here

To add the process to a workflow add a workflow block, and call the process like a function. We will learn more about the workflow block in the workflow episode.

GROOVY

//process_01.nf


process NUMSEQ {
  script:
  "zgrep -c '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
}

workflow {
  //process is called like a function in the workflow block
  NUMSEQ()
}

We can now run the process:

BASH

$ nextflow run process_01.nf -process.debug

Note We need to add the Nextflow run option -process.debug to print the output to the terminal.

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `process_01.nf` [modest_pike] - revision: 3eaa812b17
executor >  local (1)
[cd/eab1fd] process > NUMSEQ [100%] 1 of 1 ✔
6612

A Simple Process

Create a Nextflow script simple_process.nf that has one process COUNT_BASES that runs the command.

BASH

zgrep -v '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz|tr -d '\n'|wc -m

Show me the solution

GROOVY

process COUNT_BASES {
   
script:
"""
zgrep -v '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz|tr -d '\n'|wc -m
"""
}

workflow {
COUNT_BASES()
}

BASH

$ nextflow run simple_process.nf -process.debug

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `simple_process.nf`` [prickly_gilbert] - revision: 471a79c65c
executor >  local (1)
[56/5e6001] process > COUNT_BASES [100%] 1 of 1 ✔
8772368

Definition blocks

The previous example was a simple process with no defined inputs and outputs that ran only once. To control inputs, outputs and how a command is executed a process may contain five definition blocks:

1. directives - 0, 1, or more: allow the definition of optional settings that affect the execution of the current process e.g. the number of cpus a task uses and the amount of memory allocated.
2. inputs - 0, 1, or more: Define the input dependencies, usually channels, which determines the number of times a process is executed.
3. outputs - 0, 1, or more: Defines the output channels used by the process to send results/data produced by the process.
4. when clause - optional: Allows you to define a condition that must be verified in order to execute the process.
5. script block - required: A statement within quotes that defines the commands that are executed by the process to carry out its task.

The syntax is defined as follows:

GROOVY

process < NAME > {
  [ directives ]        
  input:                
  < process inputs >
  output:               
  < process outputs >
  when:                 
  < condition >
  [script|shell|exec]:  
  < user script to be executed >
}

Script

---

At minimum a process block must contain a script block.

The script block is a String “statement” that defines the command that is executed by the process to carry out its task. These are normally the commands you would run on a terminal.

A process contains only one script block, and it must be the last statement when the process contains input and output declarations.

The script block can be a simple one line string in quotes e.g.

GROOVY

process NUMSEQ {
    script:
    "zgrep -c '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
}

workflow {
  NUMSEQ()
}

Or, for commands that span multiple lines you can encase the command in triple quotes """.

For example:

GROOVY

//process_multi_line.nf


process NUMSEQ_CHR {
    script:
    """
    zgrep  '^>' ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz > ids.txt
    zgrep -c '>YA' ids.txt
    """
}

workflow {
  NUMSEQ_CHR()
}

BASH

$ nextflow run process_multi_line.nf -process.debug

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `process_multi_line.nf` [focused_jang] - revision: e32caf0dcb
executor >  local (1)
[90/6e38f4] process > NUMSEQ_CHR [100%] 1 of 1 ✔
118

Instructor Note

The following section on python is meant to be run by the instructor not the learners. It is meant to be a demonstration of the different ways to run a process. This can be skipped for time.

By default the process command is interpreted as a Bash script. However, any other scripting language can be used just simply starting the script with the corresponding Shebang declaration. For example:

GROOVY

//process_python.nf


process PROCESS_READS {
  script:
  """
  #!/usr/bin/env python
  import gzip

  reads = 0
  bases = 0

  with gzip.open('${projectDir}/data/yeast/reads/ref1_1.fq.gz', 'rb') as read:
      for id in read:
          seq = next(read)
          reads += 1
          bases += len(seq.strip())
          next(read)
          next(read)

  print("reads", reads)
  print("bases", bases)
  """
}

workflow {
  PROCESS_READS()
}

BASH

$ nextflow run process_python.nf -process.debug

OUTPUT

 N E X T F L O W   ~  version 24.04.4

Launching `process_python.nf` [mad_montalcini] DSL2 - revision: ee25d49465

executor >  local (1)
[b4/a100c3] PROCESS_READS [100%] 1 of 1 ✔
reads 14677
bases 1482377

This allows the use of a different programming languages which may better fit a particular job. However, for large chunks of code it is suggested to save them into separate files and invoke them from the process script.

Associated scripts

---

Scripts such as the one in the example below, process_reads.py, can be stored in a bin folder at the same directory level as the Nextflow workflow script that invokes them, and given execute permission. Nextflow will automatically add this folder to the PATH environment variable. To invoke the script in a Nextflow process, simply use its filename on its own rather than invoking the interpreter e.g. process_reads.py instead of python process_reads.py. Note The script process_reads.py must be executable to run.

BASH

mkdir bin
mv process_reads.py bin
chmod 755 bin/process_reads.py

PYTHON

#!/usr/bin/env python
# process_reads.py
import gzip
import sys
reads = 0
bases = 0


with gzip.open(sys.argv[1], 'rb') as read:
    for id in read:
      seq = next(read)
      reads += 1
      bases += len(seq.strip())
      next(read)
      next(read)

print("reads", reads)
print("bases", bases)

GROOVY

//process_python_script.nf


process PROCESS_READS {

  script:
  """
  process_reads.py ${projectDir}/data/yeast/reads/ref1_1.fq.gz
  """
}

workflow {
  PROCESS_READS()
}

BASH

nextflow run process_python_script.nf -process.debug

OUTPUT

N E X T F L O W  ~  version 23.10.1
Launching `pr.nf` [kickass_legentil] DSL2 - revision: 3b9eee1d47
executor >  local (1)
[88/759311] process > PROCESS_READS [100%] 1 of 1 ✔
reads 14677
bases 1482377

Associated scripts

Scripts such as the one in the example above, process_reads.py, can be stored in a bin folder at the same directory level as the Nextflow workflow script that invokes them, and given execute permission. Nextflow will automatically add this folder to the PATH environment variable. To invoke the script in a Nextflow process, simply use its filename on its own rather than invoking the interpreter e.g. process_reads.py instead of python process_reads.py.

Script parameters

The command in the script block can be defined dynamically using Nextflow variables e.g. ${projectDir}. To reference a variable in the script block you can use the $ in front of the Nextflow variable name, and additionally you can add {} around the variable name e.g. ${projectDir}.

Variable substitutions

Similar to bash scripting Nextflow uses the $ character to introduce variable substitutions. The variable name to be expanded may be enclosed in braces {variable_name}, which are optional but serve to protect the variable to be expanded from characters immediately following it which could be interpreted as part of the name. It is a good rule of thumb to always use the {} syntax because it enhances readability and clarity, ensures correct variable interpretation, and prevents potential syntax errors in complex expressions.

In the example below the variable chr is set to the value A at the top of the Nextflow script. The variable is referenced using the $chr syntax within the multi-line string statement in the script block. A Nextflow variable can be used multiple times in the script block.

GROOVY

//process_script.nf


chr = "A"

process CHR_COUNT {

  script:
  """
  printf "Number of sequences for chromosome ${chr} :"
  zgrep -c '>Y'${chr} ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
  """
}

workflow {
  CHR_COUNT()
}

In most cases we do not want to hard code parameter values. We saw in the parameter episode the use of a special Nextflow variable params that can be used to assign values from the command line. You would do this by adding a key name to the params variable and specifying a value, like params.keyname = value

In the example below we define the variable params.chr with a default value of A in the Nextflow script.

GROOVY

//process_script_params.nf


params.chr = "A"

process CHR_COUNT {

  script:
  """
  printf  'Number of sequences for chromosome '${params.chr}':'
  zgrep  -c '^>Y'${params.chr} ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
  """
}

workflow {
  CHR_COUNT()
}

Remember, we can change the default value of chr to a different value such as B, by running the Nextflow script using the command below. Note: parameters to the workflow have two hyphens --.

BASH

nextflow run process_script_params.nf --chr B -process.debug

OUTPUT

 N E X T F L O W   ~  version 24.04.3

Launching `process_script_params.nf` [pedantic_mandelbrot] DSL2 - revision: 538e3c2b38

executor >  local (1)
[19/6d96a0] process > CHR_COUNT [100%] 1 of 1 ✔
Number of sequences for chromosome B:456

Script parameters

For the Nextflow script below.

GROOVY

//process_exercise_script_params.nf


process COUNT_BASES {

script:
"""
zgrep -v  '^>'   ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz|grep -o A|wc -l   
"""
}

workflow {
   COUNT_BASES()
 }

Add a parameter params.base to the script and uses the variable ${param.base} insides the script. Run the pipeline using a base value of C using the --base command line option.

BASH

$ nextflow run process_script_params.nf --base <some value> -process.debug

Note: The Nextflow option -process.debug will print the process’ stdout to the terminal.

Show me the solution

GROOVY

 //process_exercise_script_params.nf
 

 params.base='A'

 process COUNT_BASES {
  
 script:
  """
  zgrep -v  '^>'   ${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz|grep -o ${params.base}|wc -l   
  """
 }

 workflow {
   COUNT_BASES()
 }

BASH

$ nextflow run process_script_params.nf --base C -process.debug

OUTPUT

  N E X T F L O W  ~  version 21.04.0
  Launching `process_script_params.nf ` [nostalgic_jones] - revision: 9feb8de4fe
  executor >  local (1)
  [92/cdc9de] process > COUNT_BASES [100%] 1 of 1 ✔
  1677188

Bash variables

Nextflow uses the same Bash syntax for variable substitutions, $variable, in strings. However, Bash variables need to be escaped using \ character in front of \$variable name.

In the example below we will set a bash variable NUMIDS then echo the value of NUMIDS in our script block.

GROOVY

//process_escape_bash.nf

process NUM_IDS {

  script:
  """
  #set bash variable NUMIDS
  NUMIDS=`zgrep -c '^>' $params.transcriptome`

  echo 'Number of sequences'
  printf "%'d\n" \$NUMIDS
  """
}

params.transcriptome = "${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

workflow {
  NUM_IDS()
}

Shell

Another alternative is to use a shell block definition instead of script. When using the shell statement Bash variables are referenced in the normal way $my_bash_variable; However, the shell statement uses a different syntax for Nextflow variable substitutions: !{nextflow_variable}, which is needed to use both Nextflow and Bash variables in the same script.

For example in the script below that uses the shell statement we reference the Nextflow variables as !{projectDir} , and the Bash variable as ${NUMCHAR} and ${NUMLINES}.

GROOVY

//process_shell.nf


process NUM_IDS {

  shell:
  //Shell script definition requires the use of single-quote ' delimited strings
  '''
  #set bash variable NUMIDS
  NUMIDS=`zgrep -c '^>' !{params.transcriptome}`

  echo 'Number of sequences'
  printf  $NUMIDS
  '''
}

params.transcriptome = "${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

workflow {
  NUM_IDS()
}

Conditional script execution

Sometimes you want to change how a process is run depending on some condition. In Nextflow scripts we can use conditional statements such as the if statement or any other expression evaluating to boolean value true or false.

If statement

The if statement uses the same syntax common to other programming languages such Java, C, JavaScript, etc.

GROOVY

if( < boolean expression > ) {
    // true branch
}
else if ( < boolean expression > ) {
    // true branch
}
else {
    // false branch
}

For example, the Nextflow script below will use the if statement to change what the COUNT process counts depending on the Nextflow variable params.method.

GROOVY

//process_conditional.nf


params.method = 'ids'
params.transcriptome = "$projectDir/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"


process COUNT {
  script:
  if( params.method == 'ids' ) {
    """
    echo Number of sequences in transciptome
    zgrep -c "^>" $params.transcriptome
    """
  }  
  else if( params.method == 'bases' ) {
    """
    echo Number of bases in transciptome
    zgrep -v "^>" $params.transcriptome|grep -o "."|wc -l
    """
  }  
  else {
    """
    echo Unknown method $params.method
    """
  }  
}

workflow {
  COUNT()
}

BASH

$ nextflow run process_conditional.nf -process.debug --method ids

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `juggle_processes.nf` [cheeky_shirley] - revision: 588f20ae5a
[01/60b08d] process > COUNT [100%] 1 of 1 ✔
Number of sequences in transciptome
6612

Inputs

---

Processes are isolated from each other but can communicate by sending values and files via Nextflow channels from input and into output blocks.

The input block defines which channels the process is expecting to receive input from. The number of elements in input channels determines the process dependencies and the number of times a process executes.

Process Flow

You can only define one input block at a time and it must contain one or more input declarations.

The input block follows the syntax shown below:

GROOVY

input:
  <input qualifier> <input name>

The input qualifier declares the type of data to be received.

Input qualifiers

• val: Lets you access the received input value by its name as a variable in the process script.
• env: Lets you use the input value to set an environment variable named as the specified input name.
• path: Lets you handle the received value as a file, staging the file properly in the execution context.
• stdin: Lets you forward the received value to the process stdin special file.
• tuple: Lets you handle a group of input values having one of the above qualifiers.
• each: Lets you execute the process for each entry in the input collection. A complete list of inputs can be found here.

Input values

The val qualifier allows you to receive value data as input. It can be accessed in the process script by using the specified input name, as shown in the following example:

GROOVY

//process_input_value.nf


process PRINTCHR {

  input:
  val chr

  script:
  """
  echo processing chromosome $chr
  """
}

chr_ch = Channel.of( 'A' .. 'P' )

workflow {

  PRINTCHR(chr_ch)
}

BASH

$ nextflow run process_input_value.nf -process.debug

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_input_value.nf` [wise_kalman] - revision: 7f90e1bfc5
executor >  local (24)
[b1/88df3f] process > PRINTCHR (16) [100%] 24 of 24 ✔
processing chromosome C
processing chromosome L
processing chromosome A
..truncated...

In the above example the process is executed 16 times; each time a value is received from the queue channel chr_ch it is used to run the process.

Channel order

The channel guarantees that items are delivered in the same order as they have been sent, but since the process is executed in a parallel manner, there is no guarantee that they are processed in the same order as they are received.

Input files

When you need to handle files as input, you need the path qualifier. Using the path qualifier means that Nextflow will stage it in the process execution directory, and it can be accessed in the script by using the name specified in the input declaration.

The input file name can be defined dynamically by defining the input name as a Nextflow variable and referenced in the script using the $variable_name syntax.

For example, in the script below, we assign the variable name read to the input files using the path qualifier. The file is referenced using the variable substitution syntax ${read} in the script block:

GROOVY

//process_input_file.nf


process NUMLINES {
    input:
    path read

    script:
    """
    printf '${read} '
    gunzip -c ${read} | wc -l
    """
}

reads_ch = Channel.fromPath( 'data/yeast/reads/ref*.fq.gz' )

workflow {
  NUMLINES(reads_ch)
}

BASH

$ nextflow run process_input_file.nf -process.debug

OUTPUT

[cd/77af6d] process > NUMLINES (1) [100%] 6 of 6 ✔
ref1_1.fq.gz 58708

ref3_2.fq.gz 52592

ref2_2.fq.gz 81720

ref2_1.fq.gz 81720

ref3_1.fq.gz 52592

ref1_2.fq.gz 58708

Callout

The input name can also be defined as a user-specified filename inside quotes. For example, in the script below, the name of the file is specified as 'sample.fq.gz' in the input definition and can be referenced by that name in the script block.

GROOVY

//process_input_file_02.nf


process NUMLINES {
    input:
    path 'sample.fq.gz'

    script:
    """
    printf 'sample.fq.gz '
    gunzip -c sample.fq.gz | wc -l
    """
}

reads_ch = Channel.fromPath( 'data/yeast/reads/ref*.fq.gz' )

workflow {
  NUMLINES(reads_ch)
}

BASH

$ nextflow run process_input_file_02.nf -process.debug

OUTPUT

[d2/eb0e9d] process > NUMLINES (1) [100%] 6 of 6 ✔
sample.fq.gz 58708

sample.fq.gz 52592

sample.fq.gz 81720

sample.fq.gz 81720

sample.fq.gz 52592

sample.fq.gz 58708

File Objects as inputs

When a process declares an input file, the corresponding channel elements must be file objects, i.e. created with the path helper function from the file specific channel factories, e.g. Channel.fromPath or Channel.fromFilePairs.

Add input channel

For the script process_exercise_input.nf:

1. Define a Channel using fromPath for the transcriptome params.transcriptome.
   
2. Add an input channel that takes the transcriptome channel as a file input.
3. Replace params.transcriptome in the script: block with the input variable you defined in the input: definition.

GROOVY

//process_exercise_input.nf


params.chr = "A"
params.transcriptome = "${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
process CHR_COUNT {

script:
"""
printf  'Number of sequences for chromosome '${params.chr}':'
zgrep  -c '^>Y'${params.chr} ${params.transcriptome}
"""
}

workflow {
CHR_COUNT()
}

Then run your script using

BASH

nextflow run process_exercise_input.nf -process.debug

Show me the solution

GROOVY

 

 params.chr = "A"
 params.transcriptome = "${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

 process CHR_COUNT {
  input:
  path transcriptome

  script:
  """
  printf  'Number of sequences for chromosome '${params.chr}':'
  zgrep  -c '^>Y'${params.chr} ${transcriptome}
  """
 }

 transcriptome_ch = channel.fromPath(params.transcriptome)

 workflow {
  CHR_COUNT(transcriptome_ch)
 }

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `process_exercise_input.nf` [focused_jang] - revision: e32caf0dcb
executor >  local (1)
[00/14ce67] process > CHR_COUNT (1) [100%] 1 of 1 ✔
Number of sequences for chromosome A:118

Combining input channels

A key feature of processes is the ability to handle inputs from multiple channels. However, it’s important to understand how the number of items within the multiple channels affect the execution of a process.

Consider the following example:

GROOVY

//process_combine.nf


process COMBINE {
  input:
  val x
  val y

  script:
  """
  echo $x and $y
  """
}

num_ch = Channel.of(1, 2, 3)
letters_ch = Channel.of('a', 'b', 'c')

workflow {
  COMBINE(num_ch, letters_ch)
}

BASH

$ nextflow run process_combine.nf -process.debug

Both channels contain three elements, therefore the process is executed three times, each time with a different pair:

OUTPUT

2 and b

1 and a

3 and c

What is happening is that the process waits until it receives an input value from all the queue channels declared as input.

When this condition is verified, it uses up the input values coming from the respective queue channels, runs the task. This logic repeats until one or more queue channels have no more content. The process then stops.

What happens when not all channels have the same number of elements?

For example:

GROOVY

//process_combine_02.nf


process COMBINE {
  input:
  val x
  val y

  script:
  """
  echo $x and $y
  """
}

ch_num = Channel.of(1, 2)
ch_letters = Channel.of('a', 'b', 'c', 'd')

workflow {
  COMBINE(ch_num, ch_letters)
}

BASH

$ nextflow run process_combine_02.nf -process.debug

In the above example the process is executed only two times, because when a queue channel has no more data to be processed it stops the process execution.

OUTPUT

2 and b

1 and a

Value channels and process termination

Note however that value channels, Channel.value, do not affect the process termination.

To better understand this behaviour compare the previous example with the following one:

GROOVY

//process_combine_03.nf


process COMBINE {
  input:
  val x
  val y

  script:
  """
  echo $x and $y
  """
}
ch_num = Channel.value(1)
ch_letters = Channel.of('a', 'b', 'c')

workflow {
  COMBINE(ch_num, ch_letters)
}

BASH

$ nextflow run process_combine_03.nf -process.debug

In this example the process is run three times.

OUTPUT

1 and b
1 and a
1 and c

Combining input channels

Write a nextflow script process_exercise_combine.nf that combines two input channels

GROOVY

 transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz')
 chr_ch = channel.of('A')

And include the command below in the script directive

GROOVY

  script:
  """
  zgrep -c ">Y${chr}" ${transcriptome}
  """

Show me the solution

GROOVY

 // process_exercise_combine_answer.nf
 
 process COMBINE {
  input:
  path transcriptome
  val chr

  script:
  """
  zgrep -c ">Y${chr}" ${transcriptome}
  """
 }

 transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz', checkIfExists: true)
 chr_ch = channel.of("A")

 workflow {
   COMBINE(transcriptome_ch, chr_ch)
 }

BASH

$ nextflow run process_exercise_combine.nf -process.debug

OUTPUT

N E X T F L O W   ~  version 24.04.4

Launching `process_exercise_combine.nf` [fabulous_kare] DSL2 - revision: 1eade0a2e9

executor >  local (1)
[e0/b05fe7] COMBINE (1) [100%] 1 of 1 ✔
118

Input repeaters

---

We saw previously that by default the number of times a process runs is defined by the queue channel with the fewest items. However, the each qualifier allows you to repeat the execution of a process for each item in a list or a queue channel, every time new data is received.

For example if we can fix the previous example by using the input qualifer each for the letters queue channel:

GROOVY

//process_repeat.nf


process COMBINE {
  input:
  val x
  each y

  script:
  """
  echo $x and $y
  """
}

ch_num = Channel.of(1, 2)
ch_letters = Channel.of('a', 'b', 'c', 'd')

workflow {
  COMBINE(ch_num, ch_letters)
}

BASH

$ nextflow run process_repeat.nf -process.debug

The process will run eight times.

OUTPUT

2 and d
1 and a
1 and c
2 and b
2 and c
1 and d
1 and b
2 and a

Input repeaters

Extend the script process_exercise_repeat.nf by adding more values to the chr queue channel e.g. A to P and running the process for each value.

GROOVY

//process_exercise_repeat.nf

process COMBINE {
    input:
    path transcriptome
    val chr
   
    script:
    """
     printf "Number of sequences for chromosome $chr: "
    zgrep -c "^>Y${chr}" ${transcriptome}
    """
}

transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz', checkIfExists: true)
chr_ch = channel.of('A')

workflow {
  COMBINE(transcriptome_ch, chr_ch)
}

How many times does this process run?

Show me the solution

GROOVY

 //process_exercise_repeat_answer.nf
 

 process COMBINE {
   input:
   path transcriptome
   each chr
  
   script:
   """
   printf "Number of sequences for chromosome $chr: "
   zgrep -c "^>Y${chr}" ${transcriptome}
   """
 }

 transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz', checkIfExists: true)
 chr_ch = channel.of('A'..'P')

 workflow {
   COMBINE(transcriptome_ch, chr_ch)
 }

Then run the script.

BASH

$ nextflow run process_exercise_repeat.nf -process.debug

This process runs 16 times.

OUTPUT

N E X T F L O W   ~  version 24.04.4

Launching `process_exercise_repeat.nf` [ecstatic_turing] DSL2 - revision: 17891a7528

executor >  local (16)
[65/389033] COMBINE (13) [ 62%] 10 of 16
executor >  local (16)
[6d/f803e5] COMBINE (9)  [100%] 16 of 16 ✔
Number of sequences for chromosome J: 398

Number of sequences for chromosome G: 583

Number of sequences for chromosome O: 597

Number of sequences for chromosome N: 435

Number of sequences for chromosome B: 456

Number of sequences for chromosome E: 323

executor >  local (16)
[6d/f803e5] COMBINE (9)  [100%] 16 of 16 ✔
Number of sequences for chromosome J: 398

Number of sequences for chromosome G: 583

Number of sequences for chromosome O: 597

Number of sequences for chromosome N: 435

Number of sequences for chromosome B: 456

Number of sequences for chromosome E: 323

Number of sequences for chromosome K: 348

Number of sequences for chromosome H: 321

Number of sequences for chromosome C: 186

Number of sequences for chromosome M: 505

Number of sequences for chromosome L: 580

Number of sequences for chromosome A: 118

Number of sequences for chromosome D: 836

Number of sequences for chromosome F: 140

Number of sequences for chromosome P: 513

Number of sequences for chromosome I: 245

Key Points

• A Nextflow process is an independent step in a workflow.
• Processes contain up to five definition blocks including: directives, inputs, outputs, when clause and finally a script block.
• The script block contains the commands you would like to run.
• A process should have a script but the other four blocks are optional.
• Inputs are defined in the input block with a type qualifier and a name.

Content from Processes Part 2

---

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

Estimated time: 40 minutes

Overview

Questions

• How do I get data, files, and values, out of processes?
• How do I handle grouped input and output?
• How can I control when a process is executed?
• How do I control resources, such as number of CPUs and memory, available to processes?
• How do I save output/results from a process?

Objectives

• Define outputs to a process.
• Understand how to handle grouped input and output using the tuple qualifier.
• Understand how to use conditionals to control process execution.
• Use process directives to control execution of a process.
• Use the publishDir directive to save result files to a directory.

Outputs

---

We have seen how to input data into a process; now we will see how to output files and values from a process.

The output declaration block allows us to define the channels used by the process to send out the files and values produced.

An output block is not required, but if it is present it can contain one or more output declarations.

The output block follows the syntax shown below:

GROOVY

output:
  <output qualifier> <output name>
  <output qualifier> <output name>
  ...

Output values

Like the input, the type of output data is defined using type qualifiers.

The val qualifier allows us to output a value defined in the script.

Because Nextflow processes can only communicate through channels, if we want to share a value output of one process as input to another process, we would need to define that value in the output declaration block as shown in the following example:

GROOVY

//process_output_value.nf


params.transcriptome="${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

process COUNT_CHR_SEQS {
  input:
  val chr

  output:
  val chr

  script:
  """
  zgrep -c '^>Y'$chr $params.transcriptome
  """
}
// Both 'Channel' and 'channel' keywords work to generate channels.
// However, it is a good practice to be consistent through the whole pipeline development
chr_ch = channel.of('A'..'P')

workflow {
  COUNT_CHR_SEQS(chr_ch)
  // use the view operator to display contents of the channel
  COUNT_CHR_SEQS.out.view()
}

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `p1.nf` [jovial_lavoisier] - revision: a652ef75d4
executor >  local (16)
executor >  local (16)
[6a/d82669] process > COUNT_CHR_SEQS (16) [100%] 16 of 16 ✔
B
456

A
118

C
186

[..truncated..]

Output files

If we want to capture a file instead of a value as output we can use the path qualifier that can capture one or more files produced by the process, over the specified channel.

GROOVY

//process_output_file.nf


params.transcriptome="${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

process COUNT_CHR_SEQS {
  input:
  val chr

  output:
  path "${chr}_seq_count.txt"

  script:
  """
  zgrep -c '^>Y'$chr $params.transcriptome > ${chr}_seq_count.txt
  """
}
// Both 'Channel' and 'channel' keywords work to generate channels.
// However, it is a good practice to be consistent through the whole pipeline development
chr_ch = channel.of('A'..'P')

workflow {
  COUNT_CHR_SEQS(chr_ch)
  // use the view operator to display contents of the channel
  COUNT_CHR_SEQS.out.view()
}

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `process_output_file.nf` [angry_lichterman] - revision: 6a46c69413
executor >  local (16)
[95/ec5d62] process > COUNT_CHR_SEQS (13) [100%] 16 of 16 ✔
/Users/ggrimes2/Documents/process_wf/work/f2/6d5c44985a15feb0555b7b71c37a9c/J_seq_count.txt
executor >  local (16)
[95/ec5d62] process > COUNT_CHR_SEQS (13) [100%] 16 of 16 ✔
work/f2/6d5c44985a15feb0555b7b71c37a9c/J_seq_count.txt
work/4f/f810942341d003acc80c2603671177/B_seq_count.txt
work/23/883ccf187b5357137a9a87d98717c0/I_seq_count.txt
[..truncated..]

In the above example the process COUNT_CHR_SEQS creates a file named <chr>_seq_count.txt in the work directory containing the number of transcripts within that chromosome.

Since a file parameter using the same name, <chr>_seq_count.txt, is declared in the output block, when the task is completed that file is sent over the output channel.

A downstream operator, such as .view or a process declaring the same channel as input will be able to receive it.

Multiple output files

When an output file name contains a * or ? metacharacter it is interpreted as a pattern match. This allows us to capture multiple files into a list and output them as a one item channel.

For example, here we will capture the files sequence_ids.txt and sequence.txt produced as results from SPLIT_FASTA in the output channel.

GROOVY

//process_output_multiple.nf


params.transcriptome="${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

process SPLIT_FASTA {
  input:
  path transcriptome

  output:
  path "*"

  script:
  """
  zgrep  '^>' $transcriptome > sequence_ids.txt
  zgrep -v '^>' $transcriptome > sequence.txt
  """
}
// Both 'Channel' and 'channel' keywords work to generate channels.
// However, it is a good practice to be consistent through the whole pipeline development
transcriptome_ch = channel.fromPath(params.transcriptome)

workflow {
  SPLIT_FASTA(transcriptome_ch)
  // use the view operator to display contents of the channel
  SPLIT_FASTA.out.view()
}

BASH

$ nextflow run process_output_multiple.nf

OUTPUT

N E X T F L O W  ~  version 21.10.6
Launching `process_output_multiple.nf` [goofy_meitner] - revision: 53cbf7e5a4
executor >  local (1)
[21/01e6ba] process > SPLIT_FASTA (1) [100%] 1 of 1 ✔
[/work/21/01e6baac41d2f37531f86dc7a57034/sequence.txt, work/21/01e6baac41d2f37531f86dc7a57034/sequence_ids.txt]

Note: There are some caveats on glob pattern behaviour:

• Input files are not included in the list of possible matches.
• Glob pattern matches against both files and directories path.
• When a two stars pattern ** is used to recurse through subdirectories, only file paths are matched i.e. directories are not included in the result list.

Output channels

Modify the nextflow script process_exercise_output.nf to include an output block that captures the different output file ${chr}_seqids.txt.

GROOVY

//process_exercise_output.nf


process EXTRACT_IDS {
  input:
  path transcriptome
  each chr

  //add output block here to capture the file "${chr}_seqids.txt"

  script:
  """
  zgrep '^>Y'$chr $transcriptome > ${chr}_seqids.txt
  """
}

transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz')
chr_ch = channel.of('A'..'P')

workflow {
  EXTRACT_IDS(transcriptome_ch, chr_ch)
  EXTRACT_IDS.out.view()
}

Show me the solution

GROOVY

//process_exercise_output_answer.nf


process EXTRACT_IDS {
  input:
  path transcriptome
  each chr

  //add output block here to capture the file "${chr}_seqids.txt"
  output:
  path "${chr}_seqids.txt"

  script:
  """
  zgrep '^>Y'$chr $transcriptome > ${chr}_seqids.txt
  """
}

transcriptome_ch = channel.fromPath('data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz')
chr_ch = channel.of('A'..'P')

workflow {
  EXTRACT_IDS(transcriptome_ch, chr_ch)
  EXTRACT_IDS.out.view()
}

Grouped inputs and outputs

So far we have seen how to declare multiple input and output channels, but each channel was handling only one value at time. However Nextflow can handle groups of values using the tuple qualifiers.

In tuples the first item is the grouping key and the second item is the list.

[group_key,[file1,file2,...]]

When using channel containing a tuple, such a one created with .filesFromPairs factory method, the corresponding input declaration must be declared with a tuple qualifier, followed by definition of each item in the tuple.

GROOVY

//process_tuple_input.nf


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

  script:
  """
  echo $sample_id
  echo $reads
  """
}

reads_ch = Channel.fromFilePairs('data/yeast/reads/ref1_{1,2}.fq.gz')

workflow {
  TUPLEINPUT(reads_ch)
}

outputs

OUTPUT

ref1
ref1_1.fq.gz ref1_2.fq.gz

In the same manner an output channel containing tuple of values can be declared using the tuple qualifier following by the definition of each tuple element in the tuple.

In the code snippet below the output channel would contain a tuple with the grouping key value as the Nextflow variable sample_id and a list containing the files matching the following pattern "${sample_id}.fq.gz".

GROOVY

output:
  tuple val(sample_id), path("${sample_id}.fq.gz")

An example can be seen in this script below.

GROOVY

//process_tuple_io.nf


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

  output:
  tuple val(sample_id), path("${sample_id}.fq.gz")

  script:
  """
  cat $reads > ${sample_id}.fq.gz
  """
}

reads_ch = Channel.fromFilePairs('data/yeast/reads/ref1_{1,2}.fq.gz')

workflow {
  COMBINE_FQ(reads_ch)
  COMBINE_FQ.out.view()
}

BASH

nextflow run process_tuple_io.nf

The output is now a tuple containing the sample id and the combined fastq files.

OUTPUT

[ref1, work/2d/a073d34b5b3231b1f57872599bd308/ref1.fq]

Composite inputs and outputs

Fill in the blank ___ input and output qualifiers for process_exercise_tuple.nf. Note: the output for the COMBINE_REPS process.

GROOVY

//process_exercise_tuple.nf


process COMBINE_REPS {
  input:
  tuple ___(sample_id), ___(reads)

  output:
  tuple ___(sample_id), ___("*.fq.gz")

  script:
  """
  cat *_1.fq.gz > ${sample_id}_R1.fq.gz
  cat *_2.fq.gz > ${sample_id}_R2.fq.gz
  """
}

reads_ch = Channel.fromFilePairs('data/yeast/reads/ref{1,2,3}*.fq.gz',size:-1)

workflow{
  COMBINE_REPS(reads_ch)
  COMBINE_REPS.out.view()
}

Show me the solution

GROOVY

//process_exercise_tuple_answer.nf


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

  output:
  tuple val(sample_id), path("*.fq.gz")

  script:
  """
  cat *_1.fq.gz > ${sample_id}_R1.fq.gz
  cat *_2.fq.gz > ${sample_id}_R2.fq.gz
  """
}

reads_ch = Channel.fromFilePairs('data/yeast/reads/ref*_{1,2,3}.fq.gz',size:-1)

workflow{
  COMBINE_REPS(reads_ch)
  COMBINE_REPS.out.view()
}

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_exercise_tuple.nf` [spontaneous_coulomb] - revision: 06ff22f1a9
executor >  local (3)
[75/f4a44d] process > COMBINE_REPS (3) [100%] 3 of 3 ✔
[ref3, work/99/a7d9176e332fdc0988973dbb89df63/ref3_R1.fq.gz, work/99/a7d9176e332fdc0988973dbb89df63/ref3_R2.fq.gz]
[ref2, /work/53/e3cbd39afa9f0f84a3d9cd060d991a/ref2_R1.fq.gz, /work/53/e3cbd39afa9f0f84a3d9cd060d991a/ref2_R2.fq.gz]
[ref1, work/75/f4a44d0bc761fa4774c2f23a465766/ref1_R1.fq.gz, work/75/f4a44d0bc761fa4774c2f23a465766/ref1_R2.fq.gz]

Conditional execution of a process

---

The when declaration allows you to define a condition that must be verified in order to execute the process. This can be any expression that evaluates a boolean value; true or false.

It is useful to enable/disable the process execution depending on the state of various inputs and parameters.

In the example below the process CONDITIONAL will only execute when the value of the chr variable is less than or equal to 5:

GROOVY

//process_when.nf


process CONDITIONAL {
  input:
  val chr

  when:
  chr <= 5

  script:
  """
  echo $chr
  """
}

chr_ch = channel.of(1..22)

workflow {
  CONDITIONAL(chr_ch)
}

OUTPUT

4

5

2

3

1

Directives

---

Directive declarations allow the definition of optional settings, like the number of cpus and amount of memory, that affect the execution of the current process without affecting the task itself.

They must be entered at the top of the process body, before any other declaration blocks (i.e. input, output, etc).

Note: You do not use = when assigning a value to a directive.

Directives are commonly used to define the amount of computing resources to be used or extra information for configuration or logging purpose.

For example:

GROOVY

//process_directive.nf


process PRINTCHR {
  tag "tagging with chr$chr"
  cpus 1
  echo true

  input:
  val chr

  script:
  """
  echo processing chromosome: $chr
  echo number of cpus $task.cpus
  """
}

chr_ch = channel.of(1..22, 'X', 'Y')

workflow {
  PRINTCHR(chr_ch)
}

OUTPUT

processing chromosome: 1
number of cpus 1

processing chromosome: 2
number of cpus 1

processing chromosome: 6
number of cpus 1
[..truncated..]

The above process uses the three directives, tag, cpus and echo.

The tag directive to allow you to give a custom tag to each process execution. This tag makes it easier to identify a particular task (executed instance of a process) in a log file or in the execution report.

The second directive cpus allows you to define the number of CPUs required for each task.

The third directive echo true prints the stdout to the terminal.

We use the Nextflow task.cpus variable to capture the number of cpus assigned to a task. This is frequently used to specify the number of threads in a multi-threaded command in the script block.

Another commonly used directive is memory specification: memory.

A complete list of directives is available at this link.

Adding directives

Many software tools allow users to configure the number of CPU threads used, optimizing performance for faster and more efficient data processing in high-throughput sequencing tasks.

In this exercise, we will use the bioinformatics tool FastQC to assess the quality of high-throughput sequencing read data. FastQC generates an HTML report along with a directory containing detailed analysis results. We can specify the number of CPU threads for FastQC to use with the -t option, followed by the desired number of threads.

Modify the Nextflow script process_exercise_directives.nf

1. Add a tag directive logging the sample_id in the execution output.
2. Add a cpus directive to specify the number of cpus as 2.
3. Change the fastqc -t option value to $task.cpus in the script directive.

GROOVY

//process_exercise_directives.nf


process FASTQC {
  //add tag directive
  //add cpu directive
 
  input:
  tuple val(sample_id), path(reads)
  
  output:
  tuple val(sample_id), path("fastqc_out")
  
  script:
  """
  mkdir fastqc_out
  fastqc $reads -o fastqc_out -t 1
  """
}

read_pairs_ch = Channel.fromFilePairs('data/yeast/reads/ref*_{1,2}.fq.gz')

workflow {
  FASTQC(read_pairs_ch)
  FASTQC.out.view()
}

solution

GROOVY

//process_directives_answer.nf


process FASTQC {
  tag "$sample_id"
  cpus 2
  
  input:
  tuple val(sample_id), path(reads)
  
  output:
  tuple val(sample_id), path("fastqc_out")
 
  script:
  """
  mkdir fastqc_out
  fastqc $reads -o fastqc_out -t $task.cpus
  """
}

read_pairs_ch = Channel.fromFilePairs('data/yeast/reads/ref*_{1,2}.fq.gz')

workflow {
  FASTQC(read_pairs_ch)
  FASTQC.out.view()
}

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_exercise_directives.nf` [sad_rosalind] - revision: 2ccbfa4937
executor >  local (3)
[90/de1125] process > FASTQC (ref1) [100%] 3 of 3 ✔
[ref2, work/ea/9e6a341b88caf8879e8d18b77049c8/fastqc_out]
[ref3, work/94/d059b816a9ec3d868f2924c26813e7/fastqc_out]
[ref1, work/90/de11251d362f494d6650789d9f8c1d/fastqc_out]

Organising outputs

---

PublishDir directive

Nextflow manages intermediate results from the pipeline’s expected outputs independently.

Files created by a process are stored in a task specific working directory which is considered as temporary. Normally this is under the work directory, which can be deleted upon completion.

The files you want the workflow to return as results need to be defined in the process output block and then the output directory specified using the directive publishDir. More information here.

Note: A common mistake is to specify an output directory in the publishDir directive while forgetting to specify the files you want to include in the output block.

publishDir <directory>, parameter: value, parameter2: value ...

For example if we want to capture the results of the COMBINE_READS process in a results/merged_reads output directory we need to define the files in the output and specify the location of the results directory in the publishDir directive:

GROOVY

//process_publishDir.nf


process COMBINE_READS {
  publishDir "results/merged_reads"

  input:
  tuple val(sample_id), path(reads)

  output:
  path("${sample_id}.merged.fq.gz")

  script:
  """
  cat ${reads} > ${sample_id}.merged.fq.gz
  """
}

reads_ch = Channel.fromFilePairs('data/yeast/reads/ref1_{1,2}.fq.gz')


workflow {
  COMBINE_READS(reads_ch)
}

BASH

$ nextflow run process_publishDir.nf

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_publishDir.nf` [friendly_pauling] - revision: 9b5c315893
executor >  local (1)

[a1/5956bd] process > COMBINE_READS (1) [100%] 1 of 1 ✔

We can use the UNIX command ls -l to examine the contents of the results directory.

BASH

ls -l results/merged_reads/ref1.merged.fq.gz

OUTPUT

results/merged_reads/ref1.merged.fq.gz -> /Users/nf-user/nf-training/work/a1/5956bd9a92f13694b3ada1941f0d2d/ref1.merged.fq.gz

In the above example, the publishDir "results/merged_reads", creates a symbolic link -> to the output files specified by the process merged_reads to the directory path results/merged_reads.

A symbolic link, often referred to as a symlink, is a type of file that serves as a reference or pointer to another file or directory, allowing multiple access paths to the same resource without duplicating its actual data

publishDir

The publishDir output is relative to the path the pipeline run has been launched. Hence, it is a good practice to use implicit variables like projectDir to specify publishDir value.

publishDir parameters

The publishDir directive can take optional parameters, for example the mode parameter can take the value "copy" to specify that you wish to copy the file to output directory rather than just a symbolic link to the files in the working directory. Since the working directory is generally deleted on completion of a pipeline, it is safest to use mode: "copy" for results files. The default mode (symlink) is helpful for checking intermediate files which are not needed in the long term.

GROOVY

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

Full list here.

Manage semantic sub-directories

You can use more than one publishDir to keep different outputs in separate directories. To specify which files to put in which output directory use the parameter pattern with the a glob pattern that selects which files to publish from the overall set of output files.

In the example below we will create an output folder structure in the directory results, which contains a separate sub-directory for sequence id file, pattern:"*_ids.txt" , and a sequence directory, results/sequence" for the sequence.txt file. Remember, we need to specify the files we want to copy as outputs.

GROOVY

//process_publishDir_semantic.nf


params.transcriptome="${projectDir}/data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"

process SPLIT_FASTA {
  publishDir "results/ids", pattern: "*_ids.txt", mode: "copy"
  publishDir "results/sequence", pattern: "sequence.txt", mode: "copy"


  input:
  path transcriptome

  output:
  path "*"

  script:
  """
  zgrep  '^>' $transcriptome > sequence_ids.txt
  zgrep -v '^>' $transcriptome > sequence.txt
  """
}
// Both 'Channel' and 'channel' keywords work to generate channels.
// However, it is a good practice to be consistent through the whole pipeline development
transcriptome_ch = channel.fromPath(params.transcriptome)

workflow {
  SPLIT_FASTA(transcriptome_ch)
  // use the view operator to display contents of the channel
  SPLIT_FASTA.out.view()
}

BASH

$ nextflow run process_publishDir_semantic.nf

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_publishDir_semantic.nf` [golden_poisson] - revision: 421a604840

executor >  local (1)
[be/950786] process > SPLIT_FASTA (1) [100%] 1 of 1 ✔

We can now use the ls results/* command to examine the results directory.

BASH

$ls results/*

OUTPUT

results/ids:
sequence_ids.txt

results/sequence:
sequence.txt

Publishing results

Add a publishDir directive to the nextflow script process_exercise_publishDir.nf that copies the merged reads to the results folder merged_reps.

GROOVY

//process_exercise_publishDir.nf


params.reads= "data/yeast/reads/ref{1,2,3}*{1,2}.fq.gz"

process MERGE_REPS {
 
 input:
 tuple val(sample_id), path(reads)
 
 output:
 path("*fq.gz")

 script:
 """
 cat *1.fq.gz > ${sample_id}.merged.R1.fq.gz
 cat *2.fq.gz > ${sample_id}.merged.R2.fq.gz
 """
}
reads_ch = Channel.fromFilePairs(params.reads,checkIfExists:true,size:6)

workflow {
 MERGE_REPS(reads_ch)
}

Show me the solution

GROOVY

//process_exercise_publishDir_answer.nf


params.reads= "data/yeast/reads/ref{1,2,3}*{1,2}.fq.gz"

process MERGE_REPS {
  publishDir "results/merged_reps"
  input:
  tuple val(sample_id), path(reads)
  output:
  path("*fq.gz")

  script:
  """
  cat *1.fq.gz > ${sample_id}.merged.R1.fq.gz
  cat *2.fq.gz > ${sample_id}.merged.R2.fq.gz
  """
}

reads_ch = Channel.fromFilePairs(params.reads,checkIfExists:true,size:6)

workflow {
  MERGE_REPS(reads_ch)
}

BASH

$ nextflow run process_exercise_publishDir.nf

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `process_exercise_publishDir.nf` [infallible_becquerel] - revision: 4d865241a8

executor >  local (1)
[22/88aa22] process > MERGE_REPS (1) [100%] 1 of 1 ✔

Nextflow Patterns

If you want to find out common structures of Nextflow processes, the Nextflow Patterns page collects some recurrent implementation patterns used in Nextflow applications.

Key Points

• Outputs to a process are defined using the output blocks.
• You can group input and output data from a process using the tuple qualifier.
• The execution of a process can be controlled using the when declaration and conditional statements.
• Files produced within a process and defined as output can be saved to a directory using the publishDir directive.

Content from Workflow

---

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

Estimated time: 40 minutes

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
}

Show me the solution

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 .

Content from Operators

---

Last updated on 2024-04-23 | Edit this page

Estimated time: 40 minutes

Overview

Questions

• How do I perform operations, such as filtering, on channels?
• What are the different kinds of operations I can perform on channels?
• How do I combine operations?
• How can I use a CSV file to process data into a Channel?

Objectives

• Understand what Nextflow operators are.
• Modify the contents/elements of a channel using operators.
• Perform filtering and combining operations on a channel object.
• Use the splitCsv operator to parse the contents of CSV file into a channel .

Operators

---

In the Channels episode we learnt how to create Nextflow channels to enable us to pass data and values around our workflow. If we want to modify the contents or behaviour of a channel, Nextflow provides methods called operators. We have previously used the view operator to view the contents of a channel. There are many more operator methods that can be applied to Nextflow channels that can be usefully separated into several groups:

• Filtering operators: reduce the number of elements in a channel.
• Transforming operators: transform the value/data in a channel.
• Splitting operators: split items in a channel into smaller chunks.
• Combining operators: join channels together.
• Maths operators: apply simple math functions on channels.
• Other: such as the view operator.

In this episode you will see examples, and get to use different types of operators.

Using Operators

---

To use an operator, the syntax is the channel name, followed by a dot . , followed by the operator name and brackets ().

GROOVY

channel_obj.<operator>()

view

The view operator prints the items emitted by a channel to the console appending a new line character to each item in the channel.

GROOVY

ch = channel.of('1', '2', '3')
ch.view()

We can also chain together the channel factory method .of and the operator .view() using the dot notation.

GROOVY

ch = channel.of('1', '2', '3').view()

To make code more readable we can split the operators over several lines. The blank space between the operators is ignored and is solely for readability.

GROOVY

ch = channel
      .of('1', '2', '3')
      .view()

prints:

GROOVY

1
2
3

Closures

An optional closure {} parameter can be specified to customise how items are printed.

Briefly, a closure is a block of code that can be passed as an argument to a function. In this way you can define a chunk of code and then pass it around as if it were a string or an integer. By default the parameters for a closure are specified with the groovy keyword $it (‘it’ is for ‘item’).

For example here we use the the view operator and apply a closure to it, to add a chr prefix to each element of the channel using string interpolation.

GROOVY

ch = channel
  .of('1', '2', '3')
  .view({ "chr$it" })

It prints:

OUTPUT

chr1
chr2
chr3

Note: the view() operator doesn’t change the contents of the channel object.

GROOVY

ch = channel
  .of('1', '2', '3')
  .view({ "chr$it" })

ch.view()  

OUTPUT

chr1
chr2
chr3
1
2
3

Filtering operators

We can reduce the number of items in a channel by using filtering operators.

The filter operator allows you to get only the items emitted by a channel that satisfy a condition and discard all the others. The filtering condition can be specified by using either:

• a regular expression
• a literal value
• a data type qualifier, e.g. Number (any integer,float …), String, Boolean
• or any boolean statement.

Data type qualifier

Here we use the filter operator on the chr_ch channel specifying the data type qualifier Number so that only numeric items are returned. The Number data type includes both integers and floating point numbers. We will then use the view operator to print the contents.

GROOVY

chr_ch = channel.of( 1..22, 'X', 'Y' )
autosomes_ch =chr_ch.filter( Number )
autosomes_ch.view()

To simplify the code we can chain multiple operators together, such as filter and view using a . .

The previous example could be rewritten like: The blank space between the operators is ignored and is used for readability.

GROOVY

chr_ch = channel
  .of( 1..22, 'X', 'Y' )
  .filter( Number )
  .view()

OUTPUT

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

Regular expression

To filter by a regular expression you have to do is to put ~ right in front of the string literal regular expression (e.g. ~"(^[Nn]extflow)" or use slashy strings which replace the quotes with /. ~/^[Nn]extflow/).

The following example shows how to filter a channel by using a regular expression ~/^1.*/ inside a slashy string, that returns only strings that begin with 1:

GROOVY

chr_ch = channel
  .of( 1..22, 'X', 'Y' )
  .filter(~/^1.*/)
  .view()

OUTPUT

1
10
11
12
13
14
15
16
17
18
19

Boolean statement

A filtering condition can be defined by using a Boolean expression described by a closure {} and returning a boolean value. For example the following fragment shows how to combine a filter for a type qualifier Number with another filter operator using a Boolean expression to emit numbers less than 5:

GROOVY

channel
  .of( 1..22, 'X', 'Y' )
  .filter(Number)
  .filter { it < 5 }
  .view()

OUTPUT

1
2
3
4

Closures

In the above example we have removed the brackets around the filter condition e.g. filter{ it<5}, since it specifies a closure as the operator’s argument. This is language short for filter({ it<5})

Literal value

Finally, if we only want to include elements of a specific value we can specify a literal value. In the example below we use the literal value X to filter the channel for only those elements containing the value X.

channel
  .of( 1..22, 'X', 'Y' )
  .filter('X')
  .view()

OUTPUT

X

Filter a channel

Add two channel filters to the Nextflow script below to view only the even numbered chromosomes.

Note: The expression it % 2 produces the remainder of a division.

GROOVY

chr_ch = channel
 .of( 1..22, 'X', 'Y' )
 .view()

Show me the solution

GROOVY

chr_ch = channel
  .of( 1..22, 'X', 'Y' )
  .filter( Number )
  .filter({ it % 2 == 0 })
  .view()

OUTPUT

2
4
6
8
10
12
14
16
18
20
22

Modifying the contents of a channel

If we want to modify the items in a channel, we can use transforming operators.

map

Applying a function to items in a channel

The map operator applies a function of your choosing to every item in a channel, and returns the items so obtained as a new channel. The function applied is called the mapping function and is expressed with a closure {} as shown in the example below:

GROOVY

chr = channel
  .of( 'chr1', 'chr2' )
  .map ({ it.replaceAll("chr","") })

chr.view()

Here the map function uses the groovy string function replaceAll to remove the chr prefix from each element.

OUTPUT

1
2

We can also use the map operator to transform each element into a tuple.

In the example below we use the map operator to transform a channel containing fastq files to a new channel containing a tuple with the fastq file and the number of reads in the fastq file. We use the built in countFastq file method to count the number of records in a FASTQ formatted file.

We can change the default name of the closure parameter keyword from it to a more meaningful name file using ->. When we have multiple parameters we can specify the keywords at the start of the closure, e.g. file, numreads ->.

GROOVY

fq_ch = channel
    .fromPath( 'data/yeast/reads/*.fq.gz' )
    .map ({ file -> [file, file.countFastq()] })
    .view ({ file, numreads -> "file $file contains $numreads reads" })

This would produce.

OUTPUT

file data/yeast/reads/ref1_2.fq.gz contains 14677 reads
file data/yeast/reads/etoh60_3_2.fq.gz contains 26254 reads
file data/yeast/reads/temp33_1_2.fq.gz contains 20593 reads
file data/yeast/reads/temp33_2_1.fq.gz contains 15779 reads
file data/yeast/reads/ref2_1.fq.gz contains 20430 reads
[..truncated..]

We can then add a filter operator to only retain those fastq files with more than 25000 reads.

GROOVY

channel
    .fromPath( 'data/yeast/reads/*.fq.gz' )
    .map ({ file -> [file, file.countFastq()] })
    .filter({ file, numreads -> numreads > 25000})
    .view ({ file, numreads -> "file $file contains $numreads reads" })

OUTPUT

file data/yeast/reads/etoh60_3_2.fq.gz contains 26254 reads
file data/yeast/reads/etoh60_3_1.fq.gz contains 26254 reads

map operator

Add a map operator to the Nextflow script below to transform the contents into a tuple with the file and the file’s name, using the .getName method. The getName method gives the filename. Finally view the channel contents.

GROOVY

 channel
 .fromPath( 'data/yeast/reads/*.fq.gz' )
 .view()

Show me the solution

GROOVY

ch = channel
  .fromPath( 'data/yeast/reads/*.fq.gz' )
  .map ({file -> [ file, file.getName() ]})
  .view({file, name -> "file's name: $name"})

Converting a list into multiple items

The flatten operator transforms a channel in such a way that every item in a list or tuple is flattened so that each single entry is emitted as a sole element by the resulting channel.

GROOVY

list1 = [1,2,3]
ch = channel
  .of(list1)
  .view()

OUTPUT

[1, 2, 3]

GROOVY

ch =channel
    .of(list1)
    .flatten()
    .view()

The above snippet prints:

OUTPUT

1
2
3

This is similar to the channel factory Channel.fromList.

Converting the contents of a channel to a single list item.

The reverse of the flatten operator is collect. The collect operator collects all the items emitted by a channel to a list and return the resulting object as a sole emission. This can be extremely useful when combining the results from the output of multiple processes, or a single process run multiple times.

GROOVY

ch = channel
    .of( 1, 2, 3, 4 )
    .collect()
    .view()

It prints a single value:

OUTPUT

[1,2,3,4]

The result of the collect operator is a value channel and can be used multiple times.

Grouping contents of a channel by a key.

The groupTuple operator collects tuples or lists of values by grouping together the channel elements that share the same key. Finally it emits a new tuple object for each distinct key collected.

For example.

GROOVY

ch = channel
     .of( ['wt','wt_1.fq'], ['wt','wt_2.fq'], ["mut",'mut_1.fq'], ['mut', 'mut_2.fq'] )
     .groupTuple()
     .view()

OUTPUT

[wt, [wt_1.fq, wt_1.fq]]
[mut, [mut_1.fq, mut_2.fq]]

If we know the number of items to be grouped we can use the groupTuple size parameter. When the specified size is reached, the tuple is emitted. By default incomplete tuples (i.e. with less than size grouped items) are discarded (default).

For example.

GROOVY

ch = channel
     .of( ['wt','wt_1.fq'], ['wt','wt_1.fq'], ["mut",'mut_1.fq'])
     .groupTuple(size:2)
     .view()

outputs,

OUTPUT

[wt, [wt_1.fq, wt_1.fq]]

This operator is useful to process altogether all elements for which there’s a common property or a grouping key.

Group Tuple

GROOVY

channel.fromPath('data/yeast/reads/*.fq.gz')
       .view()

Modify the Nextflow script above to add the map operator to create a tuple with the name prefix as the key and the file as the value using the closure below.

GROOVY

{ file -> [ file.getName().split('_')[0], file ] }

Finally group together all files having the same common prefix using the groupTuple operator and view the contents of the channel.

Show me the solution

GROOVY

ch = channel.fromPath('data/yeast/reads/*.fq.gz')
    .map { file -> [ file.getName().split('_')[0], file ] }
    .groupTuple()
    .view()

Merging Channels

Combining operators allows you to merge channels together. This can be useful when you want to combine the output channels from multiple processes to perform another task such as joint QC.

mix

The mix operator combines the items emitted by two (or more) channels into a single channel.

GROOVY

ch1 = channel.of( 1,2,3 )
ch2 = channel.of( 'X','Y' )
ch3 = channel.of( 'mt' )

ch4 = ch1.mix(ch2,ch3).view()

OUTPUT

1
2
3
X
Y
mt

The items emitted by the resulting mixed channel may appear in any order, regardless of which source channel they came from. Thus, the following example it could be a possible result of the above example as well.

OUTPUT

1
2
X
3
mt
Y

join

The join operator creates a channel that joins together the items emitted by two channels for which exists a matching key. The key is defined, by default, as the first element in each item emitted.

GROOVY

reads1_ch = channel
  .of(['wt', 'wt_1.fq'], ['mut','mut_1.fq'])
reads2_ch= channel
  .of(['wt', 'wt_2.fq'], ['mut','mut_2.fq'])
reads_ch = reads1_ch
  .join(reads2_ch)
  .view()

The resulting channel emits:

OUTPUT

[wt, wt_1.fq, wt_2.fq]
[mut, mut_1.fq, mut_2.fq]

Maths operators

The maths operators allows you to apply simple math function on channels.

The maths operators are:

• count
• min
• max
• sum
• toInteger

Counting items in a channel

The count operator creates a channel that emits a single item: a number that represents the total number of items emitted by the source channel. For example:

GROOVY

ch = channel
    .of(1..22,'X','Y')
    .count()
    .view()

OUTPUT

24

Splitting items in a channel

Sometimes you want to split the content of a individual item in a channel, like a file or string, into smaller chunks that can be processed by downstream operators or processes e.g. items stored in a CSV file.

Nextflow has a number of splitting operators that can achieve this:

• splitCsv: The splitCsv operator allows you to parse text items emitted by a channel, that are formatted using the CSV format, and split them into records or group them into list of records with a specified length.
• splitFasta: The splitFasta operator allows you to split the entries emitted by a channel, that are formatted using the FASTA format. It returns a channel which emits a text item for each sequence in the received FASTA content.
• splitFastq: The splitFastq operator allows you to split the entries emitted by a channel, that are formatted using the FASTQ format. It returns a channel which emits a text chunk for each sequence in the received item.
• splitText: The splitText operator allows you to split multi-line strings or text file items, emitted by a source channel into chunks containing n lines, which will be emitted by the resulting channel.

splitCsv

The splitCsv operator allows you to parse text items emitted by a channel, that are formatted using the CSV format, and split them into records or group them into list of records with a specified length. This is useful when you want to use a sample sheet.

In the simplest case just apply the splitCsv operator to a channel emitting a CSV formatted text files or text entries. For example:

For the CSV file samples.csv.

BASH

cat data/yeast/samples.csv

OUTPUT

sample_id,fastq_1,fastq_2
ref1,data/yeast/reads/ref1_1.fq.gz,data/yeast/reads/ref1_2.fq.gz
ref2,data/yeast/reads/ref2_1.fq.gz,data/yeast/reads/ref2_2.fq.gz

We can use the splitCsv() operator to split the channel contaning a CSV file into three elements.

GROOVY

csv_ch=channel
    .fromPath('data/yeast/samples.csv')
    .splitCsv()
csv_ch.view()

OUTPUT

[sample_id, fastq_1, fastq_2]
[ref1, data/yeast/reads/ref1_1.fq.gz, data/yeast/reads/ref1_2.fq.gz]
[ref2, data/yeast/reads/ref2_1.fq.gz, data/yeast/reads/ref2_2.fq.gz]

The above example shows hows the CSV file samples.csv is parsed and is split into three elements.

Accessing values

Values can be accessed by their positional indexes using the square brackets syntax[index]. So to access the first column you would use [0] as shown in the following example:

GROOVY

csv_ch=channel
    .fromPath('data/yeast/samples.csv')
    .splitCsv()
csv_ch
  .view({it[0]})

OUTPUT

sample_id
ref1
ref2

Column headers

When the CSV begins with a header line defining the column names, you can specify the parameter header: true which allows you to reference each value by its name, as shown in the following example:

GROOVY

csv_ch=channel
    .fromPath('data/yeast/samples.csv')
    .splitCsv(header:true)
csv_ch.view({it.fastq_1})

OUTPUT

data/yeast/reads/ref1_1.fq.gz
data/yeast/reads/ref2_1.fq.gz

Parse a CSV file

Modify the Nextflow script to print the first column sample_id.

GROOVY

csv_ch=channel
  .fromPath('data/yeast/samples.csv')

Show me the solution

GROOVY

 csv_ch=channel
        .fromPath('data/yeast/samples.csv')
        .splitCsv(header:true)

csv_ch.view({it.sample_id})

Tab delimited files

If you want to split a tab delimited file or file separated by another character use the sep parameter of the split splitCsv operator.

For examples,

GROOVY

Channel.of("val1\tval2\tval3\nval4\tval5\tval6\n")
  .splitCsv(sep: "\t")
  .view()

OUTPUT

[val1, val2, val3]
[val4, val5, val6]

More resources

See the operators documentation on the Nextflow web site.

Key Points

• Nextflow operators are methods that allow you to modify, set or view channels.
• Operators can be separated in to several groups; filtering , transforming , splitting , combining , forking and Maths operators
• To use an operator use the dot notation after the Channel object e.g. my_ch.view().
• You can parse text items emitted by a channel, that are formatted using the CSV format, using the splitCsv operator.

Content from Reporting

---

Last updated on 2024-04-23 | Edit this page

Estimated time: 25 minutes

Overview

Questions

• How do I get information about my pipeline run?
• How can I see what commands I ran?
• How can I create a report from my run?

Objectives

• View Nextflow pipeline run logs.
• Use nextflow log to view more information about a specific run.
• Create an HTML report from a pipeline run.

Nextflow log

---

Once a script has run, Nextflow stores a log of all the workflows executed in the current folder. Similar to an electronic lab book, this means you have a record of all processing steps and commands run.

You can print Nextflow’s execution history and log information using the nextflow log command.

BASH

$ nextflow log

OUTPUT

TIMESTAMP          	DURATION	RUN NAME               	STATUS	REVISION ID	SESSION ID                          	COMMAND

This will print a summary of the executions log and runtime information for all pipelines run. By default, included in the summary, are the date and time it ran, how long it ran for, the run name, run status, a revision ID, the session id and the command run on the command line.

Show Execution Log

Listing the execution logs of previous invocations of all pipelines in a directory.

BASH

$ nextflow log

Show me the solution

The output will look similar to this:

OUTPUT

TIMESTAMP          	DURATION	RUN NAME       	STATUS	REVISION ID	SESSION ID                          	COMMAND
2021-03-19 13:45:53	6.5s    	fervent_babbage	OK    	c54a707593 	15487395-443a-4835-9198-229f6ad7a7fd	nextflow run wc.nf
2021-03-19 13:46:53	6.6s    	soggy_miescher 	OK    	c54a707593 	58da0ccf-63f9-42e4-ba4b-1c349348ece5	nextflow run wc.nf --samples 'data/yeast/reads/*.fq.gz'

Pipeline execution report

---

If we want to get more information about an individual run we can add the run name or session ID to the log command.

For example:

BASH

$ nextflow log tiny_fermat

BASH

/data/.../work/7b/3753ff13b1fa5348d2d9b6f512153a
/data/.../work/c1/56a36d8f498c99ac6cba31e85b3e0c
/data/.../work/f7/659c65ef60582d9713252bcfbcc310
/data/.../work/82/ba67e3175bd9e6479d4310e5a92f99
/data/.../work/e5/2816b9d4e7b402bfdd6597c2c2403d
/data/.../work/3b/3485d00b0115f89e4c202eacf82eba

This will list the work directory for each process.

Task ID

The task ID is a a 32 hexadecimal digit,e.g. 3b3485d00b0115f89e4c202eacf82eba. A task’s unique ID is generated as a 128-bit hash number obtained from a composition of the task’s:

• Inputs values
• Input files
• Command line string
• Container ID
• Conda environment
• Environment modules
• Any executed scripts in the bin directory

Fields

---

If we want to print more metadata we can use the log command and the option -f (fields) followed by a comma delimited list of fields. This can be composed to track the provenance of a workflow result.

For example:

BASH

$ nextflow log tiny_fermat -f 'process,exit,hash,duration'

Will output the process name, exit status, hash and duration of the process for the tiny_fermat run to the terminal.

OUTPUT

index	0	7b/3753ff	2s
fastqc	0	c1/56a36d	9.3s
fastqc	0	f7/659c65	9.1s
quant	0	82/ba67e3	2.7s
quant	0	e5/2816b9	3.2s
multiqc	0	3b/3485d0	6.3s

The complete list of available fields can be retrieved with the command:

BASH

$ nextflow log -l

OUTPUT

attempt
complete
container
cpus
disk
duration
env
error_action
exit
hash
inv_ctxt
log
memory
module
name
native_id
pcpu
peak_rss
peak_vmem
pmem
process
queue
rchar
read_bytes
realtime
rss
scratch
script
start
status
stderr
stdout
submit
syscr
syscw
tag
task_id
time
vmem
vol_ctxt
wchar
workdir
write_bytes

Script

If we want a log of all the commands executed in the pipeline we can use the script field. It is important to note that the resultant output can not be used to run the pipeline steps.

Filtering

The output from the log command can be very long. We can subset the output using the option -F (filter) specifying the filtering criteria. This will print only those tasks matching a pattern using the syntax =~/<pattern>/.

For example to filter for process with the name fastqc we would run:

BASH

$ nextflow log tiny_fermat -F 'process =~ /fastqc/'

OUTPUT

/data/.../work/c1/56a36d8f498c99ac6cba31e85b3e0c
/data/.../work/f7/659c65ef60582d9713252bcfbcc310

This can be useful to locate specific tasks work directories.

View run log

Use the Nextflow log command specifying a run name and the fields. name, hash, process and status

Show me the solution

Example solution using run name elegant_descartes.

BASH

$ nextflow log elegant_descartes -f name,hash,process,status

Filter pipeline run log

Use the -F option and a regular expression to filter the for a specific process e.g. multiqc.

Show me the solution

BASH

$ nextflow log elegant_descartes -f name,hash,process,status -F 'process =~ /multiqc/'

Templates

---

The -t option allows a template (string or file) to be specified. This makes it possible to create a custom report in any text based format.

For example you could save this markdown snippet to a file e.g. my-template.md:

MARKDOWN

## $name

script:

    $script

exist status: $exit
task status: $status
task folder: $workdir

Then, the following log command will output a markdown file containing the script, exit status and folder of all executed tasks:

BASH

$ nextflow log elegant_descartes -t my-template.md > execution-report.md

Or, the template file can also be written in HTML.

For example:

HTML

<div>
<h2>${name}</h2>
<div>
Script:
<pre>${script}</pre>
</div>

<ul>
    <li>Exit: ${exit}</li>
    <li>Status: ${status}</li>
    <li>Work dir: ${workdir}</li>
    <li>Container: ${container}</li>
</ul>
</div>

By saving the above snippet in a file named template.html, you can run the following command:

BASH

$ nextflow log elegant_descartes -t template.html > provenance.html

To view the report open it in a browser.

Generate an HTML run report

Generate an HTML report for a run using the -t option and the template.html file.

Show me the solution

BASH

$ nextflow log elegant_descartes -t template.html > provenance.html

Key Points

• Nextflow can produce a custom execution report with run information using the log command.
• You can generate a report using the -t option specifying a template file.

Content from Nextflow configuration

---

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

Estimated time: 45 minutes

Overview

Questions

• What is the difference between the workflow implementation and the workflow configuration?
• How do I configure a Nextflow workflow?
• How do I assign different resources to different processes?
• How do I separate and provide configuration for different computational systems?
• How do I change configuration settings from the default settings provided by the workflow?

Objectives

• Understand the difference between workflow implementation and configuration.
• Understand the difference between configuring Nextflow and a Nextflow script.
• Create a Nextflow configuration file.
• Understand what a configuration scope is.
• Be able to assign resources to a process.
• Be able to refine configuration settings using process selectors.
• Be able to group configurations into profiles for use with different computer infrastructures.
• Be able to override existing settings.
• Be able to inspect configuration settings before running a workflow.

Nextflow configuration

---

A key Nextflow feature is the ability to decouple the workflow implementation, which describes the flow of data and operations to perform on that data, from the configuration settings required by the underlying execution platform. This enables the workflow to be portable, allowing it to run on different computational platforms such as an institutional HPC or cloud infrastructure, without needing to modify the workflow implementation.

We have seen earlier that it is possible to provide a process with directives. These directives are process specific configuration settings. Similarly, we have also provided parameters to our workflow which are parameter configuration settings. These configuration settings can be separated from the workflow implementation, into a configuration file.

Configuration files

---

Settings in a configuration file are sets of name-value pairs (name = value). The name is a specific property to set, while the value can be anything you can assign to a variable (see nextflow scripting), for example, strings, booleans, or other variables. It is also possible to access any variable defined in the host environment such as $PATH, $HOME, $PWD, etc.

GROOVY

// nextflow.config
my_home_dir = "$HOME"

Accessing variables in your configuration file

Generally, variables and functions defined in a configuration file are not accessible from the workflow script. Only variables defined using the params scope and the env scope (without env prefix) can be accessed from the workflow script.

GROOVY

workflow {
    MY_PROCESS( params.input )
}

Settings are also partitioned into scopes, which govern the behaviour of different elements of the workflow. For example, workflow parameters are governed from the params scope, while process directives are governed from the process scope. A full list of the available scopes can be found in the documentation. It is also possible to define your own scope.

Configuration settings for a workflow are often stored in the file nextflow.config which is in the same directory as the workflow script. Configuration can be written in either of two ways. The first is using dot notation, and the second is using brace notation. Both forms of notation can be used in the same configuration file.

An example of dot notation:

GROOVY

params.input = ''             // The workflow parameter "input" is assigned an empty string to use as a default value
params.outdir = './results'   // The workflow parameter "outdir" is assigned the value './results' to use by default.

An example of brace notation:

GROOVY

params {
    input  = ''
    outdir = './results'
}

Configuration files can also be separated into multiple files and included into another using the includeConfig statement.

GROOVY

// nextflow.config
params {
    input  = ''
    outdir = './results'
}

includeConfig 'system_resources.config'

GROOVY

// system_resources.config
process {
    cpus = 1    // default cpu usage
    time = '1h' // default time limit
}

How configuration files are combined

---

Configuration settings can be spread across several files. This also allows settings to be overridden by other configuration files. The priority of a setting is determined by the following order, ranked from highest to lowest.

1. Parameters specified on the command line (--param_name value).
2. Parameters provided using the -params-file option.
3. Config file specified using the -c my_config option.
4. The config file named nextflow.config in the current directory.
5. The config file named nextflow.config in the workflow project directory ($projectDir: the directory where the script to be run is located).
6. The config file $HOME/.nextflow/config.
7. Values defined within the workflow script itself (e.g., main.nf).

If configuration is provided by more than one of these methods, configuration is merged giving higher priority to configuration provided higher in the list.

Existing configuration can be completely ignored by using -C <custom.config> to use only configuration provided in the custom.config file.

Configuring Nextflow vs Configuring a Nextflow workflow

Parameters starting with a single dash - (e.g., -c my_config.config) are configuration options for nextflow, while parameters starting with a double dash -- (e.g., --outdir) are workflow parameters defined in the params scope.

The majority of Nextflow configuration settings must be provided on the command-line, however a handful of settings can also be provided within a configuration file, such as workdir = '/path/to/work/dir' (-w /path/to/work/dir) or resume = true (-resume), and do not belong to a configuration scope.

Determine script output

Determine the outcome of the following script executions. Given the script print_message.nf:

GROOVY

nextflow.enable.dsl = 2

params.message = 'hello'

workflow {
    PRINT_MESSAGE(params.message)
}

process PRINT_MESSAGE {
    echo true

    input:
    val my_message

    script:
    """
    echo $my_message
    """
}

and configuration (print_message.config):

params.message = 'Are you tired?'

What is the outcome of the following commands?

1. nextflow run print_message.nf
2. nextflow run print_message.nf --message '¿Que tal?'
3. nextflow run print_message.nf -c print_message.config
4. nextflow run print_message.nf -c print_message.config --message '¿Que tal?'

Show me the solution

1. ‘hello’ - Workflow script uses the value in print_message.nf
2. ‘¿Que tal?’ - The command-line parameter overrides the script setting.
3. ‘Are you tired?’ - The configuration overrides the script setting
4. ‘¿Que tal?’ - The command-line parameter overrides both the script and configuration settings.

Configuring process behaviour

---

Earlier we saw that process directives allow the specification of settings for the task execution such as cpus, memory, conda and other resources in the pipeline script. This is useful when prototyping a small workflow script, however this ties the configuration to the workflow, making it less portable. A good practice is to separate the process configuration settings into another file.

The process configuration scope allows the setting of any process directives in the Nextflow configuration file.

For example:

GROOVY

// nextflow.config
process {
    cpus = 2
    memory = 8.GB
    time = '1 hour'
    publishDir = [ path: params.outdir, mode: 'copy' ]
}

Unit values

Memory and time duration units can be specified either using a string based notation in which the digit(s) and the unit can be separated by a space character, or by using the numeric notation in which the digit(s) and the unit are separated by a dot character and not enclosed by quote characters.

String syntax	Numeric syntax	Value
‘10 KB’	10.KB	10240 bytes
‘500 MB’	500.MB	524288000 bytes
‘1 min’	1.min	60 seconds
‘1 hour 25 sec’	-	1 hour and 25 seconds

These settings are applied to all processes in the workflow. A process selector can be used to apply the configuration to a specific process or group of processes.

Process selectors

The resources for a specific process can be defined using withName: followed by the process name e.g., 'FASTQC', and the directives within curly braces. For example, we can specify different cpus and memory resources for the processes INDEX and FASTQC as follows:

GROOVY

// process_resources.config
process {
    withName: INDEX {
        cpus = 4
        memory = 8.GB
    }
    withName: FASTQC {
        cpus = 2
        memory = 4.GB
    }
}

When a workflow has many processes, it is inconvenient to specify directives for all processes individually, especially if directives are repeated for groups of processes. A helpful strategy is to annotate the processes using the label directive (processes can have multiple labels). The withLabel selector then allows the configuration of all processes annotated with a specific label, as shown below:

GROOVY

// configuration_process_labels.nf


process P1 {

    label "big_mem"

    script:
    """
    echo P1: Using $task.cpus cpus and $task.memory memory.
    """
}

process P2 {

    label "big_mem"

    script:
    """
    echo P2: Using $task.cpus cpus and $task.memory memory.
    """
}

workflow {

    P1()
    P2()

}

GROOVY

// configuration_process-labels.config
process {
    withLabel: big_mem {
        cpus = 16
        memory = 64.GB
    }
}

Another strategy is to use process selector expressions. Both withName: and withLabel: allow the use of regular expressions to apply the same configuration to all processes matching a pattern. Regular expressions must be quoted, unlike simple process names or labels.

• The | matches either-or, e.g., withName: 'INDEX|FASTQC' applies the configuration to any process matching the name INDEX or FASTQC.
• The ! inverts a selector, e.g., withLabel: '!small_mem' applies the configuration to any process without the small_mem label.
• The .* matches any number of characters, e.g., withName: 'NFCORE_RNASEQ:RNA_SEQ:BAM_SORT:.*' matches all processes of the workflow NFCORE_RNASEQ:RNA_SEQ:BAM_SORT.

A regular expression cheat-sheet can be found here if you would like to write more expressive expressions.

Selector priority

When mixing generic process configuration and selectors, the following priority rules are applied (from highest to lowest):

1. withName selector definition.
2. withLabel selector definition.
3. Process specific directive defined in the workflow script.
4. Process generic process configuration.

Process selectors

Create a Nextflow config, process-selector.config, specifying different cpus and memory resources for the two processes P1 (cpus 1 and memory 2.GB) and P2 (cpus 2 and memory 1.GB), where P1 and P2 are defined as follows:

GROOVY

// process-selector.nf


process P1 {
    echo true

    script:
    """
    echo P1: Using $task.cpus cpus and $task.memory memory.
    """
}

process P2 {
    echo true

    script:
    """
    echo P2: Using $task.cpus cpus and $task.memory memory.
    """
}

workflow {
   P1()
   P2()
}

Show me the solution

GROOVY

// process-selector.config
process {
    withName: P1 {
        cpus = 1
        memory = 2.GB
    }
    withName: P2 {
        cpus = 2
        memory = 1.GB
    }
}

BASH

$ nextflow run process-selector.nf -c process-selector.config -process.debug

OUTPUT

N E X T F L O W  ~  version 21.04.0

Launching `process-selector.nf` [clever_borg] -
revision: e765b9e62d
executor >  local (2)
[de/86cef0] process > P1 [100%] 1 of 1 ✔
[bf/8b332e] process > P2 [100%] 1 of 1 ✔
P2: Using 2 cpus and 1 GB memory.

P1: Using 1 cpus and 2 GB memory.

Dynamic expressions

A common scenario is that configuration settings may depend on the data being processed. Such settings can be dynamically expressed using a closure. For example, we can specify the memory required as a multiple of the number of cpus. Similarly, we can publish results to a subfolder based on the sample name.

GROOVY

process FASTQC {

    input:
    tuple val(sample), path(reads)

    script:
    """
    fastqc -t $task.cpus $reads
    """
}

GROOVY

// nextflow.config
process {
    withName: FASTQC {
        cpus = 2
        memory = { 2.GB * task.cpus }
        publishDir = { "fastqc/$sample" }
    }
}

Configuring execution platforms

---

Nextflow supports a wide range of execution platforms, from running locally, to running on HPC clusters or cloud infrastructures. See https://www.nextflow.io/docs/latest/executor.html for the full list of supported executors.

A diagram illustrating the different executors available in Nextflow. The diagram shows a configuration file feeding into Nextflow, which has both local and grid executors. The local executor connects to the Local OS and a standalone computer, while the grid executor connects to a batch scheduler and NFS, which further connects to various computing resources such as UNIVA, SLURM, Platform Computing, PBS Works, Kubernetes, and Amazon Web Services.

The default executor configuration is defined within the executor scope (https://www.nextflow.io/docs/latest/config.html#scope-executor). For example, in the config below we specify the executor as Sun Grid Engine, sge and the number of tasks the executor will handle in a parallel manner (queueSize) to 10.

GROOVY

// nextflow.config
executor {
    name = 'sge'
    queueSize = 10
}

The process.executor directive allows you to override the executor to be used by a specific process. This can be useful, for example, when there are short running tasks that can be run locally, and are unsuitable for submission to HPC executors (check for guidelines on best practice use of your execution system). Other process directives such as process.clusterOptions, process.queue, and process.machineType can be also be used to further configure processes depending on the executor used.

GROOVY

//nextflow.config
executor {
    name = 'sge'
    queueSize = 10
}
process {
    withLabel: 'short' {
        executor = 'local'
    }
}

Configuring software requirements

---

An important feature of Nextflow is the ability to manage software using different technologies. It supports the Conda package management system, and container engines such as Docker, Singularity, Podman, Charliecloud, and Shifter. These technologies allow one to package tools and their dependencies into a software environment such that the tools will always work as long as the environment can be loaded. This facilitates portable and reproducible workflows. Software environment specification is managed from the process scope, allowing the use of process selectors to manage which processes load which software environment. Each technology also has its own scope to provide further technology specific configuration settings.

Software configuration using Conda

Conda is a software package and environment management system that runs on Linux, Windows, and Mac OS. Software packages are bundled into Conda environments along with their dependencies for a particular operating system (Not all software is supported on all operating systems). Software packages are tied to conda channels, for example, bioinformatic software packages are found and installed from the BioConda channel.

A Conda environment can be configured in several ways:

• Provide a path to an existing Conda environment.
• Provide a path to a Conda environment specification file (written in YAML).
• Specify the software package(s) using the <channel>::<package_name>=<version> syntax (separated by spaces), which then builds the Conda environment when the process is run.

GROOVY

process {
    conda = "/home/user/miniconda3/envs/my_conda_env"
    withName: FASTQC {
        conda = "environment.yml"
    }
    withName: SALMON {
        conda = "bioconda::salmon=1.5.2"
    }
}

There is an optional conda scope which allows you to control the creation of a Conda environment by the Conda package manager. For example, conda.cacheDir specifies the path where the Conda environments are stored. By default this is in conda folder of the work directory.

Instructor Note

This following exercise necessitates the installation of conda, a package and environment management software, on your system for downloading and installing the FASTP software. If conda is not installed, then this exercise should be omitted.

Define a software requirement in the configuration file using conda

We are going to define a software requirement using the “conda” directive.

The software we are going to uses is fastp, a tool used for fast processing of next-generation sequencing data (like RNA or DNA sequences).

Each time the process is called we are going to run fastp -A -i ${read} -o out.fq 2>&1

• fastp is the main command that invokes the fastp program.
• -A: option tells fastp to automatically detect and trim adapters. Adapters are short, artificially added sequences used in sequencing that are not part of the target DNA or RNA. -i ${read}:
• The -i flag specifies the input file. ${read} represents a nextflow variable that contains the path to the sequencing reads.
• -o: The -o flag specifies the output file. In this case, the processed sequencing data will be written to a file named out.fq.
• 2>&1: This is a shell redirection command, it means that both the regular output and error messages will be sent to the console.

1. Create a config file for the Nextflow script configuration_fastp.nf.

2. Add a conda directive for the process name FASTP that includes the bioconda package fastp, version 0.12.4-0.

Hint You can specify the conda packages using the syntax <channel>::<package_name>=<version> e.g. bioconda::salmon=1.5.2

3. Run the Nextflow script configure_fastp.nf with the configuration file using the -c option.

GROOVY

// configuration_fastp.nf
nextflow.enable.dsl = 2

params.input = "data/yeast/reads/ref1_1.fq.gz"

workflow {
    FASTP( Channel.fromPath( params.input ) ).view()
}

process FASTP {

   input:
   path read

   output:
   stdout

   script:
   """
   fastp -A -i ${read} -o out.fq 2>&1
   """
}

Show me the solution

GROOVY

// fastp.config
process {
    withName: 'FASTP' {
        conda = "bioconda::fastp=0.12.4-0"
    }
}

BASH

nextflow run configure_fastp.nf -c fastp.config

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `configuration_fastp.nf` [berserk_jepsen] - revision: 28fadd2486
executor >  local (1)
[c1/c207d5] process > FASTP (1) [100%] 1 of 1 ✔
Creating Conda env: bioconda::fastp=0.12.4-0 [cache /home/training/work/conda/env-a7a3a0d820eb46bc41ebf4f72d955e5f]
ref1_1.fq.gz 58708
Read1 before filtering:
total reads: 14677
total bases: 1482377

Q20 bases: 1466210(98.9094%)
Q30 bases: 1415997(95.5221%)

Read1 after filtering:
total reads: 14671
total bases: 1481771
Q20 bases: 1465900(98.9289%)
Q30 bases: 1415769(95.5457%)

Filtering result:
reads passed filter: 14671
reads failed due to low quality: 6
reads failed due to too many N: 0
reads failed due to too short: 0

JSON report: fastp.json
HTML report: fastp.html

Software configuration using Docker

Docker is a container technology. Container images are lightweight, standalone, executable package of software that includes everything needed to run an application: code, runtime, system tools, system libraries and settings. Containerized software is intended to run the same regardless of the underlying infrastructure, unlike other package management technologies which are operating system dependant (See the published article on Nextflow). For each container image used, Nextflow uses Docker to spawn an independent and isolated container instance for each process task.

To use Docker, we must provide a container image path using the process.container directive, and also enable docker in the docker scope, docker.enabled = true. A container image path takes the form (protocol://)registry/repository/image:version--build. By default, Docker containers run software using a privileged user. This can cause issues, and so it is also a good idea to supply your user and group via the docker.runOptions.

The Docker run option -u $(id -u):$(id -g) is used to specify the user and group IDs (UID and GID) that the container should use when running. This option ensures that the processes inside the container run with the same UID and GID as the user executing the Docker command on the host system.

GROOVY

process.container = 'quay.io/biocontainers/salmon:1.5.2--h84f40af_0'
docker.enabled = true
docker.runOptions = '-u $(id -u):$(id -g)'

Software configuration using Singularity

Singularity is another container technology, commonly used on HPC clusters. It is different to Docker in several ways. The primary differences are that processes are run as the user, and certain directories are automatically “mounted” (made available) in the container instance. Singularity also supports building Singularity images from Docker images, allowing Docker image paths to be used as values for process.container.

Singularity is enabled in a similar manner to Docker. A container image path must be provided using process.container and singularity enabled using singularity.enabled = true.

GROOVY

process.container = 'https://depot.galaxyproject.org/singularity/salmon:1.5.2--h84f40af_0'
singularity.enabled = true

Container protocols

The following protocols are supported:

• docker://: download the container image from the Docker Hub and convert it to the Singularity format (default).
• library://: download the container image from the Singularity Library service.
• shub://: download the container image from the Singularity Hub.
• docker-daemon://: pull the container image from a local Docker installation and convert it to a Singularity image file.
• https://: download the singularity image from the given URL.
• file://: use a singularity image on local computer storage.

Configuration profiles

---

One of the most powerful features of Nextflow configuration is to predefine multiple configurations or profiles for different execution platforms. This allows a group of predefined settings to be called with a short invocation, -profile <profile name>.

Configuration profiles are defined in the profiles scope, which group the attributes that belong to the same profile using a common prefix.

GROOVY

//configuration_profiles.config
profiles {

    standard {
        params.genome = '/local/path/ref.fasta'
        process.executor = 'local'
    }

    cluster {
        params.genome = '/data/stared/ref.fasta'
        process.executor = 'sge'
        process.queue = 'long'
        process.memory = '10GB'
        process.conda = '/some/path/env.yml'
    }

    cloud {
        params.genome = '/data/stared/ref.fasta'
        process.executor = 'awsbatch'
        process.container = 'cbcrg/imagex'
        docker.enabled = true
    }

}

This configuration defines three different profiles: standard, cluster and cloud that set different process configuration strategies depending on the target execution platform. By convention the standard profile is implicitly used when no other profile is specified by the user. To enable a specific profile use -profile option followed by the profile name:

BASH

nextflow run <your script> -profile cluster

Configuration order

Settings from profiles will override general settings in the configuration file. However, it is also important to remember that configuration is evaluated in the order it is read in. For example, in the following example, the publishDir directive will always take the value ‘results’ even when the profile hpc is used. This is because the setting is evaluated before Nextflow knows about the hpc profile. If the publishDir directive is moved to after the profiles scope, then publishDir will use the correct value of params.results.

GROOVY

params.results = 'results'
process.publishDir = params.results
profiles {
    hpc {
        params.results = '/long/term/storage/results'
    }
}

Key Points

• Nextflow configuration can be managed using a Nextflow configuration file.
• Nextflow configuration files are plain text files containing a set of properties.
• You can define process specific settings, such as cpus and memory, within the process scope.
• You can assign different resources to different processes using the process selectors withName or withLabel.
• You can define a profile for different configurations using the profiles scope. These profiles can be selected when launching a pipeline execution by using the -profile command-line option
• Nextflow configuration settings are evaluated in the order they are read-in.

Content from Workflow caching and checkpointing

---

Last updated on 2024-08-21 | Edit this page

Estimated time: 30 minutes

Overview

Questions

• How can I restart a Nextflow workflow after an error?
• How can I add new data to a workflow without starting from the beginning?
• Where can I find intermediate data and results?

Objectives

• Resume a Nextflow workflow using the -resume option.
• Restart a Nextflow workflow using new data.

A key feature of workflow management systems, like Nextflow, is re-entrancy, which is the ability to restart a pipeline after an error from the last successfully executed process. Re-entrancy enables time consuming successfully completed steps, such as index creation, to be skipped when adding more data to a pipeline. This in turn leads to faster prototyping and development of workflows, and faster analyses of additional data. Nextflow achieves re-entrancy by automatically keeping track of all the processes executed in your pipeline via caching and checkpointing.

Resume

---

To restart from the last successfully executed process we add the command line option -resume to the Nextflow command.

For example, the command below would resume the word_count.nf script from the last successful process.

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/ref1*.fq.gz' -resume

We can see in the output that the results from the process NUM_LINES has been retrieved from the cache.

OUTPUT

Launching `word_count.nf` [condescending_dalembert] - revision: fede04a544
[c9/2597d5] process > NUM_LINES (1) [100%] 2 of 2, cached: 2 ✔
ref1_1.fq.gz 58708

ref1_2.fq.gz 58708

Resume a pipeline

Resume the Nextflow script word_count.nf by re-running the command and adding the parameter -resume and the parameter --input 'data/yeast/reads/temp33*':

Show me the solution

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/temp33*' -resume

If your previous run was successful the output will look similar to this:

OUTPUT

N E X T F L O W  ~  version 20.10.0
Launching `word_count.nf` [nauseous_leavitt] - revision: fede04a544
[21/6116de] process > NUM_LINES (4) [100%] 6 of 6, cached: 6 ✔
temp33_3_2.fq.gz 88956

temp33_3_1.fq.gz 88956

temp33_1_1.fq.gz 82372

temp33_2_2.fq.gz 63116

temp33_1_2.fq.gz 82372

temp33_2_1.fq.gz 63116

You will see that the execution of the process NUMLINES is actually skipped (cached text appears), and the results are retrieved from the cache.

How does resume work?

---

Nextflow stores all intermediate files and task results during the execution of a workflow is work directory. It acts as a scratch space where all the temporary data required for the workflow’s execution is kept. Within the work directory, Nextflow creates subdirectories named with unique hashes (e.g., work/ab/cd1234…). Each of these subdirectories corresponds to a specific process or task in the pipeline. The hashed directory names ensure that each task’s outputs are isolated and uniquely identified.

The mechanism works by assigning a unique ID to each task. This unique ID is used to create a separate execution directory, within the work directory, where the tasks are executed and the results stored. A task’s unique ID is generated as a 128-bit hash number obtained from a composition of the task’s:

• Inputs values
• Input files
• Command line string
• Container ID
• Conda environment
• Environment modules
• Any executed scripts in the bin directory

When we resume a workflow Nextflow uses this unique ID to check if:

1. The working directory exists
2. It contains a valid command exit status
3. It contains the expected output files.

If these conditions are satisfied, the task execution is skipped and the previously computed outputs are applied. When a task requires recomputation, ie. the conditions above are not fulfilled, the downstream tasks are automatically invalidated.

Therefore, if you modify some parts of your script, or alter the input data using -resume, will only execute the processes that are actually changed.

The execution of the processes that are not changed will be skipped and the cached result used instead.

This helps a lot when testing or modifying part of your pipeline without having to re-execute it from scratch.

Modify Nextflow script and re-run.

Alter the timestamp on the file temp33_3_2.fq.gz using the UNIX touch command.

BASH

$ touch data/yeast/reads/temp33_3_2.fq.gz

Run command below.

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/temp33*' -resume

How many processes will be cached and how many will run ?

Show me the solution

The output will look similar to this:

OUTPUT

N E X T F L O W  ~  version 20.10.0
Launching `word_count.nf` [gigantic_minsky] - revision: fede04a544
executor >  local (1)
[20/cda0d5] process > NUM_LINES (5) [100%] 6 of 6, cached: 5 ✔
temp33_1_2.fq.gz 82372

temp33_3_1.fq.gz 88956

temp33_2_1.fq.gz 63116

temp33_1_1.fq.gz 82372

temp33_2_2.fq.gz 63116

temp33_3_2.fq.gz 88956

As you changed the timestamp on one file it will only re-run that process. The results for the other 5 processes are retrieved from the cache.

The Work directory

---

By default the pipeline results are cached in the directory work where the pipeline is launched.

We can use the Bash tree command to list the contents of the work directory. Note: By default tree does not print hidden files (those beginning with a dot .). Use the -a to view all files.

BASH

$ tree -a work

Example output

OUTPUT

work/
├── 12
│   └── 5489f3c7dbd521c0e43f43b4c1f352
│       ├── .command.begin
│       ├── .command.err
│       ├── .command.log
│       ├── .command.out
│       ├── .command.run
│       ├── .command.sh
│       ├── .exitcode
│       └── temp33_1_2.fq.gz -> /home/training/data/yeast/reads/temp33_1_2.fq.gz
├── 3b
│   └── a3fb24ad3242e4cc8e5aa0c24d174b
│       ├── .command.begin
│       ├── .command.err
│       ├── .command.log
│       ├── .command.out
│       ├── .command.run
│       ├── .command.sh
│       ├── .exitcode
│       └── temp33_2_1.fq.gz -> /home/training/data/yeast/reads/temp33_2_1.fq.gz
├── 4c
│   └── 125b5e5a5ee144fa25dd9bccd467e9
│       ├── .command.begin
│       ├── .command.err
│       ├── .command.log
│       ├── .command.out
│       ├── .command.run
│       ├── .command.sh
│       ├── .exitcode
│       └── temp33_3_1.fq.gz -> /home/training/data/yeast/reads/temp33_3_1.fq.gz
├── 54
│   └── eb9d72e9ac24af8183de569ab0b977
│       ├── .command.begin
│       ├── .command.err
│       ├── .command.log
│       ├── .command.out
│       ├── .command.run
│       ├── .command.sh
│       ├── .exitcode
│       └── temp33_2_2.fq.gz -> /home/training/data/yeast/reads/temp33_2_2.fq.gz
├── e9
│   └── 31f28c291481342cc45d4e176a200a
│       ├── .command.begin
│       ├── .command.err
│       ├── .command.log
│       ├── .command.out
│       ├── .command.run
│       ├── .command.sh
│       ├── .exitcode
│       └── temp33_1_1.fq.gz -> /home/training/data/yeast/reads/temp33_1_1.fq.gz
└── fa
    └── cd3e49b63eadd6248aa357083763c1
        ├── .command.begin
        ├── .command.err
        ├── .command.log
        ├── .command.out
        ├── .command.run
        ├── .command.sh
        ├── .exitcode
        └── temp33_3_2.fq.gz -> /home/training/data/yeast/reads/temp33_3_2.fq.gz

Task execution directory

Within the work directory there are multiple task execution directories. There is one directory for each time a process is executed. These task directories are identified by the process execution hash. For example the task directory fa/cd3e49b63eadd6248aa357083763c1 would be location for the process identified by the hash fa/cd3e49 .

The task execution directory contains:

• .command.sh: The command script. The .command.sh file includes the specific instructions you’ve written to process your data or perform computations.

• .command.run: A Bash script generated by Nextflow to manage the execution environment of the .command.sh script. This script acts as a wrapper around .command.sh. It performs several tasks like setting up the task’s environment variables, handling the task’s pre and post execution (like moving inputs and outputs to correct locations, logging start and end times, handling errors, and ensuring resource limits are respected

• .command.out: The complete job standard output.

• .command.err: The complete job standard error.

• .command.log: The wrapper execution output.

• .command.begin: A file created as soon as the job is launched.

• .exitcode: A file containing the task exit code. This file is used to capture and store the exit status of the process that was run by the .command.sh script.

• Any task input files (symlinks)

• Any task output files

Specifying another work directory

Depending on your script, this work folder can take a lot of disk space. You can specify another work directory using the command line option -w. Note Using a different work directory will mean that any jobs will need to re-run from the beginning.

BASH

$ nextflow run word_count.nf --input 'data/yeast/reads/temp33*' -w second_work_dir -resume

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `word_count.nf` [deadly_easley] - revision: fede04a544
executor >  local (6)
[9d/0f5e89] process > NUM_LINES (5) [100%] 6 of 6 ✔
temp33_3_2.fq.gz 88956

temp33_1_1.fq.gz 82372

temp33_3_1.fq.gz 88956

temp33_1_2.fq.gz 82372

temp33_2_2.fq.gz 63116

temp33_2_1.fq.gz 63116

Clean the work directory

If you are sure you won’t resume your pipeline execution, clean this folder periodically using the command nextflow clean.

BASH

$ nextflow clean [run_name|session_id] [options]

Supply the option -n to print names of files to be removed without deleting them, or -f to force the removal of the files. If you only want to remove files from a run but retain execution log entries and metadata, add the option -k. Multiple runs can be cleaned with the options, -before, -after or -but before the run name. For example, the command below would remove all the temporary files and log entries for runs before the run gigantic_minsky.

BASH

$ nextflow clean -f -before gigantic_minsky

Remove a Nextflow run.

Remove the last Nextflow run using the command nextflow clean. First use the option -dry-run to see which files would be deleted and then re-run removing the run and associated files.

Show me the solution

An example nextflow clean command with dry-run .

BASH

$ nextflow clean nauseous_leavitt -dry-run

An example nextflow clean command removing the files.

BASH

$ nextflow clean nauseous_leavitt -f

Key Points

• Nextflow automatically keeps track of all the processes executed in your pipeline via checkpointing.
• Nextflow caches intermediate data in task directories within the work directory.
• Nextflow caching and checkpointing allows re-entrancy into a workflow after a pipeline error or using new data, skipping steps that have been successfully executed.
• Re-entrancy is enabled using the -resume option.

Content from Simple RNA-Seq pipeline

---

Last updated on 2024-09-27 | Edit this page

Estimated time: 60 minutes

Overview

Questions

• How can I create a Nextflow pipeline from a series of unix commands and input data?
• How do I log my pipelines parameters?
• How can I manage my pipeline software requirements?
• How do I know when my pipeline has finished?
• How do I see how much resources my pipeline has used?

Objectives

• Create a simple RNA-Seq pipeline.
• Use the log.info function to print all the pipeline parameters.
• Print a confirmation message when the pipeline completes.
• Use a conda environment.yml file to install the pipeline’s software requirement.
• Produce an execution report and generate run metrics from a pipeline run.

We’re now set to develop a multi-step pipeline using Nextflow, for analyzing and performing quality control on our yeast RNA-Seq experiment.

In this RNA-Seq pipeline, we’ll undertake the following steps to thoroughly analyze gene expression data:

1. Quality Control with FastQC: FastQC assesses the quality of the data by generating reports that highlight any potential issues, such as low-quality sequences or contamination. FastQC’s output includes an HTML report and a directory containing detailed analyses, which are essential for evaluating the integrity of the sequencing data.

BASH

$ mkdir fastqc_<sample_id>_logs
$ fastqc -o fastqc_<sample_id>_logs -f fastq -q <reads>

2. Transcriptome Indexing with Salmon: Salmon is a tool to quantify transcript expression in RNA-seq data. The first step in the process is for Salmon to create an index of the transcriptome. This step involves processing a reference transcriptome, which allows for efficient and accurate mapping and quantification of RNA-Seq reads.

BASH

$ salmon index --threads <cpus> -t <transcriptome file> -i index

3. Quantification with Salmon: After indexing, Salmon is used for the quantification step. In this step, Salmon maps the reads to the transcriptome index and quantifies the transcript abundances. This process is crucial for determining the expression levels of genes in the sample.

BASH

$ salmon quant --threads <cpus> --libType=U -i <index> -1 <read1> -2 <read2> -o <pair_id>

4. Aggregating Reports with MultiQC: Finally, the pipeline employs MultiQC to aggregate logs and outputs from the FastQC and Salmon. MultiQC scans the outputs and compiles a summary report, which provides an overview of the results and highlights any areas that may need further investigation.

BASH

$ multiqc .

This pipeline provides an overview of RNA-Seq data, from quality control to expression quantification, culminating in an aggregated report for easy interpretation of the results.

To start move the episode’s nextflow scripts in the scripts/rnaseq_pipeline folder to your home directory.

BASH

$ cp scripts/rnaseq_pipeline/* .

This folder contains files we will be modifying in this episode.

Define the pipeline parameters

---

The first thing we want to do when writing a pipeline is define the pipeline parameters. The script script1.nf defines the pipeline input parameters.

GROOVY

//script1.nf
params.reads = "data/yeast/reads/*_{1,2}.fq.gz"
params.transcriptome = "data/yeast/transcriptome/*.fa.gz"


println "reads: $params.reads"

Run it by using the following command:

BASH

$ nextflow run script1.nf

We can specify a different input parameter using the --<params> option, for example :

GROOVY

$ nextflow run script1.nf --reads "data/yeast/reads/ref1*_{1,2}.fq.gz"

OUTPUT

reads: data/yeast/reads/ref1*_{1,2}.fq.gz

Add a parameter

Modify the script1.nf adding a third parameter named outdir and set it to results. This parameter will be used as the pipeline output directory.

Show me the solution

GROOVY

params.outdir = "results"

It can be useful to print the pipeline parameters to the screen. This can be done using the the log.info command and a multiline string statement. The string method .stripIndent() command is used to remove the indentation on multi-line strings. log.info also saves the output to the log execution file .nextflow.log.

GROOVY

log.info """\
         transcriptome: ${params.transcriptome}
         """
         .stripIndent()

log.info

Modify the script1.nf to print all the pipeline parameters by using a single log.info command and a multiline string statement. See an example here.

BASH

$ nextflow run script1.nf

Look at the output log .nextflow.log.

Show me the solution

Below is an example log.info command printing all the pipeline parameters.

GROOVY

log.info """\
        R N A S E Q - N F   P I P E L I N E    
        ===================================
        transcriptome: ${params.transcriptome}
        reads        : ${params.reads}
        outdir       : ${params.outdir}
        """
        .stripIndent()

BASH

$ less .nextflow.log

Recap

In this step you have learned:

• How to define parameters in your pipeline script.

• How to pass parameters by using the command line.

• The use of $var and ${var} variable placeholders.

• How to use multiline strings.

• How to use log.info to print information and save it in the log execution file.

Create a transcriptome index file

---

Nextflow allows the execution of any command or user script by using a process definition.

For example,

BASH

$ salmon index --threads $task.cpus -t $transcriptome -i index

A process is defined by providing three main declarations:

1. The process inputs,
2. The process outputs
3. Finally the command script.

The second example, script2.nf adds,

1. The process INDEX which generate a directory with the index of the transcriptome. This process takes one input, a transcriptome file, and emits one output a salmon index directory.
2. A queue Channel transcriptome_ch taking the transcriptome file defined in params variable params.transcriptome.
3. Finally the script adds a workflow definition block which calls the INDEX process.

GROOVY

//script2.nf


/*
 * pipeline input parameters
 */
params.reads = "data/yeast/reads/*_{1,2}.fq.gz"
params.transcriptome = "data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
params.outdir = "results"

println """\
         R N A S E Q - N F   P I P E L I N E
         ===================================
         transcriptome: ${params.transcriptome}
         reads        : ${params.reads}
         outdir       : ${params.outdir}
         """
         .stripIndent()


/*
 * define the `INDEX` process that create a binary index
 * given the transcriptome file
 */
process INDEX {

    input:
    path transcriptome

    output:
    path 'index'

    script:
    """
    salmon index --threads $task.cpus -t $transcriptome -i index
    """
}

transcriptome_ch = channel.fromPath(params.transcriptome)

workflow {
   INDEX()
}

Try to run it by using the command:

BASH

$ nextflow run script2.nf

OUTPUT

N E X T F L O W  ~  version 22.04.0
Launching `script2.nf` [happy_brown] DSL2 - revision: 90e932bb8d
R N A S E Q - N F   P I P E L I N E
===================================
transcriptome: data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
reads        : data/yeast/reads/*_{1,2}.fq.gz
outdir       : results

Process `INDEX` declares 1 input channel but 0 were specified

 -- Check script 'script2.nf' at line: 41 or see '.nextflow.log' file for more details

The execution will fail because the program the process, INDEX , has not been passed any input channel.

Add the transcriptome_ch channel to the INDEX process call.

GROOVY

[truncated]
workflow {
   INDEX(transcriptome_ch)
}

Now try to run it again by using the command:

BASH

$ nextflow run script2.nf

Now the workflow will run successfully.

OUTPUT

N E X T F L O W  ~  version 22.04.0
Launching `script2.nf` [mad_aryabhata] DSL2 - revision: 811396b67b
R N A S E Q - N F   P I P E L I N E
===================================
transcriptome:data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
reads        : data/yeast/reads/*_{1,2}.fq.gz
outdir       : results

executor >  local (1)
[c0/418d78] process > INDEX (1) [100%] 1 of 1 ✔

The INDEX process also defines one output channel. This channel will be populated with index directory created during process. To view the contents of the channel we can use the view operator.

View the contents of the index_ch

1. Assign the output of the INDEX process to the variable index_ch.
2. View the contents of the index_ch channel by using the view operator.

Show me the solution

GROOVY

[..truncated..]
workflow {
  index_ch=INDEX(transcriptome_ch)
  index_ch.view()
}

Recap

In this step you have learned:

• How to define a process executing a custom command

• How process inputs are declared

• How to assign a channel as input to a process call

• How process outputs are declared

• How to print the content of a channel view()

Collect read files by pairs

---

This step shows how to match read files into pairs, so they can be mapped by salmon.

The script script3.nf adds a line to create a channel, read_pairs_ch, containing fastq read pair files using the fromFilePairs channel factory.

GROOVY

//script3.nf
nextflow.enable.dsl = 2

/*
 * pipeline input parameters
 */
params.reads = "data/yeast/reads/ref1_{1,2}.fq.gz"
params.transcriptome = "data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz"
params.outdir = "results"

log.info """\
         R N A S E Q - N F   P I P E L I N E
         ===================================
         transcriptome: ${params.transcriptome}
         reads        : ${params.reads}
         outdir       : ${params.outdir}
         """
         .stripIndent()


read_pairs_ch = Channel.fromFilePairs( params.reads )

We can view the contents of the read_pairs_ch by adding the following statement as the last line:

GROOVY

read_pairs_ch.view()

Now if we execute it with the following command:

BASH

$ nextflow run script3.nf

It will print an output similar to the one shown below that shows how the read_pairs_ch channel emits a tuple. The tuple is composed of two elements, where the first is the pattern matched by the glob pattern data/yeast/reads/ref1_{1,2}.fq.g, defined by the variable params.reads , and the second is a list representing the actual files.

OUTPUT

[..truncated..]
[ref1, [data/yeast/reads/ref1_1.fq.gz,data/yeast/reads/ref1_2.fq.gz]]

To read in other read pairs we can specify a different glob pattern in the params.reads variable by using --reads options on the command line. For example, the following command would read in add the ref samples:

BASH

$ nextflow run script3.nf --reads 'data/yeast/reads/ref*_{1,2}.fq.gz'

OUTPUT

[..truncated..]
[ref2, [data/yeast/reads/ref2_1.fq.gz, data/yeast/reads/ref2_2.fq.gz]]
[ref3, [data/yeast/reads/ref3_1.fq.gz, data/yeast/reads/ref3_2.fq.gz]]
[ref1, [data/yeast/reads/ref1_1.fq.gz, data/yeast/reads/ref1_2.fq.gz]]

Note File paths including one or more wildcards ie. *, ?, etc. MUST be wrapped in single-quoted characters to avoid Bash expanding the glob pattern on the command line.

We can also add a argument, checkIfExists: true , to the fromFilePairs channel factory to return an message if the file doesn’t exist.

GROOVY

//script3.nf
[..truncated..]
read_pairs_ch = Channel.fromFilePairs( params.reads, checkIfExists: true )

If we now run the script with the --reads parameter data/yeast/reads/*_1,2}.fq.gz

BASH

$ nextflow run script3.nf --reads 'data/yeast/reads/*_1,2}.fq.gz'

it will return the message .

OUTPUT

[..truncated..]
No such file: data/yeast/reads/*_1,2}.fq.gz

Read in all read pairs

1. Add the checkIfExists: true argument to the fromFilePairs channel factory in script3.nf.
2. Using the command line parameter --reads, add a glob pattern to read in all the read pairs files from the data/yeast/reads directory.

Show me the solution

GROOVY

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

BASH

nextflow run script3.nf --reads 'data/yeast/reads/*_{1,2}.fq.gz'

OUTPUT

[..truncated..]
[temp33_1, [data/yeast/reads/temp33_1_1.fq.gz, data/yeast/reads/temp33_1_2.fq.gz]]
[ref2, [data/yeast/reads/ref2_1.fq.gz, data/yeast/reads/ref2_2.fq.gz]]
[temp33_3, [data/yeast/reads/temp33_3_1.fq.gz, data/yeast/reads/temp33_3_2.fq.gz]]
[ref3, [data/yeast/reads/ref3_1.fq.gz, data/yeast/reads/ref3_2.fq.gz]]
[temp33_2, [data/yeast/reads/temp33_2_1.fq.gz,data/yeast/reads/temp33_2_2.fq.gz]]
[etoh60_2, [data/yeast/reads/etoh60_2_1.fq.gz,data/yeast/reads/etoh60_2_2.fq.gz]]
[ref1, [data/yeast/reads/ref1_1.fq.gz, data/yeast/reads/ref1_2.fq.gz]]
[etoh60_3, [data/yeast/reads/etoh60_3_1.fq.gz, data/yeast/reads/etoh60_3_2.fq.gz]]
[etoh60_1, [data/yeast/reads/etoh60_1_1.fq.gz, data/yeast/reads/etoh60_1_2.fq.gz]]

Recap

In this step you have learned:

• How to use fromFilePairs to handle read pair files

• How to use the checkIfExists option to check input file existence

Perform expression quantification

---

The script script4.nf;

1. Adds the quantification process, QUANT.
2. Calls the QUANT process in the workflow block.

GROOVY

//script4.nf
..truncated..
/*
 * Run Salmon to perform the quantification of expression using
 * the index and the matched read files
 */
process QUANT {

    input:
    each index
    tuple val(pair_id), path(reads)

    output:
    path(pair_id)

    script:
    """
    salmon quant --threads $task.cpus --libType=U -i $index -1 ${reads[0]} -2 ${reads[1]} -o $pair_id
    """
}
..truncated..
workflow {
  read_pairs_ch = Channel.fromFilePairs( params.reads, checkIfExists:true )
  transcriptome_ch = Channel.fromPath( params.transcriptome, checkIfExists:true )

  index_ch=INDEX(transcriptome_ch)
  quant_ch=QUANT(index_ch,read_pairs_ch)
}

The index_ch channel, declared as output in the INDEX process, is used as the first input argument to the QUANT process.

The second input argument of the QUANT process, the read_pairs_ch channel, is a tuple composed of two elements: the pair_id and the reads.

Execute it by using the following command:

BASH

$ nextflow run script4.nf

You will see the execution of the index and quantification process.

Re run the command using the -resume option

BASH

$ nextflow run script4.nf -resume

The -resume option cause the execution of any step that has been already processed to be skipped.

Try to execute it with more read files as shown below:

BASH

$ nextflow run script4.nf -resume --reads 'data/yeast/reads/ref*_{1,2}.fq.gz'

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `script4.nf` [shrivelled_brenner] - revision: c21df6839e
R N A S E Q - N F   P I P E L I N E
===================================
transcriptome: data/yeast/transcriptome/Saccharomyces_c
erevisiae.R64-1-1.cdna.all.fa.gz

reads        : data/yeast/reads/ref*_{1,2}.fq.gz
outdir       : results

executor >  local (8)
[02/3742cf] process > INDEX     [100%] 1 of 1, cached: 1 ✔
[9a/be3483] process > QUANT (9) [100%] 3 of 3, cached: 1 ✔

You will notice that the INDEX step and one of the QUANT steps has been cached, and the quantification process is executed more than one time.

When your input channel contains multiple data items Nextflow, where possible, parallelises the execution of your pipeline.

In these situations it is useful to add a tag directive to add some descriptive text to instance of the process being run.

Add a tag directive

Add a tag directive to the QUANT process of script4.nf to provide a more readable execution log.

Show me the solution

GROOVY

tag "quantification on $pair_id"

Data produced by the workflow during a process will be saved in the working directory, by default a directory named work. The working directory should be considered a temporary storage space and any data you wish to save at the end of the workflow should be specified in the process output with the final storage location defined in the publishDir directive.

Note: by default the publishDir directive creates a symbolic link to the files in the working this behaviour can be changed using the mode parameter.

Add a publishDir directive

---

Add a publishDir directive to the quantification process of script4.nf to store the process results into folder specified by the params.outdir Nextflow variable. Include the publishDir mode option to copy the output.

Challenge

Show me the solution

GROOVY

publishDir "${params.outdir}/quant", mode:'copy'

Recap

In this step you have learned:

• How to connect two processes by using the channel declarations.

• How to resume the script execution skipping already already computed steps.

• How to use the tag directive to provide a more readable execution output.

• How to use the publishDir to store a process results in a path of your choice.

Quality control

---

This step implements a quality control step for your input reads. The input to the FASTQC process is the same read_pairs_ch that is provided as input to the quantification process QUANT .

GROOVY

//script5.nf
[..truncated..]

/*
 * Run fastQC to check quality of reads files
 */
process FASTQC {
    tag "FASTQC on $sample_id"
    cpus 1

    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} -t ${task.cpus}
    """
}

[..truncated..]

workflow {
  read_pairs_ch = Channel.fromFilePairs( params.reads, checkIfExists:true )
  transcriptome_ch = Channel.fromPath( params.transcriptome, checkIfExists:true )

  index_ch=INDEX(transcriptome_ch)
  quant_ch=QUANT(index_ch,read_pairs_ch)
}

Run the script script5.nf by using the following command:

BASH

$ nextflow run script5.nf -resume

The FASTQC process will not run as the process has not been declared in the workflow scope.

Add FASTQC process

Add the FASTQC process to the workflow scope of script5.nf adding the read_pairs_ch channel as an input. Run the nextflow script using the -resume option.

BASH

$ nextflow run script5.nf -resume

Show me the solution

GROOVY

workflow {
read_pairs_ch = Channel.fromFilePairs( params.reads, checkIfExists:true )
transcriptome_ch = Channel.fromPath( params.transcriptome, checkIfExists:true )

index_ch = INDEX( transcriptome_ch )
quant_ch=QUANT(index_ch,read_pairs_ch)
fastqc_ch=FASTQC(read_pairs_ch)

}

Recap

In this step you have learned:

• How to use the add a process to the workflow scope.
• Add a channel as input to a process.

MultiQC report

---

This step collect the outputs from the quantification and fastqc steps to create a final report by using the MultiQC tool.

The input for the MULTIQC process requires all data in a single channel element. Therefore, we will need to combine the FASTQC and QUANT outputs using:

• The combining operator mix : combines the items in the two channels into a single channel

GROOVY

//example of the mix operator
ch1 = Channel.of(1,2)
ch2 = Channel.of('a')
ch1.mix(ch2).view()

OUTPUT

1
2
a

• The transformation operator collect collects all the items in the new combined channel into a single item.

GROOVY

//example of the collect operator
ch1 = Channel.of(1,2,3)
ch1.collect().view()

OUTPUT

[1, 2, 3]

Combining operators

Which is the correct way to combined mix and collect operators so that you have a single channel with one List item?

1. quant_ch.mix(fastqc_ch).collect()
2. quant_ch.collect(fastqc_ch).mix()
3. fastqc_ch.mix(quant_ch).collect()
4. fastqc_ch.collect(quant_ch).mix()

Show me the solution

You need to use the mix operator first to combine the channels followed by the collect operator to collect all the items in a single item.

In script6.nf we use the statement quant_ch.mix(fastqc_ch).collect() to combine and collect the outputs of the QUANT and FASTQC process to create the required input for the MULTIQC process.

GROOVY

[..truncated..]
//script6.nf
/*
 * Create a report using multiQC for the quantification
 * and fastqc processes
 */
process MULTIQC {
    publishDir "${params.outdir}/multiqc", mode:'copy'

    input:
    path('*')

    output:
    path('multiqc_report.html')

    script:
    """
    multiqc .
    """
}


workflow {
  read_pairs_ch = Channel.fromFilePairs( params.reads, checkIfExists:true )
  transcriptome_ch = Channel.fromPath( params.transcriptome, checkIfExists:true )

  index_ch=INDEX(transcriptome_ch)
  quant_ch=QUANT(index_ch,read_pairs_ch)
  fastqc_ch=FASTQC(read_pairs_ch)
  MULTIQC(quant_ch.mix(fastqc_ch).collect())
}

Execute the script with the following command:

BASH

$ nextflow run script6.nf --reads 'data/yeast/reads/*_{1,2}.fq.gz' -resume

OUTPUT

N E X T F L O W  ~  version 21.04.0
Launching `script6.nf` [small_franklin] - revision: 9062818659
R N A S E Q - N F   P I P E L I N E
===================================
transcriptome: data/yeast/transcriptome/Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
reads        : data/yeast/reads/*_{1,2}.fq.gz
outdir       : results

executor >  local (9)
[02/3742cf] process > INDEX                              [100%] 1 of 1, cached: 1 ✔
[9a/be3483] process > QUANT (quantification on etoh60_1) [100%] 9 of 9, cached: 9 ✔
[1f/b7b30a] process > FASTQC (FASTQC on etoh60_1)        [100%] 9 of 9, cached: 1 ✔
[2c/206fef] process > MULTIQC                            [100%] 1 of 1 ✔

It creates the final report in the results folder in the ${params.outdir}/multiqc directory.

Recap

In this step you have learned:

• How to collect many outputs to a single input with the collect operator

• How to mix two channels in a single channel using the mix operator.

• How to chain two or more operators togethers using the . operator.

Handle completion event

---

This step shows how to execute an action when the pipeline completes the execution.

Note: that Nextflow processes define the execution of asynchronous tasks i.e. they are not executed one after another as they are written in the pipeline script as it would happen in a common imperative programming language.

The script script7..nf uses the workflow.onComplete event handler to print a confirmation message when the script completes.

GROOVY

workflow.onComplete {
	log.info ( workflow.success ? "\nDone! Open the following report in your browser --> $params.outdir/multiqc/multiqc_report.html\n" : "Oops .. something went wrong" )
}

This code uses the ternary operator that is a shortcut expression that is equivalent to an if/else branch assigning some value to a variable.

If expression is true? "set value to a" : "else set value to b"

Try to run it by using the following command:

BASH

$ nextflow run script7.nf -resume --reads 'data/yeast/reads/*_{1,2}.fq.gz'

OUTPUT

[..truncated..]
Done! Open the following report in your browser --> results/multiqc/multiqc_report.html

Metrics and reports

---

Nextflow is able to produce multiple reports and charts providing several runtime metrics and execution information.

• The -with-report option enables the creation of the workflow execution report.

• The -with-trace option enables the create of a tab separated file containing runtime information for each executed task, including: submission time, start time, completion time, cpu and memory used..

• The -with-timeline option enables the creation of the workflow timeline report showing how processes where executed along time. This may be useful to identify most time consuming tasks and bottlenecks. See an example at this link.

• The -with-dag option enables to rendering of the workflow execution direct acyclic graph representation. Note: this feature requires the installation of Graphviz, an open source graph visualization software, in your system.

More information can be found here.

Metrics and reports

Run the script7.nf with the reporting options as shown below:

BASH

$ nextflow run script7.nf -resume -with-report -with-trace -with-timeline -with-dag dag.png

1. Open the file report.html with a browser to see the report created with the above command.
2. Check the content of the file trace.txt or view timeline.html to find the longest running process.
3. View the dag.png

Show me the solution

The INDEX process should be the longest running process. dag.png The vertices in the graph represent the pipeline’s processes and operators, while the edges represent the data connections (i.e. channels) between them.

short running tasks

Note: runtime metrics may be incomplete for run short running tasks..

Key Points

• Nextflow can combined tasks (processes) and manage data flows using channels into a single pipeline/workflow.
• A Workflow can be parameterise using params . These value of the parameters can be captured in a log file using log.info
• Nextflow can handle a workflow’s software requirements using several technologies including the conda package and enviroment manager.
• Workflow steps are connected via their inputs and outputs using Channels.
• Intermediate pipeline results can be transformed using Channel operators such as combine.
• Nextflow can execute an action when the pipeline completes the execution using the workflow.onComplete event handler to print a confirmation message.
• Nextflow is able to produce multiple reports and charts providing several runtime metrics and execution information using the command line options -with-report, -with-trace, -with-timeline and produce a graph using -with-dag.

Content from Deploying nf-core pipelines

---

Last updated on 2024-07-22 | Edit this page

Estimated time: 40 minutes

Overview

Questions

• Where can I find best-practice Nextflow bioinformatic pipelines?
• How do I run nf-core pipelines?
• How do I configure nf-core pipelines to use my data?
• How do I reference nf-core pipelines?

Objectives

• Understand what nf-core is and how it relates to Nextflow.
• Use the nf-core helper tool to find nf-core pipelines.
• Understand how to configuration nf-core pipelines.
• Run a small demo nf-core pipeline using a test dataset.

What is nf-core?

nf-core is a community-led project to develop a set of best-practice pipelines built using Nextflow workflow management system. Pipelines are governed by a set of guidelines, enforced by community code reviews and automatic code testing.

A diagram showcasing the key aspects of nf-core, a community effort to provide best-practice analysis pipelines. The diagram is divided into three sections: Deploy, Participate, and Develop. The Deploy section includes features like Stable pipelines, Centralized configs, List and update pipelines, and Download for offline use. The Participate section highlights Documentation, Slack workspace, Twitter updates, and Hackathons. The Develop section emphasizes the Starter template, Code guidelines, CI code linting and tests, and Helper tools.

In this episode we will covering finding, deploying and configuring nf-core pipelines.

What are nf-core pipelines?

nf-core pipelines are an organised collection of Nextflow scripts, other non-nextflow scripts (written in any language), configuration files, software specifications, and documentation hosted on GitHub. There is generally a single pipeline for a given data and analysis type e.g. There is a single pipeline for bulk RNA-Seq. All nf-core pipelines are distributed under the, permissive free software, MIT licences.

What is nf-core tools?

nf-core provides a suite of helper tools aim to help people run and develop pipelines. The nf-core tools package is written in Python and can run from the command line or imported and used within other packages.

nf-core tools sub-commands

You can use the --help option to see the range of nf-core tools sub-commands. In this episode we will be covering the list, launch and download sub-commands which aid in the finding and deployment of the nf-core pipelines.

BASH

nf-core --help

OUTPUT

                                          ,--./,-.
          ___     __   __   __   ___     /,-._.--~\
    |\ | |__  __ /  ` /  \ |__) |__         }  {
    | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                          `._,._,'

 nf-core/tools version 2.14.1 - https://nf-co.re



 Usage: nf-core [OPTIONS] COMMAND [ARGS]...

 nf-core/tools provides a set of helper tools for use with nf-core Nextflow pipelines.
 It is designed for both end-users running pipelines and also developers creating new pipelines.

╭─ Options ────────────────────────────────────────────────────────────────────────────────────────╮
│ --version                        Show the version and exit.                                      │
│ --verbose        -v              Print verbose output to the console.                            │
│ --hide-progress                  Don't show progress bars.                                       │
│ --log-file       -l  <filename>  Save a verbose log to a file.                                   │
│ --help           -h              Show this message and exit.                                     │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands for users ─────────────────────────────────────────────────────────────────────────────╮
│ list                  List available nf-core pipelines with local info.                          │
│ launch                Launch a pipeline using a web GUI or command line prompts.                 │
│ create-params-file    Build a parameter file for a pipeline.                                     │
│ download              Download a pipeline, nf-core/configs and pipeline singularity images.      │
│ licences              List software licences for a given workflow (DSL1 only).                   │
│ tui                   Open Textual TUI.                                                          │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands for developers ────────────────────────────────────────────────────────────────────────╮
│ create            Create a new pipeline using the nf-core template.                              │
│ lint              Check pipeline code against nf-core guidelines.                                │
│ modules           Commands to manage Nextflow DSL2 modules (tool wrappers).                      │
│ subworkflows      Commands to manage Nextflow DSL2 subworkflows (tool wrappers).                 │
│ schema            Suite of tools for developers to manage pipeline schema.                       │
│ create-logo       Generate a logo with the nf-core logo template.                                │
│ bump-version      Update nf-core pipeline version number.                                        │
│ sync              Sync a pipeline TEMPLATE branch with the nf-core template.                     │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯

Listing available nf-core pipelines

The simplest sub-command is nf-core list, which lists all available nf-core pipelines in the nf-core Github repository.

The output shows the latest version number and when that was released. If the pipeline has been pulled locally using Nextflow, it tells you when that was and whether you have the latest version.

Run the command below.

BASH

nf-core list

An example of the output from the command is as follows:

OUTPUT

                                          ,--./,-.
          ___     __   __   __   ___     /,-._.--~\
    |\ | |__  __ /  ` /  \ |__) |__         }  {
    | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                          `._,._,'

    nf-core/tools version 2.14.1 - https://nf-co.re


┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pipeline Name             ┃ Stars ┃ Latest Release ┃      Released ┃ Last Pulled ┃ Have latest release? ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ mcmicro                   │     4 │            dev │    2 days ago │           - │ -                    │
│ fastquorum                │    13 │          1.0.0 │  2 months ago │           - │ -                    │
│ rnaseq                    │   821 │         3.14.0 │  6 months ago │           - │ -                    │
│ crisprseq                 │    22 │          2.2.0 │  1 months ago │           - │ -                    │
│ funcscan                  │    62 │          1.1.6 │   2 weeks ago │           - │ -                    │
│ pairgenomealign           │     0 │            dev │    3 days ago │           - │ -                    │
│ multiplesequencealign     │    11 │            dev │    3 days ago │           - │ -                    │
│ denovotranscript          │     0 │            dev │    3 days ago │           - │ -                    │
│ demo                      │     1 │          1.0.0 │  1 months ago │           - │ -                    │
│ demultiplex               │    37 │          1.4.1 │  5 months ago │           - │ -                    │
[..truncated..]

Filtering available nf-core pipelines

If you supply additional keywords after the list sub-command, the listed pipeline will be filtered. Note: that this searches more than just the displayed output, including keywords and description text.

Here we filter on the keywords rna and rna-seq .

BASH

nf-core list rna rna-seq

OUTPUT

                                          ,--./,-.
          ___     __   __   __   ___     /,-._.--~\
    |\ | |__  __ /  ` /  \ |__) |__         }  {
    | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                          `._,._,'

    nf-core/tools version 2.14.1 - https://nf-co.re


┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pipeline Name         ┃ Stars ┃ Latest Release ┃     Released ┃ Last Pulled ┃ Have latest release? ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ rnaseq                │   821 │         3.14.0 │ 6 months ago │           - │ -                    │
│ denovotranscript      │     0 │            dev │   3 days ago │           - │ -                    │
│ scnanoseq             │     5 │            dev │   4 days ago │           - │ -                    │
│ circrna               │    43 │            dev │   6 days ago │           - │ -                    │
│ smrnaseq              │    70 │          2.3.1 │ 3 months ago │           - │ -                    │
│ scrnaseq              │   185 │          2.7.0 │  4 weeks ago │           - │ -                    │
│ differentialabundance │    47 │          1.5.0 │ 2 months ago │           - │ -                    │
│ rnafusion             │   132 │          3.0.2 │ 3 months ago │           - │ -                    │
│ spatialvi             │    45 │            dev │ 2 months ago │           - │ -                    │
│ rnasplice             │    35 │          1.0.4 │ 2 months ago │           - │ -                    │
│ dualrnaseq            │    16 │          1.0.0 │  3 years ago │           - │ -                    │
│ marsseq               │     4 │          1.0.3 │  1 years ago │           - │ -                    │
│ lncpipe               │    30 │            dev │  2 years ago │           - │ -                    │
└───────────────────────┴───────┴────────────────┴──────────────┴─────────────┴──────────────────────┘

Sorting available nf-core pipelines

You can sort the results by adding the option --sort followed by a keyword. For example, latest release (--sort release), when you last pulled a local copy (--sort pulled), alphabetically (--sort name), or number of GitHub stars (--sort stars).

BASH

nf-core list rna rna-seq --sort stars

OUTPUT

                                      ,--./,-.
      ___     __   __   __   ___     /,-._.--~\
|\ | |__  __ /  ` /  \ |__) |__         }  {
| \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                      `._,._,'

    nf-core/tools version 2.14.1 - https://nf-co.re


┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pipeline Name         ┃ Stars ┃ Latest Release ┃     Released ┃ Last Pulled ┃ Have latest release? ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ rnaseq                │   821 │         3.14.0 │ 6 months ago │           - │ -                    │
│ scrnaseq              │   185 │          2.7.0 │  4 weeks ago │           - │ -                    │
│ rnafusion             │   132 │          3.0.2 │ 3 months ago │           - │ -                    │
│ smrnaseq              │    70 │          2.3.1 │ 3 months ago │           - │ -                    │
│ differentialabundance │    47 │          1.5.0 │ 2 months ago │           - │ -                    │
│ spatialvi             │    45 │            dev │ 2 months ago │           - │ -                    │
│ circrna               │    43 │            dev │   6 days ago │           - │ -                    │
│ rnasplice             │    35 │          1.0.4 │ 2 months ago │           - │ -                    │
│ lncpipe               │    30 │            dev │  2 years ago │           - │ -                    │
│ dualrnaseq            │    16 │          1.0.0 │  3 years ago │           - │ -                    │
│ scnanoseq             │     5 │            dev │   4 days ago │           - │ -                    │
│ marsseq               │     4 │          1.0.3 │  1 years ago │           - │ -                    │
│ denovotranscript      │     0 │            dev │   3 days ago │           - │ -                    │
└───────────────────────┴───────┴────────────────┴──────────────┴─────────────┴──────────────────────┘

Archived pipelines

Archived pipelines are not returned by default. To include them, use the --show_archived flag.

Exercise: listing nf-core pipelines

1. Use the --help flag to print the list command usage.
2. Sort all pipelines by popularity (stars) and find out which is the most popular?.
3. Filter pipelines for those that work with RNA and find out how many there are?

Show me the solution

Use the --help flag to print the list command usage.

BASH

$ nf-core list --help

Sort all pipelines by popularity (stars).

BASH

$ nf-core list --sort stars

Filter pipelines for those that work with RNA.

BASH

$ nf-core list rna

Running nf-core pipelines

Software requirements for nf-core pipelines

nf-core pipeline software dependencies are specified using either Docker, Singularity or Conda. It is Nextflow that handles the downloading of containers and creation of conda environments. In theory it is possible to run the pipelines with software installed by other methods (e.g. environment modules, or manual installation), but this is not recommended.

Fetching pipeline code

Unless you are actively developing pipeline code, you should use Nextflow’s built-in functionality to fetch nf-core pipelines. You can use the following command to pull the latest version of a remote workflow from the nf-core github site.;

BASH

$ nextflow pull nf-core/<PIPELINE>

Note Nextflow will also automatically fetch the pipeline code when use nextflow run nf-core/<PIPELINE> command.

For the best reproducibility, it is good to explicitly reference the pipeline version number that you wish to use with the -revision/-r flag.

In the example below we are pulling the rnaseq pipeline version 3.0

BASH

nextflow pull nf-core/rnaseq -revision 3.14.0

We can check the pipeline has been pulled using the nf-core list command.

BASH

nf-core list rnaseq -s pulled

We can see from the output we have the latest release.

OUTPUT

                                      ,--./,-.
      ___     __   __   __   ___     /,-._.--~\
|\ | |__  __ /  ` /  \ |__) |__         }  {
| \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                      `._,._,'

    nf-core/tools version 2.14.1 - https://nf-co.re


┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Pipeline Name         ┃ Stars ┃ Latest Release ┃     Released ┃    Last Pulled ┃ Have latest release? ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ rnaseq                │   821 │         3.14.0 │ 6 months ago │ 59 seconds ago │ Yes (v3.14.0)        │
[..truncated..]

Development Releases

If not specified, Nextflow will fetch the default git branch. For pipelines with a stable release this the default branch is master - this branch contains code from the latest release. For pipelines in early development that don’t have any releases, the default branch is dev.

Exercise: Fetch the latest RNA-Seq pipeline

1. Use the nextflow pull command to download the latest nf-core/rnaseq pipeline

2. Use the nf-core list command to see if you have the latest version of the pipeline

Show me the solution

Use the nextflow pull command to download the latest nf-core/rnaseq pipeline

BASH

$ nextflow pull nf-core/rnaseq

Use the nf-core list command to see if you have the latest version of the pipeline

BASH

$ nf-core list rnaseq --sort pulled

Usage instructions and documentation

---

You can find general documentation and instructions for Nextflow and nf-core on the nf-core website . Pipeline-specific documentation is bundled with each pipeline in the /docs folder. This can be read either locally, on GitHub, or on the nf-core website.

Each pipeline has its own webpage at https://nf-co.re/<pipeline_name> e.g. nf-co.re/rnaseq

In addition to this documentation, each pipeline comes with basic command line reference. This can be seen by running the pipeline with the parameter --help , for example:

BASH

nextflow run -r 3.14.0 nf-core/rnaseq --help

OUTPUT

N E X T F L O W  ~  version 20.10.0
Launching `nf-core/rnaseq` [silly_miescher] - revision: 964425e3fd [3.4]
------------------------------------------------------
                                        ,--./,-.
        ___     __   __   __   ___     /,-._.--~'
  |\ | |__  __ /  ` /  \ |__) |__         }  {
  | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                        `._,._,'
  nf-core/rnaseq v3.14.0-gb89fac3
------------------------------------------------------
Typical pipeline command:

  nextflow run nf-core/rnaseq --input samplesheet.csv --genome GRCh37 -profile docker

Input/output options
  --input                            [string]  Path to comma-separated file containing information about the samples in the experiment.
  --outdir                           [string]  The output directory where the results will be saved. You have to use absolute paths to storage on Cloud
                                               infrastructure.
  --email                            [string]  Email address for completion summary.
  --multiqc_title                    [string]  MultiQC report title. Printed as page header, used for filename if not otherwise specified.
..truncated..

The nf-core launch command

As can be seen from the output of the help option nf-core pipelines have a number of flags that need to be passed on the command line: some mandatory, some optional.

To make it easier to launch pipelines, these parameters are described in a JSON file, nextflow_schema.json bundled with the pipeline.

The nf-core launch command uses this to build an interactive command-line wizard which walks through the different options with descriptions of each, showing the default value and prompting for values.

Once all prompts have been answered, non-default values are saved to a params.json file which can be supplied to Nextflow using the -params-file option. Optionally, the Nextflow command can be launched there and then.

To use the launch feature, just specify the pipeline name:

BASH

nf-core launch -r 3.14.0 rnaseq

Exercise : Create nf-core params file

Use the nf-core launch command to create a params file named nf-params.json.

1. Use the nf-core launch command to launch the interactive command-line wizard.
2. Add an input file name samples.csv
3. Add a genome GRCh38 ** Note ** : Do not run the command now.

Show me the solution

BASH

$ nf-core launch rnaseq

The contents of the nf-params.json file should be

{
  "input": "samples.csv",
  "genome": "GRCh38"
}

Config files

nf-core pipelines make use of Nextflow’s configuration files to specify how the pipelines runs, define custom parameters and what software management system to use e.g. docker, singularity or conda.

Nextflow can load pipeline configurations from multiple locations. nf-core pipelines load configuration in the following order:

A diagram explaining the structure and hierarchy of nextflow.config files. The diagram shows that the default ‘base’ config is always loaded. It also includes core profiles, such as docker, conda, and test, and server profiles from nf-core/config. Additionally, it highlights that your local config files, located in $HOME/.nextflow/config or specified with -c custom.config, are also considered.

1. Pipeline: Default ‘base’ config

• Always loaded. Contains pipeline-specific parameters and “sensible defaults” for things like computational requirements
• Does not specify any method for software packaging. If nothing else is specified, Nextflow will expect all software to be available on the command line.

2. Core config profiles

• All nf-core pipelines come with some generic config profiles. The most commonly used ones are for software packaging: docker, singularity and conda
• Other core profiles are debug and two test profiles. There two test profile, a small test profile (nf-core/test-datasets) for quick test and a full test profile which provides the path to full sized data from public repositories.

3. Server profiles

• At run time, nf-core pipelines fetch configuration profiles from the configs remote repository. The profiles here are specific to clusters at different institutions.
• Because this is loaded at run time, anyone can add a profile here for their system and it will be immediately available for all nf-core pipelines.

4. Local config files given to Nextflow with the -c flag

BASH

nextflow run nf-core/rnaseq -r 3.14.0 -c mylocal.config

5. Command line configuration: pipeline parameters can be passed on the command line using the --<parameter> syntax.

BASH

nextflow run nf-core/rnaseq -r 3.14.0 --email "my@email.com"`

Config Profiles

nf-core makes use of Nextflow configuration profiles to make it easy to apply a group of options on the command line.

Configuration files can contain the definition of one or more profiles. A profile is a set of configuration attributes that can be activated/chosen when launching a pipeline execution by using the -profile command line option. Common profiles are conda, singularity and docker that specify which software manager to use.

Multiple profiles are comma-separated. When there are differing configuration settings provided by different profiles, the right-most profile takes priority.

BASH

nextflow run nf-core/rnaseq -r 3.14.0 -profile test,conda
nextflow run nf-core/rnaseq -r 3.14.0 -profile <institutional_config_profile>, test, conda

Note The order in which config profiles are specified matters. Their priority increases from left to right.

Multiple Nextflow configuration locations

Be clever with multiple Nextflow configuration locations. For example, use -profile for your cluster configuration, the file $HOME/.nextflow/config for your personal config such as params.email and a working directory >nextflow.config file for reproducible run-specific configuration.

Exercise create a custom config

Add the params.email to a file called nfcore-custom.config

Show me the solution

A line similar to one below in the file custom.config

params.email = "myemail@address.com"

Running pipelines with test data

---

The nf-core config profile test is special profile, which defines a minimal data set and configuration, that runs quickly and tests the workflow from beginning to end. Since the data is minimal, the output is often nonsense. Real world example output are instead linked on the nf-core pipeline web page, where the workflow has been run with a full size data set:

BASH

nextflow run nf-core/<pipeline_name> -profile test

Software configuration profile

Note that you will typically still need to combine this with a software configuration profile for your system - e.g. -profile test,conda. Running with the test profile is a great way to confirm that you have Nextflow configured properly for your system before attempting to run with real data

Using nf-core pipelines offline

Many of the techniques and resources described above require an active internet connection at run time - pipeline files, configuration profiles and software containers are all dynamically fetched when the pipeline is launched. This can be a problem for people using secure computing resources that do not have connections to the internet.

To help with this, the nf-core download command automates the fetching of required files for running nf-core pipelines offline. The command can download a specific release of a pipeline with -r/--release .
By default, the pipeline will download the pipeline code and the institutional nf-core/configs files.

If you specify the flag --singularity, it will also download any singularity image files that are required (this needs Singularity to be installed). All files are saved to a single directory, ready to be transferred to the cluster where the pipeline will be executed.

BASH

nf-core download nf-core/rnaseq -r 3.14.0

OUTPUT

                                          ,--./,-.
          ___     __   __   __   ___     /,-._.--~\
    |\ | |__  __ /  ` /  \ |__) |__         }  {
    | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                          `._,._,'

    nf-core/tools version 2.14.1 - https://nf-co.re


WARNING  Could not find GitHub authentication token. Some API requests may fail.
? Include the nf-core's default institutional configuration files into the download? No

In addition to the pipeline code, this tool can download software containers.
? Download software container images: none

If transferring the downloaded files to another system, it can be convenient to have everything compressed in a single file.
? Choose compression type: none
INFO     Saving 'nf-core/rnaseq'
          Pipeline revision: '3.14.0'
          Use containers: 'none'
          Container library: 'quay.io'
          Output directory: 'nf-core-rnaseq_3.14.0'
          Include default institutional configuration: 'False'
INFO     Downloading workflow files from GitHub

Exercise Run a demo nf-core pipeline

Run the nf-core/demo pipeline release 1.0.0 with the provided test data using the profile test. Add the parameters --max_memory 3G, --skip_trim, and –outdir “nfcore-demo-out” on the command line. Include the config file, nfcore-custom.config, from the previous exercise using the option -c, to send an email when your pipeline finishes.

BASH

$ nextflow run nf-core/demo -r 1.0.0  -profile test --skip_trim --max_memory 3.GB -c nfcore-custom.config

The nf-core/demo pipleine is a simple nf-core style bioinformatics pipeline for workshops and demonstrations that runs FASTQC and multiqc.

Show me the solution

OUTPUT

 N E X T F L O W   ~  version 24.04.3

Launching `https://github.com/nf-core/demo` [silly_bohr] DSL2 - revision: 705f18e4b1 [master]

WARN: Access to undefined parameter `monochromeLogs` -- Initialise it to a default value eg. `params.monochromeLogs = some_value`


------------------------------------------------------
                                        ,--./,-.
        ___     __   __   __   ___     /,-._.--~'
  |\ | |__  __ /  ` /  \ |__) |__         }  {
  | \| |       \__, \__/ |  \ |___     \`-._,-`-,
                                        `._,._,'
  nf-core/demo v1.0.0-g705f18e
------------------------------------------------------
Core Nextflow options
  revision                  : master
  runName                   : silly_bohr
  launchDir                 : /home/nf-training
  workDir                   : /home/nf-training/work
  projectDir                : /home/nf-training/.nextflow/assets/nf-core/demo
  userName                  : nf-training
  profile                   : test
  configFiles               :

Input/output options
  input                     : https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv
  outdir                    : out
  email                     : myemail@address.com

Process skipping options
  skip_trim                 : true

Institutional config options
  config_profile_name       : Test profile
  config_profile_description: Minimal test dataset to check pipeline function

Max job request options
  max_cpus                  : 2
  max_memory                : 3.GB
  max_time                  : 6.h

!! Only displaying parameters that differ from the pipeline defaults !!
------------------------------------------------------
If you use nf-core/demo for your analysis please cite:

* The pipeline

* The nf-core framework
  https://doi.org/10.1038/s41587-020-0439-x

* Software dependencies
  https://github.com/nf-core/demo/blob/master/CITATIONS.md
------------------------------------------------------
executor >  local (4)
[08/94286a] process > NFCORE_DEMO:DEMO:FASTQC (SAMPLE2_PE) [100%] 3 of 3 ✔
[df/45c5c7] process > NFCORE_DEMO:DEMO:MULTIQC             [100%] 1 of 1 ✔
-[nf-core/demo] Sent summary e-mail to graeme.grimes@ed.ac.uk (sendmail)-
-[nf-core/demo] Pipeline completed successfully-

Troubleshooting

If you run into issues running your pipeline you can you the nf-core website to troubleshoot common mistakes and issues https://nf-co.re/usage/troubleshooting .

Extra resources and getting help

If you still have an issue with running the pipeline then feel free to contact the nf-core community via the Slack channel . The nf-core Slack organisation has channels dedicated for each pipeline, as well as specific topics (eg. #help, #pipelines, #tools, #configs and much more). The nf-core Slack can be found at https://nfcore.slack.com (NB: no hyphen in nfcore!). To join you will need an invite, which you can get at https://nf-co.re/join/slack.

You can also get help by opening an issue in the respective pipeline repository on GitHub asking for help.

If you have problems that are directly related to Nextflow and not our pipelines or the nf-core framework tools then check out the Nextflow gitter channel or the google group.

Referencing a Pipeline

Publications

If you use an nf-core pipeline in your work you should cite the main publication for the main nf-core paper, describing the community and framework, as follows:

The nf-core framework for community-curated bioinformatics pipelines. Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x. ReadCube: Full Access Link

Many of the pipelines have a publication listed on the nf-core website that can be found here.

DOIs

In addition, each release of an nf-core pipeline has a digital object identifiers (DOIs) for easy referencing in literature The DOIs are generated by Zenodo from the pipeline’s github repository.

Key Points

• nf-core is a community-led project to develop a set of best-practice pipelines built using the Nextflow workflow management system.
• The nf-core tool (nf-core) is a suite of helper tools that aims to help people run and develop nf-core pipelines.
• nf-core pipelines can be found using nf-core list, or by checking the nf-core website.
• nf-core launch nf-core/<pipeline> can be used to write a parameter file for an nf-core pipeline. This can be supplied to the pipeline using the -params-file option.
• An nf-core workflow is run using nextflow run nf-core/<pipeline> syntax.
• nf-core pipelines can be reconfigured by using custom config files and/or adding command line parameters.