Nextflow training for CRG PhD course 2019

Nextflow in a nutshell

A workflow engine for data analysis pipelines with a strong focus on enabling:

• Portability
• Reproducibility
• Scalability
• Usability

How?

• Functional/reactive programming model
• Decoupling and isolating tasks
• Concise domain specific language for recurrent task operations
• Pragmatic, allowing quick prototyping and iterations
• Hide complexity
• Coexists with errors (!)

Prerequisite

• Unix-like OS (Linux, macOS, etc.)
• Java 8 or later
• Docker engine 1.10.x (or later)
• Singularity 2.5.x (or later, optional)
• Conda 4.5 (or later, optional)
• Graphviz (optional)
• AWS Batch computing environment properly configured (optional)

Installation

Clone this repository with the following command:

git clone https://github.com/cbcrg/nf-phdcourse19.git && cd nf-phdcourse19

Then, install Nextflow by using the following command:

curl https://get.nextflow.io | bash

The above snippet creates the nextflow launcher in the current directory.

Finally pull the following Docker container:

docker pull nextflow/rnaseq-nf

Nextflow hands-on

During this tutorial you will implement a proof of concept of a RNA-seq pipeline which:

1. Indexes a trascriptome file.
2. Performs quality controls
3. Performs quantification.
4. Create a MultiqQC report.

Step 1 - define the pipeline parameters

The script script1.nf defines the pipeline input parameters. Run it by using the following command:

./nextflow run script1.nf

Try to specify a different input parameter, for example:

./nextflow run script1.nf --reads this/and/that

Exercise 1.1

Modify the script1.nf adding a fourth parameter named outdir and set it to a default path that will be used as the pipeline output directory.

Exercise 1.2

Modify the script1.nf to print all the pipeline parameters using log.info instead of the println command and a multiline string statement.

Tip: see an example here.

Recap

In this step you have learned:

1. How to define parameters in your pipeline script
2. How to pass parameters by using the command line
3. The use of $var and ${var} variable placeholders
4. How to use multiline strings
5. How to use log.info to report values

Step 2 - Create transcriptome index file

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

A process is defined by providing three main declarations: the process inputs, the process outputs and finally the command script.

The second example adds the index process. Open it to see how the process is defined.

It takes the transcriptome file as input and creates the transcriptome index by using the salmon tool.

Note how the input declaration defines a transcriptome variable in the process context that it is used in the command script to reference that file in the Salmon command line.

Try to run it by using the command:

./nextflow run script2.nf

The execution will fail because Salmon is not installed in your environment.

Add the command line option -with-docker to launch the execution through a Docker container as shown below:

./nextflow run script2.nf -with-docker

This time it works because it uses the Docker container nextflow/rnaseq-nf defined in the nextflow.config file.

In order to avoid to add the option -with-docker add the following line in the nextflow.config file:

docker.enabled = true

Exercise 2.1

Enable the Docker execution by default adding the above setting in the nextflow.config file.

Exercise 2.2

Print the output of the index_ch channel by using the view operator.

Exercise 2.3

Use the command tree work to see how Nextflow organises the process work directory.

Recap

In this step you have learned:

1. How to define a process executing a custom command
2. How process inputs are declared
3. How process outputs are declared
4. How to access the number of available CPUs
5. How to print the content of a channel

Step 3 - Collect read files by pairs

This step shows how to match read files into pairs, so they can be mapped by Salmon.

Edit the script script3.nf and add the following statement as the last line:

read_pairs_ch.view()

Save it and execute it with the following command:

./nextflow run script3.nf

It will print an output similar to the one shown below:

[ggal_gut, [/.../data/ggal/gut_1.fq, /.../data/ggal/gut_2.fq]]

The above example shows how the read_pairs_ch channel emits tuples composed by two elements, where the first is the read pair prefix and the second is a list representing the actual files.

Try it again specifying different read files by using a glob pattern:

./nextflow run script3.nf --reads 'data/ggal/*_{1,2}.fq'

Exercise 3.1

Use the set operator in place of = assignment to define the read_pairs_ch channel.

Exercise 3.2

Use the checkIfExists for the fromFilePairs method to make sure it returns some file pairs.

Recap

In this step you have learned:

1. How to use fromFilePairs to handle read pair files.
2. How to use the set operator to define a new channel variable.
3. How to use the checkIfExists option.

Step 4 - Perform expression quantification

The script script4.nf adds the quantification process.

In this script note as the index_ch channel, declared as output in the index process, is now used as a channel in the input section.

Also note as the second input is declared as a set composed by two elements: the pair_id and the reads in order to match the structure of the items emitted by the read_pairs_ch channel.

Execute it by using the following command:

./nextflow run script4.nf -resume

You will see the execution of the quantification process.

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:

./nextflow run script4.nf -resume --reads 'data/ggal/*_{1,2}.fq'

You will notice that the quantification process is executed more than one time.

Nextflow parallelizes the execution of your pipeline simply by providing multiple input data to your script.

Exercise 4.1

Add a tag directive to the quantification process to provide a more readable execution log.

Exercise 4.2

Add a publishDir directive to the quantification process to store the process results into a directory of your choice.

Recap

In this step you have learned:

1. How to connect two processes by using the channel declarations
2. How to resume the script execution skipping already already computed steps
3. How to use the tag directive to provide a more readable execution output
4. How to use the publishDir to store a process results in a path of your choice

Step 5 - Quality control

This step implements a quality control of your input reads. The inputs are the same read pairs which are provided to the quantification steps

You can run it by using the following command:

./nextflow run script5.nf -resume 

The script will report the following error message:

Channel `read_pairs_ch` has been used twice as an input by process `fastqc` and process `quantification`

Exercise 5.1

Modify the creation of the read_pairs_ch channel by using a into operator in place of a set.

Tip: see an example here.

Recap

In this step you have learned:

1. How to use the into operator to create multiple copies of the same channel

Step 6 - MultiQC report

This step collect the outputs from the quantification and fastqc steps to create a final report by using the MultiQC tool.

Execute the script with the following command:

./nextflow run script6.nf -resume --reads 'data/ggal/*_{1,2}.fq' 

It creates the final report in the results folder in the current work directory.

In this script note the use of the mix and collect operators chained together to get all the outputs of the quantification and fastqc process as a single input.

Recap

In this step you have learned:

1. How to collect many outputs to a single input with the collect operator
2. How to mix two channels in a single channel
3. How to chain two or more operators togethers

Step 7 - 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 uses the workflow.onComplete event handler to print a confirmation message when the script completes.

Try to run it by using the following command:

./nextflow run script7.nf -resume --reads 'data/ggal/*_{1,2}.fq'

Bonus!

Send a notification email when the workflow execution complete using the -N <email address> command line option. Note: this requires the configuration of a SMTP server in nextflow config file. See mail documentation for details.

Step 8 - Custom scripts

Real world pipelines use a lot of custom user scripts (BASH, R, Python, etc). Nextflow allows you to use and manage all these scripts in consistent manner. Simply put them in a directory named bin in the pipeline project root. They will be automatically added to the pipeline execution PATH.

For example, create a file named fastqc.sh with the following content:

#!/bin/bash 
set -e 
set -u

sample_id=${1}
reads=${2}

mkdir fastqc_${sample_id}_logs
fastqc -o fastqc_${sample_id}_logs -f fastq -q ${reads}

Save it, give execute permission and move it in the bin directory as shown below:

chmod +x fastqc.sh
mkdir -p bin 
mv fastqc.sh bin

Then, open the script7.nf file and replace the fastqc process' script with
the following code:

  script:
    """
    fastqc.sh "$sample_id" "$reads"
    """  

Run it as before:

./nextflow run script7.nf -resume --reads 'data/ggal/*_{1,2}.fq'

Recap

In this step you have learned:

1. How to write or use existing custom script in your Nextflow pipeline.
2. How to avoid the use of absolute paths having your scripts in the bin/ project folder.

Step 9 - Executors

Real world genomic application can spawn the execution of thousands of jobs. In this scenario a batch scheduler is commonly used to deploy a pipeline in a computing cluster, allowing the execution of many jobs in parallel across many computing nodes.

Nextflow has built-in support for most common used batch schedulers such as Univa Grid Engine and SLURM between the others.

To run your pipeline with a batch scheduler modify the nextflow.config file specifying the target executor and the required computing resources if needed. For example:

process.executor = 'slurm'
process.queue = 'short'
process.memory = '10 GB' 
process.time = '30 min'
process.cpus = 4 

The above configuration specify the use of the SLURM batch scheduler to run the jobs spawned by your pipeline script. Then it specifies to use the short queue (partition), 10 gigabyte of memory and 4 CPUs per job, and each job can run for no more than 30 minutes.

Note: the pipeline must be executed in a shared file system accessible to all the computing nodes.

Exercise 9.1

Print the head of the .command.run script generated by Nextflow in the task work directory and verify it contains the SLURM #SBATCH directives for the requested resources.

Exercise 9.2

Modify the configuration file to specify different resource request for the quantification process.

Tip: see the process documentation for an example.

Recap

In this step you have learned:

1. How to deploy a pipeline in a computing cluster.
2. How to specify different computing resources for different pipeline processes.

Step 10 - Run in the cloud using AWS Batch

The built-in support for AWS Batch allows the execution your workflow scripts only changing a few settings in the nextflow.config file. For example:

    workDir = 's3://cbcrg-eu/work'
    process.executor = 'awsbatch'
    process.queue = 'demo'
    process.container = 'nextflow/rnaseq-nf'      
    executor.awscli = '/home/ec2-user/miniconda/bin/aws'
    aws.region = 'eu-west-1'  

A S3 bucket must be provide by using the workDir configuration setting. Also the name of a queue previously created in the AWS Batch environment needs to be specified using the process.queue setting.

See the AWS Batch documentation for details.

Step 11 - Use configuration profiles

The Nextflow configuration file can be organised in different profiles to allow the specification of separate settings depending on the target execution environment.

For the sake of this tutorial modify the nextflow.config as shown below:

profiles {
  standard {
    process.container = 'nextflow/rnaseq-nf'
    docker.enabled = true
  }
  
  cluster {
    process.executor = 'slurm'
    process.queue = 'short'
    process.memory = '10 GB' 
    process.time = '30 min'
    process.cpus = 8     
  }

  batch {
    workDir = 's3://cbcrg-eu/work' 
    process.executor = 'awsbatch'
    process.queue = 'demo'
    process.container = 'nextflow/rnaseq-nf'  
    aws.region = 'eu-west-1'
    aws.batch.cliPath = '/home/ec2-user/miniconda/bin/aws'
  }
} 

The above configuration defines two profiles: standard and cluster. The name of the profile to use can be specified when running the pipeline script by using the -profile option. For example:

./nextflow run script7.nf -profile cluster 

The profile standard is used by default if no other profile is specified by the user.

Recap

In this step you have learned:

1. How to organise your pipeline configuration in separate profiles

Step 12 - Run a pipeline from a GitHub repository

Nextflow allows the execution of a pipeline project directly from a GitHub repository (or similar services eg. BitBucket and GitLab).

This simplifies the sharing and the deployment of complex projects and tracking changes in a consistent manner.

The following GitHub repository hosts a complete version of the workflow introduced in this tutorial:

https://github.com/nextflow-io/rnaseq-nf

You can run it by specifying the project name as shown below:

./nextflow run nextflow-io/rnaseq-nf -with-docker

It automatically downloads it and store in the $HOME/.nextflow folder.

Use the command info to show the project information, e.g.:

./nextflow info nextflow-io/rnaseq-nf

Nextflow allows the execution of a specific revision of your project by using the -r command line option. For Example:

./nextflow run nextflow-io/rnaseq-nf -r dev

Revision are defined by using Git tags or branches defined in the project repository.

This allows a precise control of the changes in your project files and dependencies over time.

Conda/Bioconda packages

Conda is popular package and environment manager. The built-in support for Conda allows Nextflow pipelines to automatically creates and activates the Conda environment(s) given the dependencies specified by each process.

To use a Conda environment with Nextflow specify it as a command line option as shown below:

./nextflow run script7.nf -with-conda env.yml

The use of a Conda environment can also be provided in the configuration file adding the following setting in the nextflow.config file:

process.conda = "env.yml"

See the Nextflow in the Nextflow documentation for details.

Metrics and reports

Nextflow is able to produce multiple reports and charts providing several runtime metrics and execution information.

Run the rnaseq-nf pipeline previously introduced as shown below:

./nextflow run rnaseq-nf -with-docker -with-report -with-trace -with-timeline -with-dag dag.png

The -with-report option enables the creation of the workflow execution report. Open the file report.html with a browser to see the report created with the above command.

The -with-trace option enables the create of a tab separated file containing runtime information for each executed task. Check the content of the file trace.txt for an example.

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.

Finally the -with-dag option enables to rendering of the workflow execution direct acyclic graph representation. Note: this feature requires the installation of Graphviz in your computer. See here for details.

Note: runtime metrics may be incomplete for run short running tasks as in the case of this tutorial.

Docker hands-on

Get practice with basic Docker commands to pull, run and build your own containers.

A container is a ready-to-run Linux environment which can be executed in an isolated manner from the hosting system. It has own copy of the file system, processes space, memory management, etc.

Containers are a Linux feature known as Control Groups or Ccgroups introduced with kernel 2.6.

Docker adds to this concept an handy management tool to build, run and share container images.

These images can be uploaded and published in a centralised repository know as Docker Hub, or hosted by other parties like for example Quay.

Step 1 - Run a container

Run a container is easy as using the following command:

docker run <container-name> 

For example:

docker run hello-world  

Step 2 - Pull a container

The pull command allows you to download a Docker image without running it. For example:

docker pull debian:wheezy 

The above command download a Debian Linux image.

Step 3 - Run a container in interactive mode

Launching a BASH shell in the container allows you to operate in an interactive mode in the containerised operating system. For example:

docker run -it debian:wheezy bash 

Once launched the container you wil noticed that's running as root (!). Use the usual commands to navigate in the file system.

To exit from the container, stop the BASH session with the exit command.

Step 4 - Your first Dockerfile

Docker images are created by using a so called Dockerfile i.e. a simple text file containing a list of commands to be executed to assemble and configure the image with the software packages required.

In this step you will create a Docker image containing the Samtools tool.

Warning: the Docker build process automatically copies all files that are located in the current directory to the Docker daemon in order to create the image. This can take a lot of time when big/many files exist. For this reason it's important to always work in a directory containing only the files you really need to include in your Docker image. Alternatively you can use the .dockerignore file to select the path to exclude from the build.

Then use your favourite editor eg. vim to create a file named Dockerfile and copy the following content:

FROM debian:wheezy 

MAINTAINER <your name>

RUN apt-get update && apt-get install -y curl cowsay 

ENV PATH=$PATH:/usr/games/
   

When done save the file.

Step 5 - Build the image

Build the Docker image by using the following command:

docker build -t my-image .

Note: don't miss the dot in the above command. When it completes, verify that the image has been created listing all available images:

docker images

You can try your new container by running this command:

docker run my-image cowsay Hello Docker!

Step 6 - Add a software package to the image

Add the Salmon package to the Docker image by adding to the Dockerfile the following snippet:

RUN curl -sSL https://github.com/COMBINE-lab/salmon/releases/download/v0.8.2/Salmon-0.8.2_linux_x86_64.tar.gz | tar xz \
 && mv /Salmon-*/bin/* /usr/bin/ \
 && mv /Salmon-*/lib/* /usr/lib/

Save the file and build again the image with the same command as before:

docker build -t my-image .

You will notice that it creates a new Docker image with the same name but with a different image ID.

Step 7 - Run Salmon in the container

Check that everything is fine running Salmon in the container as shown below:

docker run my-image salmon --version

You can even launch a container in an interactive mode by using the following command:

docker run -it my-image bash

Use the exit command to terminate the interactive session.

Step 8 - File system mounts

Create an genome index file by running Salmon in the container.

Try to run Bowtie in the container with the following command:

docker run my-image \
  salmon index -t $PWD/data/ggal/transcriptome.fa -i index

The above command fails because Salmon cannot access the input file.

This happens because the container runs in a complete separate file system and it cannot access the hosting file system by default.

You will need to use the --volume command line option to mount the input file(s) eg.

docker run --volume $PWD/data/ggal/transcriptome.fa:/transcriptome.fa my-image \
  salmon index -t /transcriptome.fa -i index 

An easier way is to mount a parent directory to an identical one in the container, this allows you to use the same path when running it in the container eg.

docker run --volume $HOME:$HOME --workdir $PWD my-image \
  salmon index -t $PWD/data/ggal/transcriptome.fa -i index

Step 9 - Upload the container in the Docker Hub (bonus)

Publish your container in the Docker Hub to share it with other people.

Create an account in the https://hub.docker.com web site. Then from your shell terminal run the following command, entering the user name and password you specified registering in the Hub:

docker login 

Tag the image with your Docker user name account:

docker tag my-image <user-name>/my-image 

Finally push it to the Docker Hub:

docker push <user-name>/my-image 

After that anyone will be able to download it by using the command:

docker pull <user-name>/my-image 

Note how after a pull and push operation, Docker prints the container digest number e.g.

Digest: sha256:aeacbd7ea1154f263cda972a96920fb228b2033544c2641476350b9317dab266
Status: Downloaded newer image for nextflow/rnaseq-nf:latest

This is a unique and immutable identifier that can be used to reference container image in a univocally manner. For example:

docker pull nextflow/rnaseq-nf@sha256:aeacbd7ea1154f263cda972a96920fb228b2033544c2641476350b9317dab266

More resources

• Nextflow documentation - The Nextflow docs home.
• Nextflow patterns - A collection of Nextflow implementation patterns.
• CalliNGS-NF - An Variant calling pipeline implementing GATK best practices.
• nf-core - A community collection of production ready genomic pipelines.