Nextflow was one of the first workflow framework to provide built-in support for Docker containers. A couple of years ago we also started to experiment with the deployment of containerised bioinformatic pipelines at CRG, using Docker technology (see here and here).
We found that by isolating and packaging the complete computational workflow environment with the use of Docker images, radically simplifies the burden of maintaining complex dependency graphs of real workload data analysis pipelines.
Even more importantly, the use of containers enables replicable results with minimal effort for the system configuration. The entire computational environment can be archived in a self-contained executable format, allowing the replication of the associated analysis at any point in time.
This ability is the main reason that drove the rapid adoption of Docker in the bioinformatic community and its support in many projects, like for example Galaxy, CWL, Bioboxes, Dockstore and .. (click here to read more)
Learn how to deploy an elastic computing cluster in the AWS cloud with Nextflow
In the previous post I introduced the new cloud native support for AWS provided by Nextflow.
It allows the creation of a computing cluster in the cloud in a no-brainer way, enabling the deployment of complex computational pipelines in a few commands.
This solution is characterised by using a lean application stack which does not require any third party component installed in the EC2 instances other than a Java VM and the Docker engine (the latter it's only required in order to deploy pipeline binary dependencies).
Each EC2 instance runs a script, at bootstrap time, that mounts the EFS storage and downloads and launches the Nextflow cluster daemon. This daemon is self-configuring, it automatically discovers the other running instances and .. (click here to read more)
Learn how to deploy and run a computational pipeline in the Amazon AWS cloud with ease thanks to Nextflow and Docker containers
Nextflow is a framework that simplifies the writing of parallel and distributed computational pipelines in a portable and reproducible manner across different computing platforms, from a laptop to a cluster of computers.
Indeed, the original idea, when this project started three years ago, was to implement a tool that would allow researchers in our lab to smoothly migrate their data analysis applications in the cloud when needed - without having to change or adapt their code.
However to date Nextflow has been used mostly to deploy computational workflows within on-premise computing clusters or HPC data-centers, because these infrastructures are easier to use and provide, on average, cheaper cost and better performance when compared to a cloud environment.
A major obstacle to .. (click here to read more)
Below is a step-by-step guide for creating Docker images for use with Nextflow pipelines. This post was inspired by recent experiences and written with the hope that it may encourage others to join in the virtualization revolution.
Modern science is built on collaboration. Recently I became involved with one such venture between several groups across Europe. The aim was to annotate long non-coding RNA (lncRNA) in farm animals and I agreed to help with the annotation based on RNA-Seq data. The basic procedure relies on mapping short read data from many different tissues to a genome, generating transcripts and then determining if they are likely to be lncRNA or protein coding genes.
During several successful 'hackathon' meetings the best approach was decided and implemented in a joint effort. I undertook the task of wrapping the procedure up into a Nextflow pipeline with a view to replicating the results across our .. (click here to read more)
Publication time acts as a snapshot for scientific work. Whether a project is ongoing or not, work which was performed months ago must be described, new software documented, data collated and figures generated.
The monumental increase in data and pipeline complexity has led to this task being performed to many differing standards, or lack of thereof. We all agree it is not good enough to simply note down the software version number. But what practical measures can be taken?
The recent publication describing Kallisto (Bray et al. 2016) provides an excellent high profile example of the growing efforts to ensure reproducible science in computational biology. The authors provide a GitHub repository that “contains all the analysis to reproduce the results in the kallisto paper”.
They should be applauded and indeed - in the Twittersphere - they were. The corresponding author Lior Pachter stated that the publication could be .. (click here to read more)
Recently a new feature has been added to Nextflow that allows failing jobs to be rescheduled, automatically increasing the amount of computational resources requested.
Nextflow provides a mechanism that allows tasks to be automatically re-executed when a command terminates with an error exit status. This is useful to handle errors caused by temporary or even permanent failures (i.e. network hiccups, broken disks, etc.) that may happen in a cloud based environment.
However in an HPC cluster these events are very rare. In this scenario error conditions are more likely to be caused by a peak in computing resources, allocated by a job exceeding the original resource requested. This leads to the batch scheduler killing the job which in turn stops the overall pipeline execution.
In this context automatically re-executing the failed task is useless because it would simply replicate the same error condition. A common solution consists of increasing .. (click here to read more)
As a new bioinformatics student with little formal computer science training, there are few things that scare me more than PhD committee meetings and having to run my code in a completely different operating environment.
Recently my work landed me in the middle of the phylogenetic tree jungle and the computational requirements of my project far outgrew the resources that were available on our institute’s Univa Grid Engine based cluster. Luckily for me, an opportunity arose to participate in a joint program at the MareNostrum HPC at the Barcelona Supercomputing Centre (BSC).
As one of the top 100 supercomputers in the world, the MareNostrum III dwarfs our cluster and consists of nearly 50'000 processors. However it soon became apparent that with great power comes great responsibility and in the case of the BSC, great restrictions. These include no internet access, restrictive wall times for jobs, longer queues, fewer .. (click here to read more)
The main goal of Nextflow is to make workflows portable across different computing platforms taking advantage of the parallelisation features provided by the underlying system without having to reimplement your application code.
From the beginning Nextflow has included executors designed to target the most popular resource managers and batch schedulers commonly used in HPC data centers, such as Univa Grid Engine, Platform LSF, SLURM, PBS and Torque.
When using one of these executors Nextflow submits the computational workflow tasks as independent job requests to the underlying platform scheduler, specifying for each of them the computing resources needed to carry out its job.
This approach works well for workflows that are composed of long running tasks, which is the case of most common genomic pipelines.
However this approach does not scale well for workloads made up of a large number of short-lived tasks (e.g. a few seconds .. (click here to read more)
In a recent publication we assessed the impact of Docker containers technology on the performance of bioinformatic tools and data analysis workflows.
We benchmarked three different data analyses: a RNA sequence pipeline for gene expression, a consensus assembly and variant calling pipeline, and finally a pipeline for the detection and mapping of long non-coding RNAs.
We found that Docker containers have only a minor impact on the performance of common genomic data analysis, which is negligible when the executed tasks are demanding in terms of computational time.
Innovation can be viewed as the application of solutions that meet new requirements or existing market needs. Academia has traditionally been the driving force of innovation. Scientific ideas have shaped the world, but only a few of them were brought to market by the inventing scientists themselves, resulting in both time and financial loses.
Lately there have been several attempts to boost scientific innovation and translation, with most notable in Europe being the Horizon 2020 funding program. The problem with these types of funding is that they are not designed for PhDs and Postdocs, but rather aim to promote the collaboration of senior scientists in different institutions. This neglects two very important facts, first and foremost that most of the Nobel prizes were given for discoveries made when scientists were in their 20's / 30's (not in their 50's / 60's). Secondly, innovation really happens when a few individuals (not .. (click here to read more)
The latest version of Nextflow introduces a new console graphical interface.
The Nextflow console is a REPL (read-eval-print loop) environment that allows one to quickly test part of a script or pieces of Nextflow code in an interactive manner.
It is a handy tool that allows one to evaluate fragments of Nextflow/Groovy code or fast prototype a complete pipeline script.
The console application is included in the latest version of Nextflow (0.13.1 or higher).
You can try this feature out, having Nextflow installed on your computer, by entering the following command in your shell terminal:
When you execute it for the first time, Nextflow will spend a few seconds downloading the required runtime dependencies. When complete the console window will appear as shown in the picture below.
It contains a text editor (the top white box) that .. (click here to read more)
Scientific data analysis pipelines are rarely composed by a single piece of software. In a real world scenario, computational pipelines are made up of multiple stages, each of which can execute many different scripts, system commands and external tools deployed in a hosting computing environment, usually an HPC cluster.
As I work as a research engineer in a bioinformatics lab I experience on a daily basis the difficulties related on keeping such a piece of software consistent.
Computing enviroments can change frequently in order to test new pieces of software or maybe because system libraries need to be updated. For this reason replicating the results of a data analysis over time can be a challenging task.
Docker has emerged recently as a new type of virtualisation technology that allows one to create a self-contained runtime environment. There are plenty of examples showing the benefits of using it to run .. (click here to read more)
The scientific world nowadays operates on the basis of published articles. These are used to report novel discoveries to the rest of the scientific community.
But have you ever wondered what a scientific article is? It is a:
Hence the very essence of Science relies on the ability of scientists to reproduce and build upon each other’s published results.
So how much can we rely on published data? In a recent report in Nature, researchers at the Amgen corporation found that only 11% of the academic research in the literature was reproducible by their groups .
While many factors are likely at play here, perhaps the most basic requirement for reproducibility holds that .. (click here to read more)
The GitHub code repository and collaboration platform is widely used between researchers to publish their work and to collaborate on projects source code.
Even more interestingly a few months ago GitHub announced improved support for researchers making it possible to get a Digital Object Identifier (DOI) for any GitHub repository archive.
With a DOI for your GitHub repository archive your code becomes formally citable in scientific publications.
The latest Nextflow release (0.9.0) seamlessly integrates with GitHub. This feature allows you to manage your code in a more consistent manner, or use other people's Nextflow pipelines, published through GitHub, in a quick and transparent manner.
The idea is very simple, when you launch a script execution with Nextflow, it will look for a file with the pipeline name you've specified. If that file does not exist, it will look for a public repository with .. (click here to read more)