BioExcel Building Blocks - Workflow Managers
Workflows built using the BioExcel Building Blocks are compatible and can be described and deployed using different workflow managers. Adapter layers (biobb_adapters) for a number of workflow managers have been developed, whereas new controllers can be easily added following the same approach.
Workflows built using the BioExcel Building Blocks have been tested in different workflow managers: Common Workflow Language, PyCOMPSs, Galaxy, KNIME and Jupyter Notebooks.
Common Workflow Language (CWL) is an open standard for describing analysis workflows and tools, and offers portability, scalability and reproducibility thanks to its workflow descavailability/tutorials/cwlription and specification language. [Example]
PyCOMPSs is a workflow manager designed for the HPC regime, and is used to control extremely parallelizable workflows. It facilitates the development of parallel computational workflows in Python, exploiting the inherent concurrency of the script, detecting the data dependencies between tasks and spawning them to the available resources. [Example]
Galaxy is a scientific workflow platform offering a web-based drag and drop graphical user interface that aims to make computational biology accessible to research scientists that do not have computer programming experience. Initially developed for genomics research, it is nowadays used in the whole bioinformatics field, and is approaching the structural domain. [Example]
KNIME analytics platform is a scientific workflow platform offering a standalone drag and drop graphical user interface to create visual workflows. It is focused on data science and analytics, with a large number of Machine Learning tools integrated. KNIME is largely used by pharmaceutical companies. [Example]
Jupyter Notebooks are web-based, interactive applications with which programmers can develop, document, execute code, and share results. The application is ultimately acting as a Graphical User Interface (GUI) that allows the graphical representation and visualization of charts, tables, molecular visualizers, and in general any kind of integration offered by the compatible libraries. Jupyter Notebooks are perfect for educational purposes, in the form of tutorials made of interactive programming code accompanied by text information and/or documentation, versatile graphical charts and data visualization. [Example]
Google Colab
Colab, or "Colaboratory", allows you to write and execute Python in your browser, with: zero configuration required, access to GPUs free of charge and easy sharing. Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Colab is particularly well suited to machine learning, data science, and education. [Example]