Four simple recommendations to encourage best practices in research software

Recommendations for the packaging and containerizing of bioinformatics software

Software Containers are changing the way scientists and researchers develop, deploy and exchange scientific software. They allow labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. However, containers and software packages should be produced under certain rules and standards in order to be reusable, compatible and easy to integrate into pipelines and analysis workflows. Here, we presented a set of recommendations developed by the BioContainers Community to produce standardized bioinformatics packages and containers. These recommendations provide practical guidelines to make bioinformatics software more discoverable, reusable and transparent.  They are aimed to guide developers, organisations, journals and funders to increase the quality and sustainability of research software.

Recommendations for the packaging and containerizing of bioinformatics software

Software Containers are changing the way scientists and researchers develop, deploy and exchange scientific software. They allow labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. However, containers and software packages should be produced under certain rules and standards in order to be reusable, compatible and easy to integrate into pipelines and analysis workflows. Here, we presented a set of recommendations developed by the BioContainers Community to produce standardized bioinformatics packages and containers. These recommendations provide practical guidelines to make bioinformatics software more discoverable, reusable and transparent.  They are aimed to guide developers, organisations, journals and funders to increase the quality and sustainability of research software.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Using bio.tools to generate and annotate workbench tool descriptions

Workbench and workflow systems such as Galaxy, Taverna, Chipster, or Common Workflow Language (CWL)-based frameworks, facilitate the access to bioinformatics tools in a user-friendly, scalable and reproducible way. Still, the integration of tools in such environments remains a cumbersome, time consuming and error-prone process. A major consequence is the incomplete or outdated description of tools that are often missing important information, including parameters and metadata such as publication or links to documentation. ToolDog (Tool DescriptiOn Generator) facilitates the integration of tools - which have been registered in the ELIXIR tools registry (https://bio.tools) - into workbench environments by generating tool description templates. ToolDog includes two modules. The first module analyses the source code of the bioinformatics software with language-specific plugins, and generates a skeleton for a Galaxy XML or CWL tool description. The second module is dedicated to the enrichment of the generated tool description, using metadata provided by bio.tools. This last module can also be used on its own to complete or correct existing tool descriptions with missing metadata.

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.