Standards articles from across Nature Portfolio

Riboformer is a deep learning-based framework that predicts changes in translation dynamics with codon-level precision. It corrects experimental artifacts in ribosome profiling data and identifies sequences causing ribosome stalling.

Reproducibility is essential for the progress of research, yet achieving it remains elusive even in computational fields. Here, authors develop the rworkflows suite, making robust CI/CD workflows easy and freely accessible to all R package developers.

A benchmarking study evaluates 11 mosaic single-nucleotide variant and insertion-deletion mutation detection approaches.

As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.

We conducted a comprehensive long-read RNA sequencing (RNA-seq) benchmarking experiment by combining spike-ins and in silico mixtures to establish a ground-truth dataset. We used long- and short-read RNA-seq technology to deeply sequence samples and compared the performance of a range of analysis tools on these data.

New traceability and reporting standards aim to improve transparency in stem cell research.

The language used by microscopists who wish to find and measure objects in an image often differs in critical ways from that used by computer scientists who create tools to help them do this, making communication hard across disciplines. This work proposes a set of standardized questions that can guide analyses and shows how it can improve the future of bioimage analysis as a whole by making image analysis workflows and tools more FAIR (findable, accessible, interoperable and reusable).

Issues with data reuse have been recognized in synthetic biology and the broader scientific community. Policies and standards fall short as machine reasoning is not emphasised and enforcement is lacking. We discuss the progress, remaining challenges, and possible solutions.