Background Tandem repeats (TRs) are major sources of genetic variation and important genetic markers. Their expansions are not only involved in gene expression regulation but also associated with…

Background Phylogenetic trees are essential diagrams used in different sciences, such as evolutionary biology or taxonomy, and they depict the relationships between a given set of taxa sharing a…

Background Dot-plots, along with linear comparisons, are fundamental visualization methods in genome comparisons, widely used for analyzing structural variations, repeat regions, and sequence…

Background Untargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectr...

Understanding how cells transform into specialized types during human development is a central challenge in biology. This complex process, known as cell differentiation, holds the key to understanding...

Background When genes are translated into proteins, mutations in the gene sequence can lead to changes in protein structure and function as well as in the interactions between proteins. These changes…

Background Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance f...

Background Interpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still re...

BMC Bioinformatics - Ultra High Resolution (UHR) mass spectrometry systems with Fourier Transform Ion Cyclotron Resonance (FT-ICR) are often used to analyze the composition of complex mixtures of...

Background Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease...

BMC Bioinformatics - Analysing large-scale mass spectrometry-based complex proteomics datasets often overwhelm desktop computational resources and require manual configuration for analysis. While...

Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.