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Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

This Perspective discusses the registered and publicly available set of Cytoscape plugins to guide potential users to suitable tools. Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5–2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.

A collection of web-browser plug-ins is making the scholarly literature more discoverable.

Complex systems such as the global climate, biological organisms, civilisation, technical or social networks exhibit diverse behaviours at various temporal and spatial scales, often characterized by nonlinearity, feedback loops, and emergence. These systems can be characterized by physical quantities such as entropy, information, chaoticity or fractality rather than classical quantities such as time, velocity, energy or temperature. The drawback of these complexity quantities is that their definitions are not always mathematically exact and computational algorithms provide estimates rather than exact values. Typically, evaluations can be cumbersome, necessitating specialized tools. We are therefore introducing ComsystanJ, a novel and user-friendly software suite, providing a comprehensive set of plugins for complex systems analysis, without the need for prior programming knowledge. It is platform independent, end-user friendly and extensible. ComsystanJ combines already known algorithms and newer methods for generalizable analysis of 1D signals, 2D images and 3D volume data including the generation of data sets such as signals and images for testing purposes. It is based on the framework of the open-source image processing software Fiji and ImageJ2. ComsystanJ plugins are macro recordable and are maintained as open-source software. ComsystanJ includes effective surrogate analysis in all dimensions to validate the features calculated by the different algorithms. Future enhancements of the project will include the implementation of parallel computing for image stacks and volumes and the integration of artificial intelligence methods to improve feature recognition and parameter calculation.

Background Shapefiles are a geospatial vector data format used to indicate geographic features in geographic information systems (GIS) software. Shapefiles are used in high-throughput phenotyping plant breeding and agronomic studies to identify plots from aerial imagery and extract remote sensing data. However, the process of manually creating shapefiles is tedious and error prone. Current options that assist in shapefile generation suffer from issues such as installation processes that require a degree of programming knowledge or inefficient methods for incorporating plot-level information from field books. In this study, we have developed a program called ‘SHP Buddy’, a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research. Results SHP Buddy is a free and open source QGIS plugin that is easily downloaded directly from the QGIS plugin repository. It provides options for generating serpentine replicated and unreplicated experimental layouts. Further, SHP Buddy is the first of its type to provide an intuitive method for removing non-experimental plots, such as non-experimental “fill” plots at the end of experiments or plots in irrigation wheel tracks. Plot information is easily incorporated by uploading a field book CSV file that contains a column of matching plot numbers. Lastly, plot dimensions can be modified to produce more precise regions of interest. Conclusions SHP Buddy substantially reduces the time and increases the accuracy of shapefile generation. This results in reliable shapefiles that improve record keeping and the quality of high-throughput phenotyping data extracted. By working natively in QGIS, SHP Buddy provides an efficient solution to shapefile generation while maintaining a low learning curve.

The generation of videos is crucial, particularly in the medical field, where a significant amount of data is presented in this format. However, due to the extensive memory requirements, creating high-resolution videos poses a substantial challenge for generative models. In this paper, we introduce the Memory Efficient Video GAN (MeVGAN)–a Generative Adversarial Network (GAN) that incorporates a plugin-type architecture. This system utilizes a pre-trained 2D-image GAN, to which we attach a straightforward neural network designed to develop specific trajectories within the noise space. These trajectories, when processed through the GAN, produce realistic videos. We deploy MeVGAN specifically for creating colonoscopy videos, a critical procedure in the medical field, notably helpful for screening and treating colorectal cancer. We show that MeVGAN can produce good quality synthetic colonoscopy videos, which can be potentially used in virtual simulators.

Background The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. Results We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availability NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE .