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Aim The Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time‐consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for underdocumented areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here, we present IUCNN, an R‐package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other data if available). Innovation We implement a user‐friendly workflow to train and validate neural network models, and use them to predict species RL status. IUCNN contains specific functions for extinction risk prediction in the RL framework, including a regression‐based approach to account for the ordinal nature of RL categories, a Bayesian approach for improved uncertainty quantification and a convolutional neural network to predict species RL status based on their raw geographic occurrences. Most analyses run with few lines of code, not requiring users to have prior experience with neural network models. We demonstrate the use of IUCNN on an empirical dataset of ~14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods based on species occurrence information. Main conclusions IUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large‐scale automated assessments of the extinction risk of species so far not well represented on the official RL.

g:Profiler ( https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

Heatmap is a widely used statistical visualization method on matrix‐like data to reveal similar patterns shared by subsets of rows and columns. In the R programming language, there are many packages that make heatmaps. Among them, the ComplexHeatmap package provides the richest toolset for constructing highly customizable heatmaps. ComplexHeatmap can easily establish connections between multisource information by automatically concatenating and adjusting a list of heatmaps as well as complex annotations, which makes it widely applied in data analysis in many fields, especially in bioinformatics, to find hidden structures in the data. In this article, we give a comprehensive introduction to the current state of ComplexHeatmap, including its modular design, its rich functionalities, and its broad applications. Complex heatmap is a powerful visualization method for revealing associations between multiple sources of information. We have developed an R package named ComplexHeatmap that provides comprehensive functionalities for heatmap visualization. It has been widely used in the bioinformatics community. We give a comprehensive introduction to the current state of ComplexHeatmap in this article. Highlights Complex heatmap is a powerful visualization method for revealing associations between multiple sources of information. We have developed an R package named ComplexHeatmap that provides comprehensive functionalities for heatmap visualization. It has been widely used in the bioinformatics community. We give a comprehensive introduction to the current state of ComplexHeatmap in this article.

The network analysis has attracted increasing attention and interest from ecological academics, thus it is of great necessity to develop more convenient and powerful tools. For that reason, we have developed an R package, named “ggClusterNet,” to complete and display the network analysis in an easier manner. In that package, ten network layout algorithms are designed to better display the modules of microbiome network (randomClusterG, PolygonClusterG, PolygonRrClusterG, ArtifCluster, randSNEClusterG, PolygonModsquareG, PolyRdmNotdCirG, model_Gephi.2, model_igraph, and model_maptree). For the convenience of the users, many functions related to microbial network analysis, such as corMicor(), net_properties(), node_properties(), ZiPiPlot(), random_Net_compate(), are integrated to complete the network mining. Furthermore, the pipeline function named network.2() and corBionetwork() are also added for the quick achievement of the network or bipartite network analysis as well as their in‐depth mining. The ggClusterNet is publicly available via GitHub (https://github.com/taowenmicro/ggClusterNet/) or Gitee (https://gitee.com/wentaomicro/ggClusterNet) for users' access. A complete description of the usages can be found on the manuscript's GitHub page (https://github.com/taowenmicro/ggClusterNet/wiki). Highlights ggClusterNet is an R package for microbial networks. Analysis functions could help the user to easily complete network analysis and interpretation. Ten network layout algorithms allow users more alternatives to plot the network and generate published‐ready figures. It is free to access on GitHub and Gitee. ggClusterNet is an R package for microbial networks analysis and interpretation. Ten network layout algorithms  allow users more alternatives to plot the network and generate published‐ready figures. It is free to access on GitHub and Gitee.

R package pcadapt is a user-friendly R package for performing genome scans for local adaptation. Here, we present version 4 of pcadapt which substantially improves computational efficiency while providing similar results. This improvement is made possible by using a different format for storing genotypes and a different algorithm for computing principal components of the genotype matrix, which is the most computationally demanding step in method pcadapt. These changes are seamlessly integrated into the existing pcadapt package, and users will experience a large reduction in computation time (by a factor of 20–60 in our analyses) as compared with previous versions.

Almost 100 years after the dawn of insect bioacoustics, freely available and robust analysis tools and protocols are still needed. We introduce Rthoptera, an open‐source R package designed for robust analysis of insect acoustic signals. It delivers accurate temporal and spectral measurements, along with intuitive visualisations, through a streamlined workflow. Interactive applications ensure accessibility for researchers across all levels of technical expertise. Four new acoustic metrics for discrete units are introduced: Spectral Excursion, Pattern Complexity Index, Temporal Excursions and Dynamic Excursion. A Broadband Activity Index with potential use in pest monitoring is also showcased. Accompanied by an appropriate recording protocol, our workflow can become part of a standard analysis protocol for insect bioacoustics. Resumen Casi 100 años después del inicio de la bioacústica de insectos, todavía se necesitan herramientas y protocolos de análisis robustos y de libre acceso. Presentamos Rthoptera, un paquete de R de código abierto diseñado para el análisis robusto de señales acústicas de insectos. Ofrece mediciones temporales y espectrales precisas, junto con visualizaciones intuitivas, mediante un flujo de trabajo optimizado. Las aplicaciones interactivas garantizan su accesibilidad para investigadores de todos los niveles de experiencia técnica. Se introducen cuatro nuevas métricas acústicas para unidades discretas: Excursión Espectral, Índice de Complejidad de Patrón, Excursión Temporal y Excursión Dinámica. También se presenta un Índice de Actividad de Banda Ancha con potencial uso en monitoreo de plagas. Acompañado de un protocolo de grabación adecuado, nuestro flujo de trabajo puede convertirse en parte de un protocolo estándar de análisis para bioacústica de insectos.

Background When applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to propose a straightforward and robust approach to obtain IPD from published survival curves with a user-friendly software platform. Results Improving upon existing methods, we propose an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we developed the R package IPDfromKM and an accompanying web-based Shiny application. Both the R package and Shiny application have an “all-in-one” feature such that users can use them to extract raw data coordinates from published K-M curves, reconstruct IPD from the extracted data coordinates, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the basis of the reconstructed IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real-world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, and hazard ratios. Conclusions IPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD and advance the use of secondary data to facilitate informed decision making in medical research.

Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between universities, the effect of state-owned science funding on national research and development performance and educational efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis and visualization tools. The included tools were divided into three categories: general bibliometric and performance analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and scientometric analysis, they have been developed for a different purpose. The number of exportable records is between 500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny. VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to decide the desired analysis output and chose the option that better fits into their aims.

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals. MoBPS utilizes a highly efficient implementation with bit-wise data storage and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations. Individual haplotypes are not stored but instead automatically derived based on points of recombination and mutations. The modular structure of MoBPS allows to combine rather coarse simulations, as needed to generate founder populations, with a very detailed modeling of todays’ complex breeding programs, making use of all available biotechnologies. MoBPS provides pre-implemented functions for common breeding practices such as optimum genetic contributions and single-step GBLUP but also allows the user to replace certain steps with personalized and/or self-written solutions.

With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.