SpatialView: an interactive web application for visualization of multiple samples in spatial transcriptomics experiments.

MOTIVATION: Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments. RESULTS: To fill the gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10× Genomics Visium ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease. AVAILABILITY AND IMPLEMENTATION: SpatialView is available at https://github.com/kendziorski-lab/SpatialView (and https://doi.org/10.5281/zenodo.10223907); a demo application is available at https://www.biostat.wisc.edu/˜kendzior/spatialviewdemo/.

SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery.

SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporters based on molecule structure introduced as SMILES. Additionally, the application provides essential insights into critical attributes of potential drugs such as blood-brain barrier penetration and human intestinal absorption. The complexity of the serotonergic system demands advanced tools for accurate predictions, which is a fundamental requirement in drug development. SerotoninAI addresses this need by providing an intuitive user interface that generates predictions of pKi values for the main serotonergic targets. The application is freely available on the Internet at https://serotoninai.streamlit.app/, implemented in Streamlit with all major web browsers supported. Currently, to the best of our knowledge, there is no tool that allows users to access affinity predictions for serotonergic targets without registration or financial obligations. SerotoninAI significantly increases the scope of drug development activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.

ViralWasm: a client-side user-friendly web application suite for viral genomics.

MOTIVATION: The genomic surveillance of viral pathogens such as SARS-CoV-2 and HIV-1 has been critical to modern epidemiology and public health, but the use of sequence analysis pipelines requires computational expertise, and web-based platforms require sending potentially sensitive raw sequence data to remote servers. RESULTS: We introduce ViralWasm, a user-friendly graphical web application suite for viral genomics. All ViralWasm tools utilize WebAssembly to execute the original command line tools client-side directly in the web browser without any user setup, with a cost of just 2-3x slowdown with respect to their command line counterparts. AVAILABILITY AND IMPLEMENTATION: The ViralWasm tool suite can be accessed at: https://niema-lab.github.io/ViralWasm.

SRplot: A free online platform for data visualization and graphing.

Graphics are widely used to provide summarization of complex data in scientific publications. Although there are many tools available for drawing graphics, their use is limited by programming skills, costs, and platform specificities. Here, we presented a freely accessible easy-to-use web server named SRplot that integrated more than a hundred of commonly used data visualization and graphing functions together. It can be run easily using all Web browsers and there are no strong requirements on the computing power of users' machines. With a user-friendly graphical interface, users can simply paste the contents of the input file into the text box according to the defined file format. Modification operations can be easily performed, and graphs can be generated in real-time. The resulting graphs can be easily downloaded in bitmap (PNG or TIFF) or vector (PDF or SVG) format in publication quality. The website is updated promptly and continuously. Functions in SRplot have been improved, optimized and updated depend on feedback and suggestions from users. The graphs prepared with SRplot have been featured in more than five hundred peer-reviewed publications. The SRplot web server is now freely available at http://www.bioinformatics.com.cn/SRplot.

Developing anti-microbial peptide database version 1 to provide comprehensive and exhaustive resource of manually curated AMPs.

Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated resource that aims to address the limitations of existing databases in the field of antimicrobial research. We have utilized the latest technology and put our best efforts into adding all relevant tools to cater to the needs of our users. AMPDB v1 is a derived database, built upon information gathered from the available resources and boasts a significant size of 59,122 entries which are classified into 88 classes. All the information in this resource was curated manually. Sequence alignment and protein feature calculation tools were integrated into the database in the form of web applications, to make them easy to use, quick, and responsive in real-time. We have included multiple types of browsing and searching options to enhance the user experience, from simple text search to a completely customizable advanced search page with intuitive options that let the user combine multiple options together to make a powerful search query. The database is accessible by a web browser at https://bblserver.org.in/ampdb/ .

Fisheries data management systems in the NW Mediterranean: from data collection to web visualization.

The European Union Data Collection Framework (DCF) states that scientific data-driven assessments are essential to achieve sustainable fisheries. To respond to the DCF call, this study introduces the information systems developed and used by Institut Català de Recerca per a la Governança del Mar (ICATMAR), the Catalan Institute of Research for the Governance of the Seas. The information systems include data from a biological monitoring, curation, processing, analysis, publication and web visualization for bottom trawl fisheries. Over the 4 years of collected data (2019-2022), the sampling program developed a dataset of over 1.1 million sampled individuals accounting for 24.6 tons of catch. The sampling data are ingested into a database through a data input website ensuring data management control and quality. The standardized metrics are automatically calculated and the data are published in the web visualizer, combined with fishing landings and Vessel Monitoring System (VMS) records. As the combination of remote sensing data with fisheries monitoring offers new approaches for ecosystem assessment, the collected fisheries data are also visualized in combination with georeferenced seabed habitats from the European Marine Observation and Data Network (EMODnet), climate and sea conditions from Copernicus Monitoring Environment Marine Service (CMEMS) on the web browser. Three public web-based products have been developed in the visualizer: geolocated bottom trawl samplings, biomass distribution per port or season and length-frequency charts per species. These information systems aim to fulfil the gaps in the scientific community, administration and civil society to access high-quality data for fisheries management, following the Findable, Accessible, Interoperable, Reusable (FAIR) principles, enabling scientific knowledge transfer. Database URL  https://icatmar.github.io/VISAP/(www.icatmar.cat).

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).

GraphSNP: an interactive distance viewer for investigating outbreaks and transmission networks using a graph approach.

BACKGROUND: Cluster and transmission analysis utilising pairwise SNP distance are increasingly used in genomic epidemiological studies. However, current methods are often challenging to install and use, and lack interactive functionalities for easy data exploration. RESULTS: GraphSNP is an interactive visualisation tool running in a web browser that allows users to rapidly generate pairwise SNP distance networks, investigate SNP distance distributions, identify clusters of related organisms, and reconstruct transmission routes. The functionality of GraphSNP is demonstrated using examples from recent multi-drug resistant bacterial outbreaks in healthcare settings. CONCLUSIONS: GraphSNP is freely available at https://github.com/nalarbp/graphsnp . An online version of GraphSNP, including demonstration datasets, input templates, and quick start guide is available for use at https://graphsnp.fordelab.com .

Cloud Computing for Metagenomics: Building a Personalized Computational Platform for Pipeline Analyses.

Cloud Computing services such as Microsoft Azure, Amazon Web Services, and Google Cloud provide a range of tools and services that enable scientists to rapidly prototype, build, and deploy platforms for their computational experiments.This chapter describes a protocol to deploy and configure an Ubuntu Linux Virtual Machine in the Microsoft Azure cloud, which includes Minconda Python, a Jupyter Lab server, and the QIIME toolkit configured for access through a web browser to facilitate a typical metagenomics analysis pipeline.

An Annotation Workbench for Semantic Annotation of Data Collection Instruments.

Semantic interoperability, i.e., the ability to automatically interpret the shared information in a meaningful way, is one of the most important requirements for data analysis of different sources. In the area of clinical and epidemiological studies, the target of the National Research Data Infrastructure for Personal Health Data (NFDI4Health), interoperability of data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires is critical. Retrospective integration of semantic codes into study metadata at item-level is important, as ongoing or completed studies contain valuable information, which should be preserved. We present a first version of a Metadata Annotation Workbench to support annotators in dealing with a variety of complex terminologies and ontologies. User-driven development with users from the fields of nutritional epidemiology and chronic diseases ensured that the service fulfills the basic requirements for a semantic metadata annotation software for these NFDI4Health use cases. The web application can be accessed using a web browser and the source code of the software is available with an open-source MIT license.

Implementación de Navegadores de Pacientes en distintos sectores del "Hospital Nacional Profesor Alejandro Posadas" / Implementation of patient browsers in different sectors of the "National Hospital Professor Alejandro Posadas"

En el año 2020 en el Hospital Nacional Profesor Alejandro Posadas, cuando comenzó la pandemia de Covid 2019, se comenzó a realizar seguimiento clínico de pacientes y a sus contactos, en forma telefónica, debido a la gran demanda de asistencia y para evitar el desborde del sistema sanitario. Conformándose así el equipo de Seguimiento Telefónico. Consecuencia de aquello comprobamos que era una herramienta valiosa que podría ser utilizada en otros procesos. En el año 2021 el equipo de Seguimiento Telefónico continuó en funcionamiento adaptándose a nuevas líneas de trabajo en donde el monitoreo de la trazabilidad del cuidado de los pacientes es imprescindible para garantizarles acceso al sistema y al mismo tiempo realizar evaluación y monitoreo de procesos asistenciales que involucran a personas con criterios de vulnerabilidad y/o salud que les confieran riesgo. Este artículo explora el papel que puede desempeñar la "Navegación del Paciente" en la mejora de los resultados de salud1 . Los navegadores pueden facilitar un mejor acceso y calidad de la atención médica para las poblaciones desatendidas. En el mes de febrero de 2023 se institucionalizó el dispositivo de Seguimiento Telefónico como sector de Revinculación Asistencial, un arma más para beneficiar a los pacientes, aumentando su Seguridad y mejorando día a día la Calidad de atención.

In 2020, when the Covid-19 pandemic began, the Professor Alejandro Posadas National Hospital implemented clinical monitoring of patients and their contacts by telephone to manage the overwhelming demand for assistance and avoid overcrowding. This led to the formation of a Telephone Monitoring team. The team's success prompted the hospital to explore other areas where the tool could be applied. In 2021, the hospital continued to use the Telephone Monitoring team, adapting it to new lines of work where patient care traceability was critical for ensuring access to the healthcare system. This article examines the role of patient navigation in improving healthcare outcomes, including better access and quality of care for underserved populations. The hospital institutionalized the Telephone Monitoring device in February 2023 as a sector of Assistance Relinking, adding another weapon to benefit patients by increasing their safety and improving the quality of care.

JBrowse 2: a modular genome browser with views of synteny and structural variation.

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. It retains core features of JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. It allows users to share sessions, open multiple genomes, and navigate between views. It can be embedded in a web page, used as a standalone application, or run from Jupyter notebooks or R sessions. These improvements are enabled by a ground-up redesign using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR data.

Digital polymerase chain reaction (dPCR) is an emerging technology that enables accurate and sensitive quantification of nucleic acids. Most available dPCR systems have two channel optics, with ad hoc software limited to the analysis of single and duplex assays. Although multiplexing strategies were developed, variable assay designs, dPCR systems, and the analysis of low DNA input data restricted the ability for a universal automated clustering approach. To overcome these issues, we developed dPCR Cluster Predictor (dPCP), an R package and a Shiny app for automated analysis of up to 4-plex dPCR data. dPCP can analyse and visualize data generated by multiple dPCR systems carrying out accurate and fast clustering not influenced by the amount and integrity of input of nucleic acids. With the companion Shiny app, the functionalities of dPCP can be accessed through a web browser.

Visual dynamics: a WEB application for molecular dynamics simulation using GROMACS.

BACKGROUND: The molecular dynamics is an approach to obtain kinetic and thermodynamic characteristics of biomolecular structures. The molecular dynamics simulation softwares are very useful, however, most of them are used in command line form and continue with the same common implementation difficulties that plague researchers who are not computer specialists. RESULTS: Here, we have developed the VisualDynamics-a WEB tool developed to automate biological simulations performed in Gromacs using a graphical interface to make molecular dynamics simulation user-friendly task. In this new application the researcher can submit a simulation of the protein in the free form or complexed with a ligand. Can also download the graphics analysis and log files at the end of the simulation. CONCLUSIONS: VisualDynamics is a tool that will accelerate implementations and learning in the area of molecular dynamics simulation. Freely available at https://visualdynamics.fiocruz.br/login , is supported by all major web browsers. VisualDynamics was developed with Flask, which is a Python-based free and open-source framework for web development. The code is freely available for download at GitHub https://github.com/LABIOQUIM/visualdynamics .

CellTrackVis: interactive browser-based visualization for analyzing cell trajectories and lineages.

BACKGROUND: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. RESULTS: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. CONCLUSIONS: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial .

ARAX: a graph-based modular reasoning tool for translational biomedicine.

MOTIVATION: With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine-supporting that language-that federates semantically integrated knowledge-bases. RESULTS: We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface (API). ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user's query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX's application and utility in specific disease contexts, we present several use-case examples. AVAILABILITY AND IMPLEMENTATION: The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web API endpoint at arax.rtx.ai/api/arax/v1.3/ui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hybrid Genome Assembly of Short and Long Reads in Galaxy.

Galaxy is a web browser-based data analysis platform that is widely used in biology. Public Galaxy instances allow the analysis of data and interpretation of results without requiring software installation. NanoGalaxy is a public Galaxy instance with tools and workflows for nanopore data analysis. This chapter describes the steps involved in performing genome assembly using short and long reads in NanoGalaxy.

Google Académico: el buscador especializado para la ayuda a la investigación / Google Scholar: the specialized search engine for research assistance

Google Académico (Google Scholar) es un buscador bibliográfico gratuito, especializado en recuperar documentos científicos de múlti-ples fuentes y en varias disciplinas. Por ejem-plo, artículos científicos, tesis, patentes o libros, además de identificar las citas que éstos han recibido, ayudando a conocer el impacto de las publicaciones.Cabe destacar que Google Académico se com-plementa con Google Books, Google Patents, Google Scholar Metrics, citas de Google y con los enlaces a bibliotecas. Estas características hacen de Google Académico una herramienta ideal para buscar en diversas fuentes desde un solo sitio, convirtiéndose así en un potente com-petidor de otros motores de búsqueda e índices de citas.(AU)

Google Scholar is a free bibliographic search engine, specialized in retrieving scientific docu-ments from multiple sources and in various dis-ciplines. For example, scientific articles, theses, patents or books, as well as identifying the cita-tions they have received, helping to determine the impact of the publications.It should be noted that Google Scholar is com-plemented by Google Books, Google Patents, Google Scholar Metrics, Google citations and links to libraries. These features make Google Scholar an ideal tool for searching diverse sources from a single site, making it a powerful competitor to other search engines and citation indexes.(AU)

JBrowse Jupyter: a Python interface to JBrowse 2.

MOTIVATION: JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. RESULTS: We have developed a package that provides a Python interface to JBrowse 2's suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google's Colab notebooks, providing reproducible interactive views. AVAILABILITY AND IMPLEMENTATION: JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter.