An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy.

BACKGROUND: Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs, but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing. FINDINGS: An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs, and several petabytes of storage to rapidly prototype and develop end-to-end AI projects. Using a JupyterLab notebook, long-running AI model training programs can also be executed remotely to create trained models, represented in open neural network exchange (ONNX) format, and other output datasets in Galaxy. Other features include Git integration for version control, the option of creating and executing pipelines of notebooks, and multiple dashboards and packages for monitoring compute resources and visualization, respectively. CONCLUSIONS: These features make JupyterLab in Galaxy Europe highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions in COVID-19 computed tomography scan images is reproduced using various features of JupyterLab on Galaxy Europe. In addition, ColabFold, a faster implementation of AlphaFold2, is accessed in JupyterLab to predict the 3-dimensional structure of protein sequences. JupyterLab is accessible in 2 ways-one as an interactive Galaxy tool and the other by running the underlying Docker container. In both ways, long-running training can be executed on Galaxy's compute infrastructure. Scripts to create the Docker container are available under MIT license at https://github.com/usegalaxy-eu/gpu-jupyterlab-docker.

Distinct SARS-CoV-2 RNA fragments activate Toll-like receptors 7 and 8 and induce cytokine release from human macrophages and microglia.

Introduction: The pandemic coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is marked by thromboembolic events and an inflammatory response throughout the body, including the brain. Methods: Employing the machine learning approach BrainDead we systematically screened for SARS-CoV-2 genome-derived single-stranded (ss) RNA fragments with high potential to activate the viral RNA-sensing innate immune receptors Toll-like receptor (TLR)7 and/or TLR8. Analyzing HEK TLR7/8 reporter cells we tested such RNA fragments with respect to their potential to induce activation of human TLR7 and TLR8 and to activate human macrophages, as well as iPSC-derived human microglia, the resident immune cells in the brain. Results: We experimentally validated several sequence-specific RNA fragment candidates out of the SARS-CoV-2 RNA fragments predicted in silico as activators of human TLR7 and TLR8. Moreover, these SARS-CoV-2 ssRNAs induced cytokine release from human macrophages and iPSC-derived human microglia in a sequence- and species-specific fashion. Discussion: Our findings determine TLR7 and TLR8 as key sensors of SARS-CoV-2-derived ssRNAs and may deepen our understanding of the mechanisms how this virus triggers, but also modulates an inflammatory response through innate immune signaling.

Distinct SARS-CoV-2 RNA fragments activate Toll-like receptors 7 and 8 and induce cytokine release from human macrophages and microglia

Introduction The pandemic coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is marked by thromboembolic events and an inflammatory response throughout the body, including the brain Methods Employing the machine learning approach BrainDead we systematically screened for SARS-CoV-2 genome-derived single-stranded (ss) RNA fragments with high potential to activate the viral RNA-sensing innate immune receptors Toll-like receptor (TLR)7 and/or TLR8. Analyzing HEK TLR7/8 reporter cells we tested such RNA fragments with respect to their potential to induce activation of human TLR7 and TLR8 and to activate human macrophages, as well as iPSC-derived human microglia, the resident immune cells in the brain. Results We experimentally validated several sequence-specific RNA fragment candidates out of the SARS-CoV-2 RNA fragments predicted in silico as activators of human TLR7 and TLR8. Moreover, these SARS-CoV-2 ssRNAs induced cytokine release from human macrophages and iPSC-derived human microglia in a sequence- and species-specific fashion. Discussion Our findings determine TLR7 and TLR8 as key sensors of SARS-CoV-2-derived ssRNAs and may deepen our understanding of the mechanisms how this virus triggers, but also modulates an inflammatory response through innate immune signaling.

Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease.

We present several workflows for protein-ligand docking and free energy calculation for use in the workflow management system Galaxy. The workflows are composed of several widely used open-source tools, including rDock and GROMACS, and can be executed on public infrastructure using either Galaxy's graphical interface or the command line. We demonstrate the utility of the workflows by running a high-throughput virtual screening of around 50000 compounds against the SARS-CoV-2 main protease, a system which has been the subject of intense study in the last year.

A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive.

SUMMARY: Many aspects of the global response to the COVID-19 pandemic are enabled by the fast and open publication of SARS-CoV-2 genetic sequence data. The European Nucleotide Archive (ENA) is the European recommended open repository for genetic sequences. In this work, we present a tool for submitting raw sequencing reads of SARS-CoV-2 to ENA. The tool features a single-step submission process, a graphical user interface, tabular-formatted metadata and the possibility to remove human reads prior to submission. A Galaxy wrap of the tool allows users with little or no bioinformatic knowledge to do bulk sequencing read submissions. The tool is also packed in a Docker container to ease deployment. AVAILABILITY: CLI ENA upload tool is available at github.com/usegalaxy-eu/ena-upload-cli (DOI 10.5281/zenodo.4537621); Galaxy ENA upload tool at toolshed.g2.bx.psu.edu/view/iuc/ena_upload/382518f24d6d and https://github.com/galaxyproject/tools-iuc/tree/master/tools/ena_upload (development) and; ENA upload Galaxy container at github.com/ELIXIR-Belgium/ena-upload-container (DOI 10.5281/zenodo.4730785).