INB/ELIXIR-ES nodes and TransBioNet members effort on COVID-19 research

INB/ELIXIR-ES nodes and TransBioNet members effort on COVID-19 research


INB/ELIXIR-ES nodes and TransBioNet members effort on COVID-19 research is a collection of databases, public repositories, tools, scientific publications, consortia and research projects of the INB/ELIXIR-ES nodes and TransBioNet members across Spain supporting global SARS-CoV-2 and COVID-19 research and knowledge. This page will be updated frequently (last updated February 18th, 2020). If you have questions about the information contained in this page, please contact INB coordination node at or to each individual group leader.


  • BIMCV COVID-19 dataset: The Valencia Region Image data Bank (BIMCV) offer medical images data from the RX and CT scans based on COVID-19 pathology to provide the open scientific community with data of clinical-scientific value that helps early detection of COVID-19. The Dataset will be available through the collaboration of TransBioNet and the INB/ELIXIR-ES Coordination Node hosted at the BSC.
  • BioExcel-CV19: a platform designed to provide web-access to atomistic-MD trajectories for macromolecules involved in the COVID-19 disease. The project is part of the open access initiatives promoted by the world-wide scientific community to share information about COVID-19 research. BioExcel-CV19 web server interface presents the resulting trajectories, with a set of quality control analyses and system information. All data produced by the project is available to download from an associated programmatic access API. Resource developed as part of the BioExcel CoE by the Molecular Modeling and Bioinformatics Unit led by Modesto Orozco at IRB Barcelona and the INB/ELIXIR-ES Computational node led by Josep Lluís Gelpí at BSC.
  • Chemical Checker (CC): a resource that provides processed, harmonized and integrated bioactivity data for 1M small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of the chemical similarity between compounds to similarities between bioactivity signatures of different kinds. These are exploited now to identify compounds that display similar properties to those suggested to be effective to treat COVID-19, thereby expanding the portfolio of molecules of interest. Besides, thanks to a collaboration with the Amazon Search group, it has been incorporated literature support by automatically processing the growing corpus of COVID-19 related articles. Automatically it has been read over 10,000 scientific articles related to Covid-19 (4,000 of which appeared in the last weeks), and extracted the relevant treatment information that is then fed into the Chemical Checker. This resource is automatically updated every day and is intended for research only. Please, help to improve the search engine and contribute drug annotations. Resource developed Structural Bioinformatics and Network Biology group led by Patrick Aloy at IRB Barcelona.
  • Coronavirus Phylomes: PhylomeDB includes a full phylogenomic analysis of 60 coronaviruses genomes, including SARS-Cov2, SARS and MERS. Gene phylogenetic trees and multiple sequence alignments can be browsed and downloaded. This resource is developed by the Comparative Genomics group led by Toni Gabaldón at BSC and IRB Barcelona.


  • 3DBIONOTES-COVID19 tool: the resource is integrated into 3DBIONOTES-WS, a web-based application for automatically annotating and visualising biomedical information ranging from immune epitopes sites to genomics data. The new 3DBIONOTES-COVID19 tool will enable the study of antiviral targets and the visualisation and annotation of SARS-CoV-2 protein structures. The tool contains SARS-CoV-2 protein models from numerous sources: PDB-Redo, SwissProt, AlphaFold, European Projects and the repository of the Coronavirus Structural Taskforce. The 3DBIONOTES Application Programming Interface (API) is an ELIXIR Recommended Interoperability Resource and has been highlighted in the ELIXIR COVID-19 resources. This tool is developed by the Biocomputing Unit led by JM Carazo at CNB, and in cooperation with INB/ELIXIR-ES and Instruct-ERIC (Instruct Image Processing Centre).
  • CoV-Hipathia: a web tool implementing a mechanistic model of human signalling for the interpretation of the consequences of the combined changes of gene expression levels and/or genomic mutations in the context of signalling pathways. It includes first versions of affected pathways by Covid-19. Adapted tool developed by Clinical Bioinformatics Area led by Joaquin Dopazo at FPS.
  • COVID-19 Flow-Maps: tool to visualize the relationship between the citizens’ mobility and the risk of Covid-19 spreading. The platform monitors COVID-19 outbreaks and mobility-associated risk by integrating health information, population-level mobility patterns into a Geographical Information System. The tool can serve as support to the administrations, for decision making; to epidemiologists, to feed their models with updated data, and to citizens, to visualize the relationship of the pandemic, mobility and its associated risk. COVID-19 Flow-Maps is an initiative of the Department of Life Sciences of the Barcelona Supercomputing Center (BSC) with an important contribution from those belonging to the INB/ELIXIR-ES.
  • COVID-19 Viral Beacon: tool to find SARS-CoV-2 variability at genomic, amino acid and motif level. It offers the possibility to (i) search in detail about the genomic variants, (ii) filter queries and find unique cases, (iii) filter strain/country-specific variants, (iv) explore associated metadata and much more. It has been developed as a branch of the GA4GH Beacon standard, as a special use case for testing and demonstration of new features in Beacon v2 (and implicitly of Beacon v1). The tool has been developed by the EGA team led by Jordi Rambla at the Center for Genomic Regulation (CRG).
  • DatAC (Data Against COVID-19): a publicly available platform that integrates information from COVID-19 with information on environmental and meteorological factors with temporal space aggregation by Spanish provinces and autonomous communities. Not only does DatAC centralize and integrate this type of data, but also implements different analysis and visual exploration functionalities that allow researchers to analyse and look for patterns among the different data sources. DatAC has been developed by the Bioinformatics Unit (TransBioNet member) led by Pedro Carmona Sáez at GENyO and the Statistics Department of the University of Granada.

  • DisGeNET and COVID-19 literature platform: As part of the community efforts to structure the newly emerging knowledge about the COVID-19 pandemic, a text mining resource on COVID-19 has been developed. Since the epidemic outbreak, there has been a daily increase in publications containing information about COVID-19. The text mining pipeline used in DisGeNET has been customized to scan the literature and identify mentions of COVID-19, the SARS-CoV-2 virus and other concomitant diseases and symptoms. This information is being updated regularly and is publicly available. This resource can support the investigation of risk factors relevant for the pandemic, as well as of the variety of symptoms observed in different populations or the occurrence of concomitant diseases. This development is possible thanks to the support of MedBioinformatics Solutions. DisGeNeT is an ELIXIR Recommended Interoperability Resource and part of the ELIXIR COVID-19 resources. Developed by F. Ronzano, J. Piñero, J. Saüch, L. Furlong from the Integrative Biomedical Informatics group led by Ferran Sanz at IMIM-UPF.
  • EPIDEMIXS Coronavirus: web app makes available to the community (general public and professionals) trustworthy sources of information related to the COVID-19 at the national level. M.A. Mayer is content contributor and expert advisor, from the Integrative Biomedical Informatics, group led by Ferran Sanz at IMIM-UPF.
  • SARS-CoV-2 training with galaxy: self-paced training containing all the information necessary to follow the steps to analyze SARS-CoV-2 data with Galaxy. The tutorial has been developed by the Core bioinformatics Unit (TransBioNet member) led by Isabel Cuesta at of Institute of Health Carlos III.

  • Viralrecon: bioinformatics analysis pipeline used to perform assembly and intra-host/low-frequency variant calling for viral samples. The pipeline supports short-read Illumina sequencing data from both shotgun and enrichment-based library preparation methods. The pipeline is built using Nextflow, and it comes with Docker containers making installation trivial and results highly reproducible. Furthermore, automated continuous integration tests that run the pipeline on a full-sized dataset using AWS cloud ensure that the code is stable. The scripts have been written by the Core bioinformatics Unit (TransBioNet member) led by Isabel Cuesta at of Institute of Health Carlos III (ISCIII) and updated through collaboration with the nf-core community.


  • COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms. Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G. Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E. Ackerman, Jason Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, D. A. B. Rex, Denise Slenter,  Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic-Milacic, Andrea Senff Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean-Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W. Overall, Dieter Maier, Angela Bauch, Benjamin M. Gyori, John A. Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C. Freeman, Franck Augé, Jacques S. Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L. Wilighagen, Alexander R. Pico, Chris T. Evelo, Marc E. Gillespie, Lincoln D. Stein, Henning Hermjakob, Peter D’Eustachio, Julio Saez-Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, the COVID-19 Disease Map Community.
  • COVID-19 Outcomes in 4712 consecutively confirmed SARS-CoV2 cases in the city of Madrid. Sarah Heili-Frades, Pablo Minguez, Ignacio Mahillo-Fernandez, Tomas Prieto-Rumeau, Antonio Herrero Gonzalez, Lorena de la Fuente, Maria Jesus Rodriguez Nieto, German Peces-Barba Romero, Mario Peces-Barba, Maria del Pilar Carballosa de Miguel, Itziar Fernandez Ormaechea, Alba Naya Prieto, Farah Ezzine de Blas, Luis Jimenez Hiscock, Cesar Perez Calvo, Arnoldo Santos, Luis Enrique Munoz Alameda, Fredeswinda Romero Bueno, Miguel Gorgolas Hernandez-Mora, Alfonso Cabello Ubeda, Beatriz Alvarez Alvarez, Elizabet Petkova, Nerea Carrasco, Dolores Martin Rios, Nicolas Gonzalez Mangado, Olga Sanchez Pernaute. medRxiv 2020.05.22.20109850; doi:
  • Mental health impact of the first wave of COVID-19 pandemic on Spanish healthcare workers: A large cross-sectional survey. Jordi Alonso, Gemma Vilagut, Philippe Mortier, Montse Ferrer, Itxaso Alayo, Andrés Aragón-Peña, Enric Aragonès, Mireia Campos, Isabel D. Cura-González, José I. Emparanza, Meritxell Espuga, Maria João Forjaz, Ana González-Pinto, Josep M. Haro, Nieves López-Fresneña, Alma D. Martínez de Salázar, Juan D. Molina, Rafael M. Ortí-Lucas, Mara Parellada, José Maria Pelayo-Terán, Aurora Pérez-Zapata, José I. Pijoan, Nieves Plana, Maria Teresa Puig, Cristina Rius, Carmen Rodríguez-Blázquez, Ferran Sanz, Consol Serra, Ronald C. Kessler, Ronny Bruffaerts, Eduard Vieta, Víctor Pérez-Solà, MINDCOVID Working group (2020). Revista de psiquiatria y salud mental, S1888-9891(20)30128-2. Advance online publication.


  • BSC, IrsiCaixa, CReSA and Grifols ConsortiumComputational Biology group led by Alfonso Valencia at BSC is involved in this consortium. The objective is to find an antiviral drug for people who have already been infected with the SARS-CoV-2 coronavirus, and to design and test a vaccine that works against all coronaviruses. The BSC will carry out the bioinformatics predictions to get more detailed information on how the virus’ S proteins interact with receptors in human cells.
    • COVID-19 Disease MapsClinical Bioinformatics Area led by Joaquin Dopazo at FPS participates in this international consortium, as well as Janet Piñero and Laura Furlong from the Integrative Biomedical Informatics, led by Ferran Sanz at IMIM-UPF, that contribute to the annotation of genes and variants to diseases with DisGeNET. The project has created the page COVID-19 Map Curation where they share resources and best practices to develop a COVID-19 disease map. They aim to establish a knowledge repository on mechanisms of COVID-19 as a broad community-driven effort. The COVID-19 disease map is an assembly of molecular interaction diagrams, established based on literature evidence. They focus on host-pathogen interactions, specific to the SARS-CoV-2 virus.
    • COVID19 SD - Down Syndrome with COVID-19 - M.A. Mayer is researcher and member of the Steering Committee of this international research project, he is also part of the Integrative Biomedical Informatics group led by Ferran Sanz at IMIM-UPF. This international research project is led by Mara Dierssen (group leader at CRG) and promoted by the Trisomy 21 Research Society (T21RS). The outbreak of Coronavirus (COVID-19) may cause additional challenges for people who may be vulnerable with certain health conditions. While there is no evidence about how people with Down syndrome are affected by coronavirus disease (COVID-19) versus another population, overall, people with Down syndrome may have some factors influencing how coronavirus can negatively affect their health. To better understand the risk and to inform appropriate recommendations to protect individuals with Down syndrome against COVID-19, the T21RS has launched an initiative in Europe, US and South America, based on a rapid international survey for carers and clinicians caring for individuals who have been diagnosed with COVID-19.
    • CovSeq (Sequencing of the SARS-CoV-2 virus genome for the monitoring and management of the Covid-19 epidemic in Andalusia and the rapid generation of prognostic and response to treatment biomarkers). A consortium, made up of 14 hospitals covering all the Andalusian provinces, the 5 Andalusian Health Research Institutes, the Technical Sub-Directorate for Information Management, the General Public Health Directorate (Epidemiological Surveillance and Occupational Health Service) and the Progress and Health Foundation (Joaquin Dopazo, Clinical Bioinformatics Area, FPS). The differences in the spread, severity of symptoms, mortality and other characteristics of COVID-19 are due to a combination of epidemiological factors, although it is to be expected that the genetics of the virus will play a very important role, which is still completely unknown. The consortium intends to use the network of diagnostic centers of the Andalusian community and the sequencers of the Health Research Institutes and associated centers to sequence about 1,000 samples of the SARS-CoV-2 virus covering all of Andalusia and representing a balanced sample of the different types of patients (age, sex, previous complications, previous treatments), as well as the different clinical conditions observed and the responses to the treatments. The genomic data associated with these clinical data, which can be enriched with information from the Population Health Base, will be used to quickly obtain diagnostic and prognostic biomarkers. In addition, they will be used to carry out epidemiological monitoring of the disease in real time. And finally, they will allow the development of a genomic and clinical Big Data computing resource for future research projects. The project will demonstrate the usefulness of virus genomic sequencing as a real-time translational application tool for this epidemic and others to come.
    • Double strategy to inhibit SARS-CoV-2 infectious cycle (Doble estratègia per inhibir el cicle infecciós de SARS-CoV-2) - Jana Selent from Integrative Biomedical Informatics, group led by Ferran Sanz at IMIM-UPF, is the leader of the project granted by the Direcció General de Recerca i Innovació en Salut (DGRIS) from the Generalitat of Catalonia in collaboration with AQuAS and Biocat. The goal is to massively analyze potential drugs that prevent the entry of SARS-CoV-2 into the cell using 3D computational models and cell assays. This is to identify antiviral drugs that interfere with the SARS-CoV2 infectious cycle by blocking two different phases of virus entry into the human cell. The group seeks to accelerate the discovery of antiviral drugs through the "drug re-profiling" strategy that allows for reallocation of existing drugs under COVID-19 Treatment. The strength of the project lies in a multidisciplinary approach (ranging from computational and structural biology to cell-based experimental validation) combined with a dual drug discovery strategy.
    • Dynamics of profiling and identification of gene expression of patients at high risk for severe COVID-19 (Dinàmica del perfilat i identificació d'expressions gèniques de pacients d'alt risc per COVID-19 greu) - Lara Nonell from MARGenomics (TransBioNet member) collaborates in the project led by IDIBELL and granted by the Direcció General de Recerca i Innovació en Salut (DGRIS) of the Generalitat of Catalonia in collaboration with AQuAS and Biocat. The main objective is to determine the dynamic transcriptomic profile of the adult patient hospitalized by COVID-19 and to characterize the subgroup that develops the severe illness (acute respiratory distress syndrome, SDRA). Samples will be processed by NGS and data analyzed by MARGenomics.
    • Exploring Covid19 Infectious Mechanisms and Host Selection Process. The project awarded in the PRACE Fast Track Call is coordinated by Modesto Orozco (Molecular Modeling and Bioinformatics Unit at IRB Barcelona), to work with Núria López-Bigas (Biomedical Genomics lab at IRB Barcelona), Josep Lluís Gelpí (INB/ELIXIR-ES Computational node at BSC), another computational team at BSC and experimental groups in Marseille and Milan. The 6-month project has been awarded 6.000.000 core hours on Joliot-Curie Rome hosted by GENCI at CEA, France.
      The project aims to understand the evolutionary path driving the virus from bat to humans, predict differential human sensitivity to infection and the impact of virus mutations in infectivity. The goal is to predict potential new variants of the virus emerging in a second infective wave and their potential of infectivity.
      The objectives of this project is to fight Covid19 and proximal strains now confined in other mammals: the project aims to anticipate virus’s next move and clarify the zoonotic pathway used by virus, its mutational space, as well as to understand different susceptibility to infection of human population and predict genomic changes impacting infectiveness. Molecular dynamics (MD) simulations will provide information on potential cavities in the variants of viral proteins which can be tackled by small drugs. They will focus on the entrance of the virus into the host cell, and particularly in the mechanisms linked to RBD recognition by ACE2 and CD147. We aim to determine the impact of genetic changes in the viral RBD and in ACE2/RBD in the recognition of the virus.
    • EXSCALATE4CoV - Computational Biology group led by Alfonso Valencia at BSC is involved in this consortium. The project aims to exploit the EXSCALATE Platform along with the most powerful computing resources currently based in Europe to empower smart in-silico drug design. EXSCALATE4COV is a fully integrated network built to have a continuous run of in silico simulations followed by in vitro experimental validation that speedup the identification of active compounds to be tested in humans as novel treatments for Covid19. Main task of Alfonso Valencia, Alba Lepore and Victor Guallar (BSC), is the Big data analysis of molecular dynamics simulations of the viral proteins and the ultrafast virtual screening of the E4C library, using supercomputing facilities. Phylogenetic analysis of COVID-19, Detection of specificity determining regions involved in host adaptation and Molecular Co-evolution Analysis and identification of host-pathogen interactions.
    • IRB and UniSRMolecular Modeling and Bioinformatics Unit led by Modesto Orozco at IRB Barcelona works on understanding how the virus has evolved by comparing its structure/genome to other coronaviridae including GATg13, SARS-CoV as well as the US-variant. Hence, to extensive unbiased and enhanced sampling MD simulations, as well as bioinformatic analysis, work aims at the unveiling the virus evolution and host selection mechanism by identifying key structural determinants and pathogenic mutations that occurred from one viral strain to the others; a point of utmost importance also for future pandemic surveillance. Importantly, the gathered knowledge will be used to identify virus inhibition opportunities by means of in-silico drug screening, starting from known and commercially available drugs first. This work uses the atomistic-MD trajectories for macromolecules involved in the COVID-19 disease stored in the BioExcel-CV19 server. The research work is done in collaboration with Roberto Burioni’s laboratory for viral research in Università Vita-Salute San Raffaele, Italy.
    • Melatonin as a potential compound against SARS-CoV-2. Gianni de Fabritiis is head of the Computational Science research group of GRIB led by Ferran Sanz at IMIM-UPF, this work is also developed in collaboration with Acellera. In silico binding assay suggests melatonin can bind to SARS-CoV-2 main protease. The website includes simulation data, PDB of poses and all code to run.

    • OHDSI-EHDEN - M.A. Mayer is part of this initiative as a researcher and he is part of the Integrative Biomedical Informatics group led by Ferran Sanz at IMIM-UPF. Global data to support COVID-19 research. Observational Health Data Sciences and Informatics (OHDSI) initiative, an international multidisciplinary collaboration in response to the current global pandemic. Follow their COVID-19 updates page.
    • RiPCoN - Rapid interaction profiling of COVID-19 for network-based deep drug-repurpose learning - EU urgent project -  with 1,3M€: Patrick Aloy (Structural Bioinformatics and Network Biology, IRB Barcelona) will perform a computational study of the interactions between coronavirus and human cells, with the aim to identify drugs (already on the market or in trials) that can halt the spread of the virus. His role as PI is underway together with Pascal Falter-Braun (Helmholtz Zentrum München, DE) and Christine Brun (INSERM Marseille, FR). Taking into account the acute emergency caused by the current coronavirus pandemic, the project has been planned in two parts. First, we aim to identify approved drugs that can be repurposed for the treatment of COVID-19 (SARS-CoV-2) using interactome profiling and deep-learning. We will deploy rapid high-throughput protein-protein interaction mapping and computational protein-RNA interaction predictions to chart the coronavirus host interactome network (CoHIN), which will become a public resource for translational and basic coronavirus research few months after project start. CoHIN will serve as input into an existing deep-learning model to identify approved drugs that are likely effective against COVID-19, which will be validated in in vitro and in vivo systems. In the second stage, we will exploit our resources towards better preparedness against future outbreaks by pursuing mid-term goals. Experimentally, we will determine the matrix of viral protein alleles vs. variants of the interacting human proteins to understand how human and viral natural variations jointly mediate disease severity in different individuals. These data will be integrated with epidemiological and human genomics data to improve risk management and improve preparedness for future coronavirus outbreaks. Similarly, on the drug discovery side we will apply existing artificial intelligence (AI) approaches to identify the most likely efficient and already approved drugs against the COVID-19.
    • Study about how vaccines are mentioned on Social Media (including future COVID-19 vaccine). Angela Leis is part of the Integrative Biomedical Informatics group led by Ferran Sanz at IMIM-UPF. It is well known that there are many antivaccination groups that are using Social Media platforms, such as Twitter, as a means to disseminate fake information related to vaccines. In this study, tweets related to vaccines in Spanish have been gathered from October 2019 to determine, using text mining, changes in the people’s perception of vaccines coinciding with the coronavirus outbreak.
    • TriNetX COVID-19 Initiative: M.A. Mayer is part of this initiative as a researcher and he is part of the Integrative Biomedical Informatics group led by Ferran Sanz at IMIM-UPF. TriNetX is an international Global network to enable observational studies and trial opportunities related to COVID-19. TriNetX has fast-tracked updates to its real-world data (RWD) platform to incorporate specific COVID-19 terminology including diagnosis and LOINC terminology and World Health Organization (WHO) and Centers for Disease Control (CDC) specific coding guidelines to support novel coronavirus test results. As a result, COVID-19 clinical data is now flowing in from its global network of healthcare organizations (HCOs). The company is also collaborating with its network of 150 HCOs to connect pharmaceutical companies to sites that are open to receiving COVID-19 clinical trials and observational studies. TriNetX is the global health research network that connects the world of drug discovery and development from pharmaceutical company to study site, and investigator to patient by sharing real-world data to make clinical and observational research easier and more efficient. TriNetX combines real time access to longitudinal clinical data with state-of-the-art analytics to optimize protocol design and feasibility, site selection, patient recruitment, and enable discoveries through the generation of real-world evidence.