Single-cell transcriptomic data analysis in COVID-19 patient groups with different severity profile.
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An exploration on gene expression data was carried out on single-cell RNAseq analyses of bronchoalveolar lavages from nine COVID-19 patients, three moderate cases, one severe case and five critical cases (GSE145826) (doi: 10.1038/s41591-020-0901-9). To these data, single-cell RNA-sequencing from one COVID-19 lung biopsy, ~10 weeks after initial infection was added to represent persistent severe COVID19 patient group (3 weeks after symptom onset) (GSE163919). For this analysis, the epithelial cell group (TPPP3, KRT18), directly involved in SARS-CoV-2 infection, was selected per patient groups and overexpressed genes were analysed, comparing with control lung explant tissue of health subjects (GSE160664) (DOI: 10.1164/rccm.202008-3198OC). URL: https://github.com/INMIbioinfo/scC19DM
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Created: 14th Apr 2021 at 10:00
Last updated: 17th May 2022 at 14:17
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https://orcid.org/0000-0001-8076-7217
The Disease Maps Project is designed as a large-scale community effort. It is a network of groups that work together in order to better understand disease mechanisms. The project exchanges best practices, share information, develop tools to make it easier for all the involved groups to achieve their goals.
Projects: COVID-19 Disease Map
Web page: https://disease-maps.org
Here we share resources and best practices to develop a disease map for COVID-19. The project is progressing as a broad community-driven effort. We aim to establish a knowledge repository on virus-host interaction mechanisms specific to the SARS-CoV-2. The COVID-19 Disease Map is an assembly of molecular interaction diagrams established based on literature evidence.
Programme: Disease Maps
Public web page: http://doi.org/10.17881/covid19-disease-map
In this investigation we aim to develop automatic workflows to analyze COVID19 Omics data to understand and predict the molecular pathways depicting host-virus interaction.
