PT - JOURNAL ARTICLE AU - Alice Wittig AU - Fábio Miranda AU - Martin Hölzer AU - Tom Altenburg AU - Jakub M. Bartoszewicz AU - Marius A. Dieckmann AU - Ulrich Genske AU - Sven H. Giese AU - Melania Nowicka AU - Henning Schiebenhoefer AU - Anna-Juliane Schmachtenberg AU - Ming Tang AU - Bernhard Y. Renard AU - Stephan Fuchs TI - CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance AID - 10.1101/2021.02.03.429146 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.02.03.429146 4099 - http://biorxiv.org/content/early/2021/04/06/2021.02.03.429146.short 4100 - http://biorxiv.org/content/early/2021/04/06/2021.02.03.429146.full AB - Summary The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of molecular surveillance to understand the evolution of the virus and to monitor and plan the epidemiological responses. Quick analysis, easy visualization and convenient filtering of the latest viral sequences are essential for this purpose. We present CovRadar, a tool for molecular surveillance of the Corona spike protein. The spike protein contains the receptor binding domain (RBD) that is used as a target for most vaccine candidates. CovRadar consists of a workflow pipeline and a web application that enable the analysis and visualization of over 1 million sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants, consensus sequences and phylogenetic trees and finally presents the results in an interactive PDF-like app, making reporting fast, easy and flexible.Availability and implementation CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar.Supplementary information Supplementary data are available at Bioinformatics online.Competing Interest StatementThe authors have declared no competing interest.