42 Overcoming barriers to SARS-CoV-2 data analysis

ARTICnetwork & CLIMB-BIG-DATA present a panel discussion on overcoming barriers to SARS-CoV-2 data analysis with Nick Loman and Will Rowe from the University of Birmingham, Áine O'Toole from the University of Edinburgh, Andrew Page from the Quadram Institute and Anna Price from MRC CLIMB and Cardiff University. This was part of a workshop on COVID-19 data analysis. Topics covered: Collecting sample metadata intrapatient variability Building bridges with policy makers to start sequencing Data sharing Improving bioinformatics skills Pipeline and software validation Bioinformatics reproducibility and quality Papers: The PHA4GE SARS-CoV-2 Contextual Data Specification for Open Genomic Epidemiology https://www.preprints.org/manuscript/202008.0220/v1 MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance https://www.biorxiv.org/content/10.1101/2020.10.06.328328v1 Genomic sequencing of SARS-CoV-2: a guide to implementation for maximum impact on public health https://www.who.int/publications/i/item/9789240018440 Resources: https://www.climb.ac.uk/artic-and-climb-big-data-joint-workshop/ https://github.com/SamStudio8/majora https://soundcloud.com/microbinfie/majora https://github.com/pha4ge/SARS-CoV-2-Contextual-Data-Specification https://pha4ge.org/ Software: https://github.com/cov-lineages/pangolin https://github.com/artic-network/civet https://github.com/COG-UK/grapevine https://github.com/cov-lineages/pangoLEARN