The questions this addresses are:
- How can a complete analysis, including viral consensus sequence reconstruction and lineage assignment be performed?
- How can such an analysis be kept manageable for lots of samples, yet flexible enough to handle different types of input data?
- What are key results beyond consensus genomes and lineage assignments that need to be understood to avoid inappropriate conclusions about samples?
- How can the needs for high-throughput data analysis in an ongoing infectious disease outbreak/pandemic and the need for proper quality control and data inspection be balanced?

The objectives are:
- Discover and obtain recommended Galaxy workflows for SARS-CoV-2 sequence data analysis through public workflow registries
- Choose and run a workflow to discover mutations in a batch of viral samples from sequencing data obtained through a range of different protocols and platforms
- Run a workflow to summarize and visualize the mutation discovery results for a batch of samples
- Run a workflow to construct viral consensus sequences for the samples in a batch
- Know different SARS-CoV-2 lineage classification systems, and use pangolin and Nextclade to assign samples to predefined lineages
- Combine information from different analysis steps to be able to draw appropriate conclusions about individual samples and batches of viral data