- Beer Microbiome Data: Google Sheets Dataset
- Workflow Link: MGnify's Amplicon Pipeline Complete
- Inputs:
- SRA Accession List: listing accession IDs
- Clan Information File: ribo.claninfo file
- Covariance Models: ribo.cm file
- Workflow History: History with Beer Dataset
(This workflow is not necessary and was only used because the complete workflow failed at the end. I didn't want to run the whole workflow again.)
- Workflow Link: MGnify's Amplicon Pipeline - After Quality Control
- Inputs:
- Processed Sequences
- Clan Information File: ribo.claninfo file
- Covariance Models: ribo.cm file
- Workflow History: History with Beer Dataset
- Workflow Link: mapseqToAmpviz and AmpvizLoad Workflow
- Inputs:
- mapseq Outputs: Each output (OTU_SSU_SILVA, OTU_LSU_SILVA, etc.) from the MGnify workflow
- Metadata: Metadata Beer
- Workflow Histories:
After completing the data preparation workflows, the processed data can be visualized using Ampvis2 tools. Here's how to proceed:
-
Input Data:
- Ampvis2 RDS Dataset: This is the output from the mapseqToAmpviz and AmpvizLoad workflow.
- Metadata List (Optional): The metadata list output from the mapseqToAmpviz and AmpvizLoad workflow.
-
Creating a Heatmap:
- Tool: Ampvis2 Heatmap Tool
- Inputs:
- Ampvis2 RDS Dataset: Select the Ampvis2 RDS dataset output.
- Metadata List (Optional): Input the metadata list if available.
- Taxonomic Level: Choose the desired taxonomic level to aggregate the OTUs.
- Grouping and Faceting: You can group and facet the samples based on values from the metadata list (if provided).
For example, using the OTU_ITS_UNITE input aggregated at the species level and grouped by beer type, the resulting heatmap provides a clear visualization of the data.
Sample output can be seen here:
By following these steps, you can generate informative heatmaps to visualize the microbiome data from beer samples effectively.