MaraBesemer / BeerMicrobiomeMetaAnalysis

Meta-Analysis and Visualization of Beer Microbiome Data

Beer Microbiome Meta-Analysis

Meta-Analysis and Visualization of Beer Microbiome Data

Dataset

• Beer Microbiome Data: Google Sheets Dataset

Steps

1. MGnify Workflow Complete

• 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

2. MGnify Workflow After Quality Control

(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

3. mapseqToAmpviz and AmpvizLoad Workflow

• 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:
  • OTU_SSU_SILVA Input
  • OTU_LSU_SILVA Input
  • OTU_ITS_UNITE Input
  • OTU_ITSoneDB Input

Data Visualization with Ampvis2

Preparing the Ampvis2 Ready Data

After completing the data preparation workflows, the processed data can be visualized using Ampvis2 tools. Here's how to proceed:

Generating Heatmaps with Ampvis2

1. 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.

2. 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).

Example Output

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.