16S rRNA gene targeted-amplicon data processing including quality control, clustering, classification (using mothur)

Input: paired end sequence data

Jupyter notebook:

  • imid_reanalysis_gh.ipynb

Scripts:

  • sbatch_make_contigs.sh
  • make_contigs.sh
  • merge_fasta.sh
  • groups_20dec2017.sh
  • analysis_20dec2017.sh
  • summary.single.sh

Post-OTU analyses: normalization and machine learning classification

Inputs: An OTU count table obtained from above processing with taxanomic information for the OTUs and sample information (e.g. meta-data).

Scripts:

  • microbiome_1stStep.r: data wrangling
  • microbiome_2ndStep.r: data filtering, normalization and machine learning classification
  • ***_batch.r files: sourced in the previous two files to do analyses in batch