mbbu / 16s-mini-project Goto Github PK

• Do Quality Check using FASTQC
• Trimming and Filtering Reads using Trimmomatic
• Paired read stitching using PEAR
• Chimera Detection using UCHIIME

Train a classifier (using the qiime feature-classifier fit-classifier-naive-bayes model)

1. Obtain the SILVA 132 or Greengenes 13_8 (latest releases for both) databases for 16S analysis.
2. Extract representative sequences and taxonomy files.
3. Train a classifier using the Naive Bayes model using database of choice.
4. Use the classifier to assign taxonomies for dataset.
5. Construct taxonomy barplots to explore microbiome profiles (our dataset in this case).

There may be a concern for the steps followed after Chimera decetion. The steps outligned in the script used may actually have been for another process involved in a Qiime2 process called "Training feature classifiers with q2-feature-classifier" which is important for the assigning taxonomy. This could have led to elucidating wrong results from the dataset and hence wrong inferances. Further research would be required to determine the correct steps to follow.

• OTU Picking using QIIME.
• ASV prediction using QIIME2.
• Classification using UCLUST referencing the SILVA DB.
• Alignment using PyNAST.
• Construction of Phylogenetic Tree - FastTree.

• Alpha Diversity using QIIME.
• Beta Diversity using QIIME.
• Any Other Analysis.

Kindly check the image obs-met rarefaction.png. It is missing