ASVs

This step will extract Amplicon Sequence Variants (ASVs) from read sequences. This step involves the following two steps

  1. Denoising: This step perform quality filtering and chimera removing. The results of this step is include a feature table and a frequency count table.

  2. Taxonomic classification: This step assign taxonomic classification to the extracted features.

Denoising

This step will denoise our sequence dataset and also remove chimera sequences. We will use dada2 (Callahan et al. 2016) for denoising.

This step performs the following three tasks

  1. Quality filtering
  2. Chimera checking
  3. Paired-end joining
qiime dada2 denoise-paired \
  --i-demultiplexed-seqs demultiplexed-sequences.qza \
  --p-trunc-len-f 204 \
  --p-trim-left-r 1 \
  --p-trunc-len-r 205 \
  --o-representative-sequences asv-sequences-0.qza \
  --o-table feature-table-0.qza \
  --o-denoising-stats dada2-stats.qza

We will also generate a summary file to gain insights into the result of the command.

qiime metadata tabulate \
  --m-input-file dada2-stats.qza \
  --o-visualization dada2-stats-summ.qzv

Taxonomic classification

qiime feature-classifier classify-sklearn \
    --i-classifier /home/pankaj/reference_db/silva-138.1-ssu-nr99-515f-806r-classifier.qza \
    --i-reads asv-sequences-0.qza
    --o-classification rep-seqs-classification.qza

References

Callahan, Benjamin J, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy Jo A Johnson, and Susan P Holmes. 2016. “DADA2: High-Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13 (7): 581–83.