Targeted Gene Metagenomic Data Analysis    ◾    301

amplicon-based metagenomic analysis because it can identify bacterial species. A region

or regions of the gene are amplified using PCR and the amplicon then is sequenced with

the high-throughput technologies. The reads are usually for the targeted gene but for sev-

eral species. The analysis is then focused on identifying the taxonomic groups and their

abundance in the sample. After quality control, features unique sequences representing

taxonomic groups are obtained either by clustering or denoising. There are three kinds of

clustering: de novo clustering, open-reference clustering, and closed-reference clustering.

Any of these clustering methods will generate OTUs or operational taxonomic units. On

the other hand, denoising attempts to remove base call errors and classification error and

it produces ASVs, which are unique features that represent species in the sample. There

are three common algorithms for denoising: DADA2, Deblur, and UNOISE2. The most

commonly used program for amplicon-based metagenomic data analysis is QIIME2,

which implements both clustering methods and denoising methods. To analyze data with

QIMME2, raw data must be imported into QIIME2 artifacts. Several analyses can be con-

ducted with QIIME2 including taxonomic group identification and abundance, phyloge-

netic analysis, and diversity analysis.

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