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name: bio-microbiome-taxonomy-assignment description: Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing. tool_type: mixed primary_tool: dada2

Taxonomy Assignment

DADA2 Naive Bayes Classifier

Copylibrary(dada2)

seqtab_nochim <- readRDS('seqtab_nochim.rds')

# SILVA for 16S (download from https://zenodo.org/record/4587955)
taxa <- assignTaxonomy(seqtab_nochim, 'silva_nr99_v138.1_train_set.fa.gz',
                       multithread = TRUE)

# Add species-level (exact matching)
taxa <- addSpecies(taxa, 'silva_species_assignment_v138.1.fa.gz')

# Check results
head(taxa)

GTDB for 16S

Copy# GTDB-formatted database (better for environmental samples)
taxa_gtdb <- assignTaxonomy(seqtab_nochim, 'GTDB_bac120_arc53_ssu_r220_fullTaxo.fa.gz',
                            multithread = TRUE)

UNITE for ITS (Fungi)

Copy# UNITE database for fungal ITS
taxa_its <- assignTaxonomy(seqtab_nochim, 'sh_general_release_dynamic_25.07.2023.fasta',
                           multithread = TRUE)

QIIME2 Feature Classifier

Copy# Train classifier (one-time)
qiime feature-classifier fit-classifier-naive-bayes \
    --i-reference-reads silva-138-99-seqs.qza \
    --i-reference-taxonomy silva-138-99-tax.qza \
    --o-classifier silva-138-99-nb-classifier.qza

# Classify ASVs
qiime feature-classifier classify-sklearn \
    --i-classifier silva-138-99-nb-classifier.qza \
    --i-reads rep-seqs.qza \
    --o-classification taxonomy.qza

VSEARCH Exact Matching

Copy# Faster but requires exact or near-exact matches
vsearch --usearch_global asv_seqs.fasta \
    --db silva_138_SSURef_NR99.fasta \
    --id 0.97 \
    --blast6out taxonomy_vsearch.tsv \
    --top_hits_only

RDP Classifier

Copylibrary(dada2)

# RDP training set (less detailed than SILVA)
taxa_rdp <- assignTaxonomy(seqtab_nochim, 'rdp_train_set_18.fa.gz',
                           multithread = TRUE)

IDTAXA (DECIPHER) - Often More Accurate

Copylibrary(DECIPHER)

# Load IDTAXA training set (download from http://www2.decipher.codes/Downloads.html)
load('SILVA_SSU_r138_2019.RData')  # Creates 'trainingSet' object

# Convert ASV sequences to DNAStringSet
dna <- DNAStringSet(getSequences(seqtab_nochim))

# Classify with IDTAXA
ids <- IdTaxa(dna, trainingSet, strand = 'top', processors = NULL, verbose = TRUE)

# Convert to matrix format like assignTaxonomy
ranks <- c('domain', 'phylum', 'class', 'order', 'family', 'genus', 'species')
taxa_idtaxa <- t(sapply(ids, function(x) {
    m <- match(ranks, x$rank)
    taxa <- x$taxon[m]
    taxa[startsWith(taxa, 'unclassified_')] <- NA
    taxa
}))
colnames(taxa_idtaxa) <- ranks

Confidence Filtering

Copy# assignTaxonomy returns bootstrap confidence
# Filter low-confidence assignments
taxa_filtered <- taxa
taxa_filtered[taxa_filtered < 80] <- NA  # If using minBoot output

# Or use confidence threshold during assignment
taxa <- assignTaxonomy(seqtab_nochim, 'silva_nr99_v138.1_train_set.fa.gz',
                       minBoot = 80, multithread = TRUE)

Combine into phyloseq

Copylibrary(phyloseq)

# Create phyloseq object
ps <- phyloseq(otu_table(seqtab_nochim, taxa_are_rows = FALSE),
               tax_table(taxa))

# Add sample metadata
sample_data(ps) <- read.csv('sample_metadata.csv', row.names = 1)

# Rename ASVs for readability
taxa_names(ps) <- paste0('ASV', seq(ntaxa(ps)))

Database Comparison

Related Skills

• amplicon-processing - Generate ASV table for classification
• diversity-analysis - Analyze classified communities
• metagenomics/kraken-classification - Read-level taxonomic classification