bio-microbiome-amplicon-processing

Version Compatibility

Reference examples tested with: DADA2 1.30+, cutadapt 4.4+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed

package and adapt the example to match the actual API rather than retrying.

Amplicon Processing with DADA2

"Process my 16S amplicon data to get ASVs" → Denoise amplicon sequencing reads into exact amplicon sequence variants (ASVs) through quality filtering, error model learning, and chimera removal.

• R: dada2::filterAndTrim() → learnErrors() → dada() → removeBimeraDenovo()

Complete DADA2 Workflow

library(dada2)

path <- 'raw_reads'
fnFs <- sort(list.files(path, pattern = '_R1_001.fastq.gz', full.names = TRUE))
fnRs <- sort(list.files(path, pattern = '_R2_001.fastq.gz', full.names = TRUE))
sample_names <- sapply(strsplit(basename(fnFs), '_'), `[`, 1)

# Quality profiles
plotQualityProfile(fnFs[1:2])
plotQualityProfile(fnRs[1:2])

Quality Filtering and Trimming

filtFs <- file.path('filtered', paste0(sample_names, '_F_filt.fastq.gz'))
filtRs <- file.path('filtered', paste0(sample_names, '_R_filt.fastq.gz'))
names(filtFs) <- sample_names
names(filtRs) <- sample_names

# Filter parameters depend on amplicon region and read length
out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,
                     truncLen = c(240, 160),      # Trim to quality scores
                     maxN = 0,                     # No ambiguous bases
                     maxEE = c(2, 2),              # Max expected errors
                     truncQ = 2,                   # Truncate at first Q <= 2
                     rm.phix = TRUE,               # Remove PhiX
                     compress = TRUE,
                     multithread = TRUE)

Error Rate Learning

errF <- learnErrors(filtFs, multithread = TRUE)
errR <- learnErrors(filtRs, multithread = TRUE)

# Visualize error rates
plotErrors(errF, nominalQ = TRUE)

Sample Inference (Denoising)

dadaFs <- dada(filtFs, err = errF, multithread = TRUE)
dadaRs <- dada(filtRs, err = errR, multithread = TRUE)

# Check results
dadaFs[[1]]

Merge Paired Reads

mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose = TRUE)

# Check merge success
head(mergers[[1]])

Construct Sequence Table

seqtab <- makeSequenceTable(mergers)
dim(seqtab)

# Check length distribution
table(nchar(getSequences(seqtab)))

Remove Chimeras

seqtab_nochim <- removeBimeraDenovo(seqtab, method = 'consensus',
                                     multithread = TRUE, verbose = TRUE)

# Percentage retained
sum(seqtab_nochim) / sum(seqtab)

Track Reads Through Pipeline

Goal: Generate a per-sample summary table showing how many reads survived each DADA2 processing step for quality assessment.

Approach: Extract read counts from each pipeline stage (filtering, denoising, merging, chimera removal) and combine into a single tracking matrix.

getN <- function(x) sum(getUniques(x))
track <- cbind(out, sapply(dadaFs, getN), sapply(dadaRs, getN),
               sapply(mergers, getN), rowSums(seqtab_nochim))
colnames(track) <- c('input', 'filtered', 'denoisedF', 'denoisedR', 'merged', 'nonchim')
rownames(track) <- sample_names
track

ITS-Specific Processing

# For ITS, use cutadapt to remove primers first (variable length amplicons)
# Then skip truncLen (don't truncate ITS to fixed length)

out_its <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,
                         maxN = 0, maxEE = c(2, 2), truncQ = 2,
                         minLen = 50,  # Minimum length
                         rm.phix = TRUE, compress = TRUE, multithread = TRUE)

Related Skills

• taxonomy-assignment - Assign taxonomy to ASVs
• read-qc/quality-reports - Pre-DADA2 quality assessment
• diversity-analysis - Analyze ASV table