--- name: bio-microbiome-amplicon-processing description: Amplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis. tool_type: r primary_tool: dada2 --- ## 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('')` 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 ```r 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 ```r 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 ```r errF <- learnErrors(filtFs, multithread = TRUE) errR <- learnErrors(filtRs, multithread = TRUE) # Visualize error rates plotErrors(errF, nominalQ = TRUE) ``` ## Sample Inference (Denoising) ```r dadaFs <- dada(filtFs, err = errF, multithread = TRUE) dadaRs <- dada(filtRs, err = errR, multithread = TRUE) # Check results dadaFs[[1]] ``` ## Merge Paired Reads ```r mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose = TRUE) # Check merge success head(mergers[[1]]) ``` ## Construct Sequence Table ```r seqtab <- makeSequenceTable(mergers) dim(seqtab) # Check length distribution table(nchar(getSequences(seqtab))) ``` ## Remove Chimeras ```r 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. ```r 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 ```r # 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

bio-microbiome-amplicon-processing

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