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

```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