biotender-max/bio-microbiome-amplicon-processing
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.
npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-microbiome-amplicon-processingReference examples tested with: DADA2 1.30+, cutadapt 4.4+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
"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.
dada2::filterAndTrim() → learnErrors() → dada() → removeBimeraDenovo()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])
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)
errF <- learnErrors(filtFs, multithread = TRUE)
errR <- learnErrors(filtRs, multithread = TRUE)
# Visualize error rates
plotErrors(errF, nominalQ = TRUE)
dadaFs <- dada(filtFs, err = errF, multithread = TRUE)
dadaRs <- dada(filtRs, err = errR, multithread = TRUE)
# Check results
dadaFs[[1]]
mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose = TRUE)
# Check merge success
head(mergers[[1]])
seqtab <- makeSequenceTable(mergers)
dim(seqtab)
# Check length distribution
table(nchar(getSequences(seqtab)))
seqtab_nochim <- removeBimeraDenovo(seqtab, method = 'consensus',
multithread = TRUE, verbose = TRUE)
# Percentage retained
sum(seqtab_nochim) / sum(seqtab)
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
# 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)
Take biotender-max/bio-microbiome-amplicon-processing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.