ReddRadar
Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-microbiome-amplicon-processing
SKILL.md
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_nameto 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
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