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Is Bio Microbiome Amplicon Processing free to use?

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Who published Bio Microbiome Amplicon Processing?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Microbiome Amplicon Processing by majiayu000

Name: Bio Microbiome Amplicon Processing
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

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.

majiayu000 50a5f8e 2 files · 4.1 KB Updated Sep 9, 2026 567 repo stars

File contents

Amplicon Processing with DADA2

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

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

---name: bio-microbiome-amplicon-processingdescription: 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.--- # Amplicon Processing with DADA2 ## 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 ```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

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