Version Compatibility

Reference examples tested with: DADA2 1.30+, ggplot2 3.5+, phyloseq 1.46+, scanpy 1.10+, vegan 2.6+

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.

Microbiome Pipeline

"Run end-to-end 16S microbiome analysis from FASTQ" → Orchestrate DADA2 denoising, taxonomy assignment, alpha/beta diversity (phyloseq), differential abundance (ALDEx2/ANCOM-BC2), and functional prediction (PICRUSt2).

Pipeline Overview

Paired-End FASTQ (16S V4)
           │
           ▼
┌──────────────────────────────────────────────────┐
│              microbiome-pipeline                 │
├──────────────────────────────────────────────────┤
│  1. Quality Filtering (DADA2 filterAndTrim)     │
│  2. Error Learning & Denoising                   │
│  3. Merge Pairs & Remove Chimeras                │
│  4. Taxonomy Assignment (SILVA)                  │
│  5. Create phyloseq Object                       │
│  6. Alpha/Beta Diversity                         │
│  7. Differential Abundance (ALDEx2)              │
│  8. Visualization & Export                       │
└──────────────────────────────────────────────────┘
           │
           ▼
ASV Table + Taxonomy + Diversity Plots + Differential Taxa

Complete R Workflow

library(dada2)
library(phyloseq)
library(ALDEx2)
library(vegan)
library(ggplot2)

# === CONFIGURATION ===
path <- 'raw_reads'
silva_train <- 'silva_nr99_v138.1_train_set.fa.gz'
silva_species <- 'silva_species_assignment_v138.1.fa.gz'
metadata_file <- 'sample_metadata.csv'

# === 1. READ FILES ===
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)

# Setup filtered files
filtFs <- file.path('filtered', paste0(sample_names, '_F_filt.fastq.gz'))
filtRs <- file.path('filtered', paste0(sample_names, '_R_filt.fastq.gz'))

# === 2. FILTER & TRIM ===
out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,
                     truncLen = c(240, 160), maxN = 0, maxEE = c(2, 2),
                     truncQ = 2, rm.phix = TRUE, compress = TRUE, multithread = TRUE)

# === 3. LEARN ERRORS & DENOISE ===
errF <- learnErrors(filtFs, multithread = TRUE)
errR <- learnErrors(filtRs, multithread = TRUE)
dadaFs <- dada(filtFs, err = errF, multithread = TRUE)
dadaRs <- dada(filtRs, err = errR, multithread = TRUE)

# === 4. MERGE & CHIMERAS ===
mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose = TRUE)
seqtab <- makeSequenceTable(mergers)
seqtab_nochim <- removeBimeraDenovo(seqtab, method = 'consensus', multithread = TRUE)

# === 5. ASSIGN TAXONOMY ===
taxa <- assignTaxonomy(seqtab_nochim, silva_train, multithread = TRUE)
taxa <- addSpecies(taxa, silva_species)

# === 6. BUILD PHYLOGENETIC TREE (for UniFrac) ===
library(DECIPHER)
library(phangorn)

seqs <- getSequences(seqtab_nochim)
names(seqs) <- paste0('ASV', seq_along(seqs))
alignment <- AlignSeqs(DNAStringSet(seqs), anchor = NA, processors = NULL)
phang_align <- phyDat(as(alignment, 'matrix'), type = 'DNA')
dm <- dist.ml(phang_align)
tree <- NJ(dm)
tree <- midpoint(ladderize(tree))

# === 7. CREATE PHYLOSEQ ===
metadata <- read.csv(metadata_file, row.names = 1)
ps <- phyloseq(otu_table(seqtab_nochim, taxa_are_rows = FALSE),
               tax_table(taxa), sample_data(metadata), phy_tree(tree))
taxa_names(ps) <- paste0('ASV', seq(ntaxa(ps)))

# === 8. DIVERSITY ===
# Alpha diversity (including Faith's PD with tree)
library(picante)
alpha_div <- estimate_richness(ps, measures = c('Observed', 'Shannon', 'Simpson'))
faith_pd <- pd(t(otu_table(ps)), phy_tree(ps), include.root = TRUE)
alpha_div$PD <- faith_pd$PD
alpha_div$Group <- sample_data(ps)$Group

# Beta diversity (Bray-Curtis and UniFrac)
bray_dist <- phyloseq::distance(ps, method = 'bray')
unifrac_dist <- UniFrac(ps, weighted = TRUE)
pcoa_bray <- ordinate(ps, method = 'PCoA', distance = bray_dist)
pcoa_unifrac <- ordinate(ps, method = 'PCoA', distance = unifrac_dist)

# PERMANOVA on both metrics
meta_df <- data.frame(sample_data(ps))
permanova_bray <- adonis2(bray_dist ~ Group, data = meta_df, permutations = 999)
permanova_unifrac <- adonis2(unifrac_dist ~ Group, data = meta_df, permutations = 999)

# === 9. DIFFERENTIAL ABUNDANCE ===
# Filter low-abundance taxa
ps_filt <- filter_taxa(ps, function(x) sum(x > 0) > 0.1 * nsamples(ps), TRUE)

# ALDEx2
otu <- as.data.frame(t(otu_table(ps_filt)))
groups <- as.character(sample_data(ps_filt)$Group)
aldex_results <- aldex(otu, groups, mc.samples = 128, test = 'welch', effect = TRUE)
aldex_results$significant <- aldex_results$we.eBH < 0.05 & abs(aldex_results$effect) > 1

# === 10. OUTPUT ===
cat('Pipeline complete!\n')
cat('  ASVs:', ntaxa(ps), '\n')
cat('  Samples:', nsamples(ps), '\n')
cat('  PERMANOVA R2:', round(permanova$R2[1], 3), 'p =', permanova$`Pr(>F)`[1], '\n')
cat('  Differential taxa:', sum(aldex_results$significant), '\n')

QC Checkpoints

Stage	Check	Expected	Action if Failed
Filter	>70% reads pass	>70%	Adjust truncLen/maxEE
Merge	>80% pairs merge	>80%	Check amplicon length
Chimera	<25% chimeras	<25%	Check PCR cycles
Taxonomy	>80% genus assigned	>80%	Try different database
Rarefaction	Curves plateau	Plateau	Increase depth
PERMANOVA	p < 0.05	p < 0.05	Check experimental design

Output Files

microbiome_results/
├── phyloseq_object.rds      # Complete phyloseq
├── asv_table.csv            # ASV counts
├── taxonomy.csv             # Taxonomic assignments
├── alpha_diversity.csv      # Per-sample metrics
├── aldex2_results.csv       # Differential taxa
├── read_tracking.csv        # Reads per pipeline stage
├── plots/
│   ├── quality_profiles.pdf
│   ├── alpha_diversity.pdf
│   ├── beta_diversity_pcoa.pdf
│   ├── taxonomic_barplot.pdf
│   └── aldex2_effect_plot.pdf

Workflow Variants

ITS Fungal Workflow

# Key differences for ITS:
# 1. No truncLen (variable length amplicons)
out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, maxN = 0, maxEE = c(2, 2),
                     truncQ = 2, minLen = 50, rm.phix = TRUE, multithread = TRUE)

# 2. Use UNITE database
taxa <- assignTaxonomy(seqtab_nochim, 'sh_general_release_dynamic_25.07.2023.fasta',
                       multithread = TRUE)

Different 16S Regions

# V3-V4 (~460bp): truncLen = c(280, 200)
# V4 (~253bp): truncLen = c(240, 160)
# V1-V3 (~500bp): truncLen = c(260, 220)

GTDB Taxonomy

# For environmental samples, GTDB may be more accurate
taxa <- assignTaxonomy(seqtab_nochim, 'GTDB_bac120_arc53_ssu_r214_fullTaxo.fa.gz',
                       multithread = TRUE)

Related Skills

• microbiome/amplicon-processing - DADA2 details
• microbiome/taxonomy-assignment - Database options, IDTAXA
• microbiome/diversity-analysis - Diversity metrics, Faith's PD
• microbiome/differential-abundance - ALDEx2, ANCOM-BC2
• microbiome/functional-prediction - PICRUSt2 functional analysis