bio-workflows-microbiome-pipeline

name	bio-workflows-microbiome-pipeline
description	End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data.
tool_type	r
primary_tool	dada2
workflow	true
depends_on	["microbiome/amplicon-processing","microbiome/taxonomy-assignment","microbiome/diversity-analysis","microbiome/differential-abundance"]

Microbiome Pipeline

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


filtFs  file.path paste0sample_names 
filtRs  file.path paste0sample_names 


out  filterAndTrimfnFs filtFs fnRs filtRs
                     truncLen    maxN   maxEE   
                     truncQ   rm.phix   compress   multithread  


errF  learnErrorsfiltFs multithread  
errR  learnErrorsfiltRs multithread  
dadaFs  dadafiltFs err  errF multithread  
dadaRs  dadafiltRs err  errR multithread  


mergers  mergePairsdadaFs filtFs dadaRs filtRs verbose  
seqtab  makeSequenceTablemergers
seqtab_nochim  removeBimeraDenovoseqtab method   multithread  


taxa  assignTaxonomyseqtab_nochim silva_train multithread  
taxa  addSpeciestaxa silva_species


libraryDECIPHER
libraryphangorn

seqs  getSequencesseqtab_nochim
seqs  paste0 seqs
alignment  AlignSeqsDNAStringSetseqs anchor   processors  
phang_align  phyDatasalignment  type  
dm  dist.mlphang_align
tree  NJdm
tree  midpointladderizetree


metadata  read.csvmetadata_file row.names  
ps  phyloseqotu_tableseqtab_nochim taxa_are_rows  
               tax_tabletaxa sample_datametadata phy_treetree
taxa_namesps  paste0 seqntaxaps



librarypicante
alpha_div  estimate_richnessps measures    
faith_pd  pdtotu_tableps phy_treeps include.root  
alpha_divPD  faith_pdPD
alpha_divGroup  sample_datapsGroup


bray_dist  phyloseqdistanceps method  
unifrac_dist  UniFracps weighted  
pcoa_bray  ordinateps method   distance  bray_dist
pcoa_unifrac  ordinateps method   distance  unifrac_dist


meta_df  data.framesample_dataps
permanova_bray  adonis2bray_dist  Group data  meta_df permutations  
permanova_unifrac  adonis2unifrac_dist  Group data  meta_df permutations  



ps_filt  filter_taxaps x x      nsamplesps 


otu  as.data.frametotu_tableps_filt
groups  sample_dataps_filtGroup
aldex_results  aldexotu groups mc.samples   test   effect  
aldex_resultssignificant  aldex_resultswe.eBH    aldex_resultseffect  


cat
cat ntaxaps 
cat nsamplesps 
cat permanovaR2   permanova`Pr(>F)` 
cat aldex_resultssignificant 

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