Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then to confirm flags
<tool> --help
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
eDNA Metabarcoding
"Process eDNA samples to identify species present" -> Trim primers, denoise to ASVs (or cluster to OTUs), detect chimeras, assign taxonomy, filter contamination with negative controls AND DNA concentration, decompose tag-jumping artifacts, and quantify detection uncertainty via site-occupancy modeling. For the foundational eDNA-for-wildlife review, see Bohmann et al. 2014 Trends Ecol Evol 29:358-367.
CLI: cutadapt for primer removal (linked-adapter mode)
R: dada2::filterAndTrim() -> dada() -> assignTaxonomy() for ASV pipeline
R: decontam::isContaminant() for contamination screening
R: occumb::occumb() for detection-corrected occurrence
The Single Most Important Modern Insight -- Read Counts Are NOT Abundance
Elbrecht & Leese 2015 PLoS One 10:e0130324 and Lamb et al. 2019 Mol Ecol 28:420-430 (meta-analysis) established that metabarcoding read counts have weak-to-moderate, taxon-specific, NONLINEAR correlation with biomass or DNA input. Primer-binding bias dominates; PCR replicates introduce stochasticity. Reporting read counts as abundance without mock-community calibration is malpractice. Modern practice: report PRESENCE/ABSENCE or relative abundance with explicit calibration; use multiple PCR replicates; apply site-occupancy models for detection correction.
A second cornerstone: the ASV-vs-OTU debate is taxon-specific, not universal. Callahan, McMurdie, Holmes 2017 ISME J 11:2639-2643 argued ASVs replace OTUs because modern denoising resolves single-nucleotide differences. Schloss 2021 mSphere 6:e00191-21 showed that for bacterial 16S with 1-15 intra-genomic rRNA copies, a single E. coli strain produces ~7 distinct ASVs, splitting bacterial genomes across artificial clusters. For COI metazoan metabarcoding, ASVs (DADA2/UNOISE3) are recommended; for bacterial 16S, ASVs inflate alpha-diversity and OTUs may be appropriate.
A third: decontam (Davis 2018) is a SCREENING tool, not a deterministic classifier. It flags candidates; biological plausibility check is required before deletion. The default threshold=0.1 over-flags in low-biomass data.
Algorithmic Taxonomy
Method
Output
Strength
Fails when
DADA2
Single-nucleotide ASVs
High resolution; learned error model; standard for COI/12S/18S/fungal-ITS
Small datasets (< 100 samples) for error learning; multi-copy bacterial rRNA
UNOISE3 (USEARCH/VSEARCH; Edgar 2016)
zOTUs (essentially ASVs)
Fast; algorithmic simplicity
Limited Linux/Mac binary distribution under license
Swarm v2 -d 1 --fastidious (Mahé 2015)
Abundance-weighted single-linkage OTUs
Modern OTU pipeline; better than legacy 97% UCLUST
OTUs by design (not single-nt resolution)
97% UCLUST
Classical OTUs
Legacy familiarity
Biologically arbitrary threshold; supersedes by DADA2/swarm
VSEARCH global pairwise
Taxonomic assignment via best-hit
Fast, transparent, no training
Conservative; mis-assigns sister species when ref incomplete
Naive Bayes (q2-feature-classifier, RDP)
Probabilistic taxonomic assignment
Probabilistic confidence; standard for 16S
Confidence values are scikit-learn calibrated, not true probabilities (Bokulich 2018)
SINTAX (Edgar)
Bootstrap-supported taxonomy
Fast; no training
Less accurate than Naive Bayes for divergent sequences
LCA (BASTA, MEGAN-LCA)
Lowest common ancestor of multiple hits
Conservative; never over-confident
Can over-merge to high taxonomic ranks
Phylogenetic placement (EPA-ng + gappa)
Position on reference tree
Most rigorous; phylogenetically explicit
10-100x slower; emerging not yet standard
decontam
Flagged contaminant candidates
Statistical screening of negative controls and DNA concentration patterns
Output is screening, not classification; needs biological-plausibility check
Goal: Remove primer sequences while discarding reads that lack primers, before quality filtering.
Approach: Use cutadapt linked-adapter mode with marker-specific 5' and 3' primer pairs. --discard-untrimmed removes reads lacking expected primers; min_overlap prevents false primer detection in random sequence regions.
Goal: Denoise paired-end amplicon reads into exact amplicon sequence variants (ASVs) with chimera removal and reference-based taxonomy assignment, per Callahan et al. 2016 Nat Methods 13:581-583.
Approach: Filter to length/quality thresholds, learn error rates per dataset, run dada() to denoise, merge pairs, build sequence table, remove chimeras with UCHIME3-equivalent in DADA2, then assign taxonomy against the marker-appropriate reference DB. CRITICAL: primers must be removed (cutadapt) BEFORE filterAndTrim, OR the error model is corrupted.
library(dada2)# CRITICAL: primer removal MUST precede filterAndTrim# DADA2's error model assumes primer-free reads
fwd_reads <- sort(list.files('primer_trimmed/', pattern ='_R1', full.names =TRUE))
rev_reads <- sort(list.files('primer_trimmed/', pattern ='_R2', full.names =TRUE))
filt_fwd <- file.path('filtered', basename(fwd_reads))
filt_rev <- file.path('filtered', basename(rev_reads))# Filter and trim# maxEE=c(2,2): expected errors per read; tradeoff sensitivity/specificity# truncLen: set from quality profile inspection; do not guess
out <- filterAndTrim(fwd_reads, filt_fwd, rev_reads, filt_rev,
maxN =0, maxEE =c(2,2), truncQ =2,
truncLen =c(220,180),# data-dependent; inspect plotQualityProfile()
minLen =100, rm.phix =TRUE, multithread =TRUE)# Learn error rates# For small datasets (< 100 samples), pool aggressively or use pre-learned model
err_fwd <- learnErrors(filt_fwd, multithread =TRUE)
err_rev <- learnErrors(filt_rev, multithread =TRUE)# Denoise
dada_fwd <- dada(filt_fwd, err = err_fwd, multithread =TRUE)
dada_rev <- dada(filt_rev, err = err_rev, multithread =TRUE)# Merge pairs with minimum overlap
merged <- mergePairs(dada_fwd, filt_fwd, dada_rev, filt_rev, minOverlap =12)# Build sequence table
seqtab <- makeSequenceTable(merged)# Remove chimeras# method='consensus': per-sample then consensus; conservative (default)# method='pooled': pooled across samples; aggressive; can over-merge real diversity# Chimera rate >30% typically indicates library prep problems
seqtab_nochim <- removeBimeraDenovo(seqtab, method ='consensus',
multithread =TRUE)
cat('Chimera rate:',round(1-sum(seqtab_nochim)/sum(seqtab),3),'\n')# Taxonomy assignment# minBoot=80: standard genus-level confidence; 50 for family-level# IMPORTANT: pair the marker with the appropriate reference DB# COI -> MIDORI2 LONGEST_NUC_GB259_CO1 (or BOLD with curation)# 12S -> MitoFish (Miya lab)# 16S V4 -> SILVA 138.1+# 18S V4/V9 -> SILVA 138.1+ or PR2# Fungal ITS -> UNITE 9.0+
taxa <- assignTaxonomy(seqtab_nochim,'MIDORI2_LONGEST_NUC_GB259_CO1_DADA2.fasta.gz',
minBoot =80, multithread =TRUE)
OBITools3 Pipeline — The v1 -> v3 Command Break
Goal: Process eDNA reads through the Unix-style OBITools v3 pipeline (Boyer et al. 2016 Mol Ecol Resour 16:176-182 introduced OBITools v1; v3 is the post-2018 Python 3 rewrite) with DMS-based sequence management.
Approach: v3 introduces a Database Management System (DMS) abstraction; sequences are imported into a DMS rather than read directly from FASTQ. Commands use spaces (e.g., obi stats plural, not obistat). Taxonomy import expects .tar.gz archive, not a directory.
Across most metabarcoding studies, 50-85% of ASVs cannot be assigned to species level due to incomplete references (Wangensteen et al. 2018 PeerJ 6:e4705 documented this for marine COI + 18S). Report this gap honestly; do not infer ecology from "unassigned" reads.
Goal: Detect and remove sequence-to-sample misassignments arising from chimeric library molecules with mismatched indices.
Approach: Use dual-indexing (different indices at both ends; cross-jumped pairs are discarded). Quantify residual tag-jumping rate from per-ASV cross-sample appearance and apply per-ASV abundance threshold filtering with metabaR::tagjumpslayer. For NovaSeq libraries, expect ~10x higher tag-jumping than MiSeq due to patterned flow cells.
library(metabaR)# metabaR expects an metabarlist object (asv table + sample info + ngsfilter)# tagjumpslayer applies per-ASV abundance-threshold filter# threshold: 0.01 (1% of ASV total) is conservative; 0.001 for aggressive removal# Adjust threshold higher for NovaSeq (~0.005-0.01) than MiSeq (~0.001-0.005)# Quantify residual tag-jumping rate before filtering:# Count reads in sample x ASV combinations that should be 0 by experimental design# (e.g., samples explicitly excluded from a particular condition)# That rate / total reads = empirical tag-jumping rate# Report this rate in methods section
Contamination Screening with decontam
Goal: Identify candidate contaminant ASVs from negative controls and DNA-concentration patterns.
Approach: Use decontam::isContaminant with method='combined' when both DNA concentration AND negative controls are available. Treat flagged ASVs as SCREENING CANDIDATES; verify biological plausibility before deletion. The default threshold=0.1 is over-aggressive in low-biomass data.
library(decontam)# Frequency method: contaminants more frequent at LOW DNA concentration# Prevalence method: contaminants more frequent in negative controls# Combined: uses both signals (most robust)
contam <- isContaminant(seqtab_nochim,
conc = dna_concentration,# qPCR or Qubit per sample
neg = is_negative_control,# logical: which samples are controls
method ='combined',
threshold =0.1)# default; lower for high-confidence calls# CRITICAL: decontam output is SCREENING, not classification# Manually inspect each flagged ASV: is the taxonomic assignment plausibly a reagent contaminant?# Common reagent contaminants: Delftia, Sphingomonas, Burkholderia, Propionibacterium
flagged <- which(contam$contaminant)
cat('Decontam flagged',length(flagged),'ASVs as candidates\n')# After manual review, remove confirmed contaminants
confirmed_contam <- intersect(flagged, biological_plausibility_check_result)
seqtab_clean <- seqtab_nochim[,!(colnames(seqtab_nochim)%in% confirmed_contam)]
Site-Occupancy Modeling — Correcting for Imperfect Detection
Goal: Estimate true species occurrence probabilities from replicated eDNA samples, accounting for false negatives in any single PCR replicate.
Approach: Fit a multi-species occupancy model via MCMC (Ficetola 2015 Mol Ecol Resour 15:543-556) on a 3D array of replicated read counts. Output: per-site, per-species occupancy probabilities corrected for detection.
library(occumb)# y: 3D array [species, sites, replicates] of read counts# spec_cov: species covariates (traits)# site_cov: site covariates (env)
data_obj <- occumbData(y = count_array, spec_cov = species_covariates,
site_cov = site_covariates)# Fit hierarchical occupancy model# Requires JAGS installation# n.iter >= 10000, n.burn >= 2500 for publication-quality posteriors
fit <- occumb(data = data_obj, n.chains =4, n.iter =10000,
n.thin =5, n.burn =2500)# Extract detection-corrected occupancy
summary(fit)
Per-Method Failure Modes
Reporting read counts as biomass without mock-community calibration
Trigger: Comparing read counts of two ASVs and reporting the ratio as a biomass / abundance estimate.
Mechanism: Primer-template binding affinity varies systematically across taxa; PCR amplification is non-linear (saturates); read counts have weak-to-moderate, NONLINEAR correlation with biomass (Elbrecht 2015; Lamb 2019).
Symptom: Reviewer asks "how is it known that reads = biomass?"; cross-study quantitative comparisons fail to replicate.
Fix: Either (a) restrict reporting to presence/absence; (b) report read counts as relative abundances with explicit caveat; or (c) include mock-community of known composition for primer-specific calibration. Do not silently equate reads with biomass.
NovaSeq tag-jumping with MiSeq-tuned filtering
Trigger: Applying tag-jumping filters calibrated on MiSeq libraries to NovaSeq data.
Mechanism: NovaSeq patterned flow cells have ~10x higher index hopping than MiSeq. MiSeq-calibrated thresholds (often ~0.001 fraction) are too permissive on NovaSeq data.
Symptom: Apparent rare-species detections in NovaSeq libraries do not replicate; per-ASV cross-sample appearance is unusually broad.
Fix: Use NovaSeq-appropriate tag-jumping thresholds (~0.005-0.01) and report the empirical tag-jumping rate from explicit-zero combinations.
decontam threshold over-aggressive in low-biomass data
Trigger: Applying default threshold = 0.1 to ASVs from open-ocean water, ancient sediments, or other dilute samples.
Mechanism: In low-biomass samples, the contaminant signal/background ratio approaches 1; decontam over-flags real but dilute biology as "contaminant" because the statistical pattern looks similar.
Symptom: Many ASVs flagged from low-biomass samples; taxonomic profile of "flagged contaminants" looks biologically realistic.
Fix: Lower threshold (0.05 or 0.01); always manually review flagged ASVs for biological plausibility; cite Salter 2014 BMC Biol 12:87 for the low-biomass reagent-contamination caveat.
Skipping primer removal before DADA2 filterAndTrim
Trigger: Running DADA2's filterAndTrim() on FASTQ files that still contain primer sequences.
Mechanism: DADA2 learns sequencing error from the empirical data; if primer sequences are present, they look like "perfect agreement" and corrupt the error model. ASVs are inferred with primer artifacts attached.
Symptom: DADA2 reports "phix-like contamination" (false; it's primers); ASVs start with the primer sequence; chimera rate elevated.
Fix: Always run cutadapt (or similar) BEFORE filterAndTrim. Verify with head of trimmed FASTQ that primer sequences are gone.
OBITools v3 commands with v1 syntax
Trigger: Running obistat or obigrep on a v3 install.
Mechanism: v1 used concatenated command names (obistat); v3 uses subcommand syntax with a space (obi stats — note plural).
Symptom: Bash error obistat: command not found; tutorial documentation does not match installed version.
Fix: Use obi <subcommand> syntax; consult obi --help for current command list. Taxonomy import requires .tar.gz archive, not unpacked directory.
Quantitative Thresholds
Threshold
Value
Source / rationale
DADA2 maxEE per read
2
Standard sensitivity/specificity balance
DADA2 chimera rate alarm
> 30% suggests library issues
Empirical convention
DADA2 minBoot for taxonomy
80 for genus; 50 for family
Standard confidence cutoffs
Tag-jumping filter MiSeq
0.001-0.005 fraction of ASV total
Schnell 2015
Tag-jumping filter NovaSeq
0.005-0.01 fraction of ASV total
Patterned-cell index hopping ~10x higher
decontam threshold
0.1 default; 0.05 for low-biomass
Davis 2018; reduce for dilute samples
Per-sample minimum reads
1000 (after filtering)
Below this rare-species detection unreliable
Singleton removal
count >= 2
Singletons often error-driven
Bootstrap nperm for tests
999
Standard permutation count
Occupancy model iterations
n.iter >= 10000, n.burn >= 2500
occumb default for stable posteriors
eDNA decay (20 deg C surface water)
half-life ~4-15 hours
Strickler 2015 Biol Conserv 183:85-92
Common errors
Error
Cause
Solution
obistat: command not found
OBITools v3 uses obi stats (plural)
Use v3 syntax
DADA2 error rate plot looks pathological
Primer sequences still in reads
Re-run cutadapt before filterAndTrim
Chimera rate > 30%
Library-prep issue or primer dimers
Inspect raw FASTQ; check PCR conditions
decontam flags many real species
Default threshold too aggressive for low-biomass
Lower threshold; manual review
Naive Bayes confidence 0.95 but species is wrong
scikit-learn-calibrated "confidence" not true probability
Use phylogenetic placement for borderline assignments
occumb JAGS not found error
JAGS not installed system-wide
Install JAGS (CRAN page has platform instructions)
eDNA detections do not replicate
Read counts treated as abundance
Switch to presence/absence; use mock-community calibration
MIDORI2 download path expired
Database updated; old URL gone
Check current MIDORI2 / MitoFish download page
References
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2. Nat Methods 13(7):581-583. doi:10.1038/nmeth.3869
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ (2018) decontam. Microbiome 6:226. doi:10.1186/s40168-018-0605-2
Boyer F, Mercier C, Bonin A, Le Bras Y, Taberlet P, Coissac E (2016) OBITools. Mol Ecol Resour 16(1):176-182. doi:10.1111/1755-0998.12428
Elbrecht V, Leese F (2015) DNA-based ecosystem quantification critique. PLoS One 10(7):e0130324. doi:10.1371/journal.pone.0130324
Lamb PD, Hunter E, Pinnegar JK, Creer S, Davies RG, Taylor MI (2019) How quantitative is metabarcoding: meta-analysis. Mol Ecol 28(2):420-430. doi:10.1111/mec.14920
Ficetola GF, Pansu J, Bonin A et al. (2015) Replication levels and false presences in eDNA. Mol Ecol Resour 15(3):543-556. doi:10.1111/1755-0998.12338
Wangensteen OS, Palacin C, Guardiola M, Turon X (2018) COI + 18S marine metabarcoding. PeerJ 6:e4705. doi:10.7717/peerj.4705
Bokulich NA, Kaehler BD, Rideout JR et al. (2018) Optimizing taxonomic classification with q2-feature-classifier. Microbiome 6:90. doi:10.1186/s40168-018-0470-z
Bohmann K, Evans A, Gilbert MTP et al. (2014) eDNA for wildlife and biodiversity. Trends Ecol Evol 29(6):358-367. doi:10.1016/j.tree.2014.04.003
Schloss PD (2021) Amplicon sequence variants artificially split bacterial genomes into separate clusters. mSphere 6(4):e00191-21. doi:10.1128/mSphere.00191-21
Salter SJ, Cox MJ, Turek EM et al. (2014) Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 12:87. doi:10.1186/s12915-014-0087-z
Taberlet P, Coissac E, Pompanon F et al. (2007) Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res 35(3):e14. doi:10.1093/nar/gkl938
Related Skills
ecological-genomics/biodiversity-metrics - Diversity analysis from species occurrence tables (Hill numbers, beta partition)
ecological-genomics/community-ecology - Environmental gradient analysis of community composition (PERMANOVA + PERMDISP, ordination)
microbiome/amplicon-processing - 16S clinical microbiome alternative pipeline
read-qc/quality-reports - Upstream read-quality assessment before primer trimming
database-access/entrez-fetch - Retrieve reference sequences for custom taxonomy databases
