Metagenomics analysis
16S amplicons become DADA2 ASV tables with taxonomy, diversity, and differential abundance via pinned QIIME2 or DADA2 workflows and a Methods draft [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Illumina MiSeq / NextSeq / NovaSeq (paired- and single-end FASTQ); MGI DNBSEQ-G400 and related DNBSEQ instruments (2×150 or 2×200 PE FASTQ; [5]); Ion Torrent 16S (`denoise-pyro`) and PacBio CCS amplicons (`denoise-css`) when scoped at kickoff |
| Input requirements | Demultiplexed FASTQ + sample metadata; primer and hypervariable region documented; high-biomass stool/soil typically ≥30,000-50,000 PE reads (rarefaction depth set from alpha-rarefaction curves at kickoff); low-biomass swabs/biopsies often need higher depth, confirmed at kickoff; standard cohort ≤24 samples, one contrast |
| Reference builds supported | SILVA 138 (16S/18S, default); Greengenes2 2022.10 when primers match classifier training set; UNITE 9.0 for ITS-only projects scoped separately |
| Primary tools (with versions) | QIIME2 2024.10; DADA2 (q2-dada2) 2024.10.0; q2-cutadapt; fastp 0.24.0; FastQC 0.12.1; MultiQC 1.25.1; phyloseq 1.48.0; vegan 2.6-8.1; ANCOM-BC 2.4.0; MaAsLin2 1.18.0; PICRUSt2 2.5.2 (on request) |
| Typical turnaround time | 3-5 weeks (standard ≤24 samples, one contrast); 5-8 weeks (multi-run merges, multi-contrast designs, or bespoke extensions). Confirmed at kickoff |
| Deliverable formats | ASV abundance tables (`.csv`, `.biom`, `.qza` on request); taxonomy tables; diversity and differential-abundance results; PDF/SVG figures; HTML MultiQC report; commented R/Python scripts; Methods draft |
| Key cited best-practice reference | Callahan et al. [1], Nature Methods; Bokulich et al. [6], Microbiome |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, e.g. PICRUSt2 functional inference, pre-built ASV table re-analysis, client-specified filters, co-occurrence or network extensions, custom reference databases, or alternative differential-abundance models |
16S rRNA amplicon sequencing infers amplicon sequence variants (ASVs) from demultiplexed reads, assigns taxonomy against curated reference databases, and applies compositional statistics. It covers the analysis, not library prep or PCR. DADA2 resolves variants that differ by one nucleotide [1]. Compared with shotgun metagenomics, 16S targets one marker gene and, with short-read V4 amplicons, typically resolves taxa to genus [3]. Functional profiling uses inference tools such as PICRUSt2 when that work is in scope. The Human Microbiome Compendium reprocessed 168,464 public gut 16S samples [4]. Pepkio starts from FASTQs or ASV tables and returns version-pinned outputs. See the 16S amplicon sequencing glossary.
16S amplicon sequencing fits cost-sensitive cohort studies where genus-level taxonomic profiling and alpha/beta diversity are the main endpoints.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| 16S amplicon (ASV) | Large cohorts, longitudinal stool/soil studies, genus-level community profiling | Limited species/strain resolution with short-read HV amplicons [3]; primer bias; no direct pathway quantification without inference | Lowest sequencing + bioinformatics cost per sample |
| Shotgun metagenomics | Species/strain resolution, functional pathways, MAG recovery | Higher per-sample cost; host DNA and low biomass reduce effective microbial depth [7] | Higher than 16S; shallow shotgun (~0.5 M reads) can recover species-level profiles at similar per-sample cost to 16S [7] |
| Metatranscriptomics | Active gene expression in mixed communities | RNA instability; rRNA depletion required; highest analytical complexity | Highest complexity and cost |
Pepkio verifies FASTQ integrity, read layout, and records sample IDs, condition, batch, sequencing run, primers, and hypervariable region in `sample_manifest.csv`. Missing primer metadata or mismatched read structure is flagged before import.
Tools used: Custom validation scripts; MD5 checksum verification
Output: sample_manifest.csv with sample IDs, platform, read counts, primer/HV region, and QC flags
Adapter contamination, low-quality tails, and overrepresented sequences are assessed before denoising [10, 11]. Per-sample read counts and quality distributions are aggregated for review.
Tools used: FastQC 0.12.1; fastp 0.24.0; MultiQC 1.25.1
Output: Per-sample FastQC/fastp reports; multiqc_report.html
Reads import via sample manifest. Pepkio confirms Phred+33 encoding and records per-sample read counts in the demultiplex summary [6].
Tools used: QIIME2 2024.10 qiime tools import
Output: demux.qza; demux-summary.qzv with per-sample read counts and quality score boxplots
Client-supplied primers are removed with q2-cutadapt before DADA2. Untrimmed primers inflate chimera rates [6].
Tools used: QIIME2 2024.10 q2-cutadapt
Output: trimmed-demux.qza; primer-trim statistics table
DADA2 runs separately on each sequencing run because error profiles differ by instrument and flowcell [1, 5]. Truncation lengths (`p-trunc-len-f/r`) are set from quality plots. Paired-end merging requires sufficient overlap between reads. Low read retention triggers parameter review before proceeding.
Tools used: QIIME2 2024.10 q2-dada2 (denoise-paired, denoise-single, denoise-pyro, or denoise-css when scoped)
Output: feature-table.qza; rep-seqs.qza; dada2-stats.qza with input, filtered, denoised, merged, and non-chimeric read counts per sample
Feature tables and representative sequences from separate runs merge into a single cohort table. Mitochondrial, chloroplast, and unassigned features are removed. Low-frequency features below the documented prevalence threshold are filtered [12].
Tools used: QIIME2 2024.10 feature-table merge; filter-features; custom filtering scripts
Output: table-filtered.qza; rep-seqs-filtered.qza; asv_filter_log.csv documenting removed features and thresholds
ASVs classify against a Naive Bayes classifier trained on SILVA 138 (default) or Greengenes2 2022.10 when primers match the training amplicon [6]. Classification confidence scores are retained for downstream filtering.
Tools used: QIIME2 2024.10 classify-sklearn
Output: taxonomy.qza; taxonomy.tsv with Feature ID, Taxon, and Confidence
Representative sequences are aligned and a phylogenetic tree is built. Alpha diversity (Shannon, Faith phylogenetic diversity) and beta diversity (weighted and unweighted UniFrac) are then computed on rarefied or normalized tables as documented in the Methods draft. PERMANOVA tests group differences with covariates recorded when provided [13, 14].
Tools used: QIIME2 2024.10 phylogeny align-to-tree-mafft-fasttree; q2-diversity; phyloseq 1.48.0; vegan 2.6-8.1
Output: rooted-tree.qza; diversity_results/ (alpha and beta metrics); permanova_results.csv; rarefaction curves
ANCOM-BC is the default compositional differential-abundance method [15]. MaAsLin2 fits multivariable models with continuous and categorical covariates when scoped [16]. Contrasts, reference levels, and covariates are confirmed at kickoff.
Tools used: ANCOM-BC 2.4.0; MaAsLin2 1.18.0 (when scoped)
Output: da_results_<contrast>.csv with taxon, effect size, p-value, q-value, and contrast metadata
ASV tables, taxonomy, diversity results, figures, scripts, README, Methods draft, and HTML QC report are assembled per agreed retention policy.
Tools used: MultiQC 1.25.1; custom export and plotting scripts
Output: Deliverable bundle with processed tables, figures, code, QC report, and Methods draft
For high-biomass samples (stool, soil), Pepkio typically recommends ≥30,000-50,000 paired-end reads per sample, with rarefaction depth set from alpha-rarefaction curves at kickoff. Low-biomass swabs or biopsies often need higher depth. Standard projects cover ≤24 samples with one primary contrast. Larger cohorts are scoped at kickoff.
Yes, with documented caveats. Samples with low read counts after DADA2 are flagged in dada2_denoising_stats.csv and may lack power for rare taxa or subgroup comparisons. Pepkio proceeds when clients accept reduced sensitivity. Re-sequencing is discussed when input reads are very low for the matrix type.
Yes for Illumina MiSeq, NextSeq, and NovaSeq paired- and single-end FASTQs. MGI DNBSEQ FASTQs (e.g., G400; 2×150 or 2×200) are supported with DADA2 parameters tuned per read length [5]. Ion Torrent data uses denoise-pyro. PacBio CCS amplicons use denoise-css when scoped at kickoff.
Standard projects (≤24 samples, one contrast, single or few sequencing runs) typically complete in 3-5 weeks. Multi-run merges, multi-contrast designs, PICRUSt2 extensions, or cohorts >24 samples may take 5-8 weeks. All timelines are confirmed at kickoff.
Sequencing run, extraction kit, and primer choice are recorded in metadata and QC reports. DADA2 runs per sequencing run. PERMANOVA and MaAsLin2 can include batch as a covariate when scoped. Abdill et al. [4] showed primer and extraction kit associate with compositional variation. Pepkio documents these confounders rather than over-interpreting batch-driven shifts.
Yes. You retain full ownership of code, scripts, and results. Pepkio delivers commented QIIME2 export scripts plus R or Python analysis code with conda lockfiles. Jupyter or R Markdown notebooks are available on request.
Yes. Checkpoint reviews occur after raw QC, after DADA2 denoising (before differential testing), and before final delivery. You can review metadata, covariates, filtering thresholds, and contrasts within agreed scope. A PhD-level bioinformatician is your primary contact at each milestone.
Clarification of methods, DADA2 parameters, filtering logic, and minor figure or table revisions within agreed scope (typically up to 20% of deliverables). Substantial new analyses, such as additional contrasts, re-running with alternate databases, or PICRUSt2 extensions, are scoped as separate milestones.
No. Pepkio operates as a fee-for-service provider and does not require co-authorship unless that is discussed in advance.
Primer choice affects taxonomic coverage and resolution. Johnson et al. [3] showed short-read hypervariable-region amplicons cannot match full-length 16S species-level resolution. V4 (515F/806R) is widely used in Earth Microbiome Project protocols [17]. V3-V4 amplicons (~460 bp) offer broader coverage but require sufficient paired-end overlap. Confirm read length against amplicon size at kickoff.
ASVs represent exact sequences and avoid arbitrary clustering thresholds that merge distinct variants [1]. In mock-community benchmarks, denoising removed spurious sequences more effectively than distance-based OTU picking. Legacy OTU output for method comparison is scoped on request.
Yes, when scoped at kickoff. PICRUSt2 functional inference, co-occurrence or network analysis, pre-built ASV table re-analysis with alternate databases, client-specified differential-abundance models, and custom reference classifiers are agreed before analysis begins.
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