Metagenomics
Shotgun metagenomic DNA becomes taxonomic profiles and HUMAnN pathway tables with pinned MetaPhlAn or Kraken workflows, figures, scripts, and a Methods draft [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Illumina NovaSeq X / 6000 / NextSeq 2000, MiSeq; MGI DNBSEQ T7, G400, and G99 (paired-end FASTQ; read-header normalization when scoped at kickoff). Cross-platform profiles can be compared when library prep, profiling strategy, and depth are harmonized [4] |
| Input requirements | Paired-end FASTQ (≥2×100-150 bp typical); ≥0.5-1.0 Gb for reference-based species/strain profiling [2]; ≥2 Gb for pathway-level completeness [2]; ≥15 million reads for species-level MWAS [5]; ≥10 Gb for MAG co-assembly when scoped [2, 6]. Sample count and contrasts confirmed at kickoff |
| Reference builds supported | GTDB release 214; Kraken2 PlusPF / PlusPF16G; ChocoPhlAn SGB (Jun 2023) for HUMAnN 3.9; host removal against GRCh38 or GRCm39; custom reference databases scoped at kickoff |
| Primary tools (with versions) | fastp 0.24.0; FastQC 0.12.1; KneadData 0.12.0; Bowtie2 2.5.4; MetaPhlAn 4.1.0; Kraken2 2.1.3; Bracken 2.9; HUMAnN 3.9; MEGAHIT 1.2.9; MetaBAT2 2.17; CheckM2 1.0.2 (MAG, on request); ANCOM-BC 2.4.0; MaAsLin2 1.18.0; phyloseq 1.48.0; vegan 2.6-8.1; MultiQC 1.25.1 |
| Typical turnaround time | 3-5 weeks (standard cohort ≤24 samples, profiling + one contrast); 5-8 weeks (deep shotgun, MAG recovery, multi-contrast designs). Confirmed at kickoff |
| Deliverable formats | MetaPhlAn/Kraken abundance tables (.tsv, .csv); HUMAnN pathabundance and genefamilies; diversity and differential-abundance tables; PDF/SVG figures; HTML MultiQC report; commented R/Python scripts; Methods draft |
| Key cited best-practice reference | Quince et al. [1], Nature Biotechnology; Meyer et al. [6], Nature Methods (CAMI II); Treichel et al. [2], Nature Microbiology |
| Custom / bespoke analysis | StrainPhlAn tracking, custom Kraken2/MetaPhlAn databases, MAG binning, client-specified statistical models, non-standard inputs or outputs. Scoped at kickoff |
Shotgun metagenomics sequences all DNA in a mixed microbial community without PCR targeting a single marker gene, then assigns reads to taxa and functional categories by reference mapping or assembly [1]. Unlike 16S amplicon sequencing, it can profile bacteria, archaea, fungi, and viruses at higher resolution when databases and depth support detection, and it quantifies pathways from genomic reads rather than marker-gene proxies. In paired stool samples, shotgun metagenomes yielded 47-92 million reads alongside >300,000 16S reads [3]. Pepkio processes Illumina and MGI FASTQs through host depletion, dual taxonomic profiling, and HUMAnN. MAG recovery and other custom extensions are agreed at kickoff. See the shotgun metagenomics glossary.
Shotgun metagenomics fits when you need species- or strain-level taxonomy, direct functional pathway quantification, or detection of non-bacterial community members. The table compares shotgun with 16S amplicon and metatranscriptomics.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Shotgun metagenomics | Species/strain taxonomy, MetaCyc pathway quantification, viruses/fungi/archaea | Higher per-sample cost than 16S; host DNA reduces effective microbial depth; MAG and proteome goals need deep sequencing (>10 Gb) [2] | Library prep + sequencing + bioinformatics vary by depth, sample count, and MAG scope |
| 16S rRNA amplicon | Large cohorts, cost-effective community-shift detection | Poorer species and functional resolution relative to shotgun [7] | Lowest sequencing cost per sample |
| Metatranscriptomics | Active gene expression and treatment-response dynamics | rRNA depletion, RNA stability, and higher lab complexity [8] | Higher prep and compute than DNA shotgun |
Pepkio verifies FASTQ integrity (MD5), read length, paired-end structure, and platform. Sample matrix, host species, batch, and contrasts are recorded in sample_manifest.csv; sub-threshold yield is flagged [1]. MGI read-header normalization when scoped at kickoff.
Tools used: md5sum; custom validation scripts
Output: sample_manifest.csv with sample IDs, platform, read counts, host species, and QC flags
FastQC assesses per-base quality, adapter content, and duplication; fastp trims adapters and low-quality ends when needed [12]. Low Q30 yield or extreme adapter contamination is flagged before host removal. MultiQC aggregates per-sample metrics [13].
Tools used: FastQC 0.12.1; fastp 0.24.0; MultiQC 1.25.1
Output: fastqc/ reports; fastp.json / fastp.html; multiqc_report.html
KneadData trims, filters, and removes host DNA with Bowtie2 against GRCh38 or GRCm39 [7]. Host-depletion rate and post-filter read counts are reported because host DNA confounds shallow metagenomics [2, 8]. Insufficient microbial reads after depletion are flagged before profiling.
Tools used: KneadData 0.12.0; Bowtie2 2.5.4
Output: Host-depleted FASTQ; host_depletion_summary.csv with pre/post read counts and pct host removed
MetaPhlAn 4 maps reads to clade-specific marker genes in the SGB catalog and estimates relative abundance at species and strain level where markers support it [7]. Unclassified read fraction is compared against depth expectations before downstream testing.
Tools used: MetaPhlAn 4.1.0
Output: Per-sample MetaPhlAn profiles; merged metaphlan4_species.tsv
Kraken2 assigns reads by k-mer matching against PlusPF [14]; Bracken re-estimates species-level abundance [14]. Results are cross-checked against MetaPhlAn to flag phantom-taxa patterns at high depth [15, 16].
Tools used: Kraken2 2.1.3; Bracken 2.9
Output: Kraken2 reports; merged kraken2_bracken_species.tsv
HUMAnN 3 maps reads to ChocoPhlAn pangenomes and UniRef gene families, then aggregates MetaCyc pathway abundances [8]. Samples below agreed pathway depth are flagged before differential testing [2].
Tools used: HUMAnN 3.9
Output: pathabundance.tsv; genefamilies.tsv; per-sample HUMAnN logs with mapping statistics
MEGAHIT co-assembles host-depleted reads; MetaBAT2 bins contigs; CheckM2 assesses completeness and contamination [1, 17, 18]. MAG chimerism limits are documented because even high-quality MAGs may not represent a single strain [2, 6]. Optional; scoped at kickoff.
Tools used: MEGAHIT 1.2.9; MetaBAT2 2.17; CheckM2 1.0.2
Output: {sample_or_cohort}.contigs.fa; {bin}.fa MAGs; mag_qc_summary.csv with completeness, contamination, and CheckM2 lineage
Alpha diversity (Shannon, Simpson, observed richness) and beta diversity (Bray-Curtis, Aitchison distance after CLR transform) are computed on rarefied or transformed abundance tables [19]. PCoA or NMDS ordination and PERMANOVA test community separation by metadata factors [20].
Tools used: phyloseq 1.48.0; vegan 2.6-8.1
Output: alpha_diversity.csv; beta_diversity_distance_matrix.csv; PERMANOVA results table
Taxa and pathways are tested with ANCOM-BC or MaAsLin2 for compositional data with covariate adjustment [21, 22]. Benjamini-Hochberg FDR unless a pre-specified alternative is agreed at kickoff.
Tools used: ANCOM-BC 2.4.0; MaAsLin2 1.18.0
Output: da_results_<contrast>.csv with feature-level coefficients, standard errors, p-values, and q-values
MultiQC aggregates QC metrics across samples. Figure-ready plots, commented scripts, README, and a Methods draft listing exact tool versions and database builds are packaged per agreed retention policy [6].
Tools used: R/Python plotting scripts; MultiQC 1.25.1
Output: PDF/SVG figures; final deliverable bundle with scripts, README, Methods draft, and HTML QC report
For species-level taxonomic profiling, Pepkio typically uses ≥0.5-1.0 Gb per sample when reference databases cover the community [2]. Pathway-level inference often needs ≥2 Gb; species-level MWAS benefits from ≥15 million reads [5]. Sample count depends on effect size, prevalence, and contrast design; minimum n and power are confirmed at kickoff. MAG recovery needs ≥10 Gb and is scoped separately.
Yes, with caveats. Low Q30 yield, high adapter content, or insufficient post-host-depletion reads reduce taxonomic and functional sensitivity [2]. Sub-threshold samples are flagged in the QC report; re-sequencing is discussed before full differential testing. Shallow shotgun (2-5 million reads) showed high concordance with deep shotgun for alpha/beta diversity and species composition in dense longitudinal stool sampling [7], but rare-taxa and strain-level claims need higher depth.
Yes. Illumina NovaSeq X, 6000, NextSeq 2000, and MiSeq paired-end FASTQs use the standard workflow. MGI DNBSEQ T7, G400, and G99 paired-end FASTQs are supported; read-header normalization is applied when scoped at kickoff. Cross-platform profiles can be compared when library prep, profiling strategy, and depth are harmonized [4]. Ion Torrent and long-read metagenomics are scoped separately when feasibility is confirmed during intake.
Standard projects (roughly 4-24 samples, host depletion, dual profiling, HUMAnN, one contrast, differential abundance) typically complete in 3-5 weeks. Deep shotgun with MAG recovery, multi-contrast designs, or >24 samples may take 5-8 weeks. Milestone check-ins occur after QC, after profiling, and before delivery. Exact timelines are confirmed at kickoff.
Batch effects from extraction kit, sequencing center, and flowcell can exceed biological signal if unaddressed [1, 4]. Pepkio records batch covariates in sample_manifest.csv, stratifies QC by batch, and includes batch in MaAsLin2 or ANCOM-BC when specified at kickoff. Batch randomization across conditions is recommended at study design. Correction beyond standard covariate adjustment is scoped when needed.
Yes. You retain full ownership of code, scripts, and results. Pepkio delivers commented bash, R, and Python scripts with conda lockfiles or sessionInfo() exports so you can rerun from raw FASTQs on Linux or HPC. Deliverables are organized by pipeline stage with README instructions. Jupyter or R Markdown notebooks are available on request.
Yes. Checkpoint reviews occur after QC and host depletion, after taxonomic and functional profiling, and before final delivery. Within agreed scope, you can review metadata, covariates, filtering thresholds, and contrasts before final statistics. A PhD-level scientific contact leads the project, coordinates milestone feedback, and documents decisions in the project record.
Clarification of methods, QC thresholds, database builds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Methods and Supplementary drafts are included for analyses Pepkio performed. Substantial new analyses, re-analysis with new contrasts, or major rewrites requested after delivery are scoped as separate milestones with updated pricing.
No. Pepkio does not require co-authorship unless that is discussed and agreed in writing before project start. Acknowledgment of bioinformatics support in the Methods or Acknowledgments section is standard practice.
Treat concordant species-level calls as higher confidence. Treat Kraken2-only low-abundance taxa with caution, because k-mer classifiers can misassign reads from abundant species at high depth [15]. MetaPhlAn-only calls reflect marker-gene coverage and may miss taxa absent from the SGB catalog. Pepkio delivers both profiles and documents discordance in the QC report. Consensus filtering or integrative approaches are scoped at kickoff when a single feature table is required.
Host DNA is a confounder in shallow metagenomics and reduces effective microbial depth [2]. Pepkio reports host-depletion rate per sample. Samples with very high pre-depletion host fractions often retain too little microbial depth for pathway or rare-taxa analysis even after KneadData. Sample-specific thresholds are confirmed at kickoff based on matrix (stool vs swab vs environmental).
Yes. MAG co-assembly and binning, StrainPhlAn strain tracking, custom Kraken2 or MetaPhlAn databases, and client-specified statistical models are scoped at kickoff with milestone pricing. Resistome or other specialized profiling is available when reference databases and study design support the endpoint. Pepkio also accepts pre-built MetaPhlAn, Kraken2, or HUMAnN profiles when clients need downstream statistics only.
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