Metagenomics
Metatranscriptomic RNA shows which microbes are active, not just present [1], via rRNA-filtered reads, MetaPhlAn and HUMAnN tables, differential-activity tests, and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Illumina NovaSeq X / 6000 / NextSeq 2000, HiSeq 2500/4000; MGI DNBSEQ-T7 / G400 / G99 when scoped at kickoff; ribo-depleted total RNA (e.g., Ribo-Zero, QIAseq FastSelect, NEBNext rRNA Depletion); stranded libraries when metadata provided; pre-built HUMAnN or MetaPhlAn outputs accepted on request |
| Input requirements | ≥40-50 million raw paired-end reads per human gut sample for >90% accuracy on low-abundance transcript estimates after annotation [2]; ≥2×100 bp or 2×150 bp typical; ≥3 biological replicates per condition recommended for differential testing (fewer flagged at kickoff); RIN and RNA extraction metadata encouraged; paired shotgun metagenomes optional for gene-copy normalization |
| Reference builds supported | ChocoPhlAn SGB (Jun 2023) for HUMAnN 3.9; MetaPhlAn 4.1.0 marker database; SILVA 138 (SortMeRNA rRNA filtering); UniRef90 for HUMAnN; host subtraction against GRCh38 or GRCm39 when scoped |
| Primary tools (with versions) | SortMeRNA 4.3.7; HUMAnN 3.9; MetaPhlAn 4.1.0; Kraken2 2.1.3; Bracken 2.9; Bowtie2 2.5.4; Salmon 1.10.3; fastp 0.24.0; FastQC 0.12.1; MultiQC 1.25.1; MaAsLin2 1.18.0; ANCOM-BC 2.4.0; MEGAHIT 1.2.9 (assembly scoped on request) |
| Typical turnaround time | 5-8 weeks (standard cohort, ≤24 samples, one contrast, profiling through differential activity); multi-contrast or paired DNA+RNA designs may extend the timeline. Confirmed at kickoff |
| Deliverable formats | HUMAnN pathabundance.tsv and genefamilies.tsv; MetaPhlAn relative-abundance profiles; differential-activity tables (.csv); PDF/SVG figures; HTML MultiQC report; commented R/Python scripts; Methods draft |
| Key cited best-practice reference | Zhang et al. [1], Annual Review of Biomedical Data Science; Franzosa et al. [3], Nature Methods (HUMAnN functional profiling) |
| Custom / bespoke analysis | Paired DNA normalization, Salmon assembly quantification, co-expression networks, AMR/virulence panels, custom references, or client-specified models. Scoped at kickoff |
Metatranscriptomics aligns and quantifies RNA-seq reads from mixed microbial communities to measure which genes and pathways are transcribed at sampling time, not only those encoded in community DNA. Shotgun metagenomics reports gene catalog presence and copy number; metatranscriptomics captures activity such as nutrient utilization and stress responses [3]. Unlike 16S amplicon sequencing, it measures functional expression instead of inferring activity from taxonomy. Automated platforms have profiled cohorts exceeding 10,000 human stool samples [4]. Pepkio processes ribo-depleted FASTQs through host and rRNA filtering, taxonomic activity profiling, and HUMAnN pathway quantification with documented parameters. Custom entry points are agreed at kickoff. See the metatranscriptomics glossary.
Metatranscriptomics fits when the question is active microbial function rather than static community membership, for example pathway changes after treatment, transcriptional response to host immune activation, or time-resolved community activity.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Metatranscriptomics | Active pathway and gene-family expression; perturbation and treatment-response studies; pairing with host phenotypes | RNA degradation; rRNA depletion kit bias; several-fold higher depth than metagenomics for rare transcripts [5] | Higher per-sample sequencing and bioinformatics than 16S or shallow shotgun |
| Shotgun metagenomics | Species/strain catalog, gene presence, MAG recovery, gene copy number | Does not distinguish expressed from silent genes | Moderate to high sequencing and storage cost |
| 16S rRNA amplicon | Large cohorts, cost-effective taxonomy, longitudinal membership tracking | No direct functional expression; species resolution limited | Lowest per-sample cost |
Pepkio confirms FASTQ integrity (MD5 checksums), read layout, platform, ribo-depletion method, and experimental design. Sample metadata are recorded in sample_manifest.csv. Designs with fewer than three biological replicates per condition are flagged before differential testing.
Tools used: Custom validation scripts; md5sum
Output: sample_manifest.csv with library IDs, read counts, depletion kit, host species, and QC flags
Adapter contamination, low-quality tails, and overrepresented sequences are assessed per library. When trimming is warranted, reads are filtered before downstream steps [8]. Aggregated metrics are compiled for review [9].
Tools used: FastQC 0.12.1; fastp 0.24.0; MultiQC 1.25.1
Output: Per-sample FastQC/fastp reports; multiqc_report.html
In mucosal swabs and tissue, host RNA can dominate without subtraction [5]. Stool often shows low host fractions [2]; host mapping is applied when scoped. Excessive host fractions are flagged before profiling.
Tools used: Bowtie2 2.5.4; GRCh38 or GRCm39 reference index
Output: Host-depleted FASTQ; host_removal_summary.csv with columns: sample_id, total_reads, host_reads, host_fraction
Remaining rRNA is removed after experimental depletion. Kit depletion leaves species-skewed fractions; SortMeRNA filtering improves functional yield [2, 5].
Tools used: SortMeRNA 4.3.7; SILVA 138 SSU/LSU databases
Output: rRNA-depleted FASTQ; rrna_filter_summary.csv with columns: sample_id, reads_pre_filter, rrna_reads, rrna_fraction, reads_post_filter
MetaPhlAn 4.1.0 estimates relative abundance of transcriptionally active lineages via marker genes [10]. Kraken2 2.1.3 + Bracken 2.9 is run as an optional cross-check when scoped [11].
Tools used: MetaPhlAn 4.1.0; Kraken2 2.1.3 + Bracken 2.9 (on request)
Output: metaphlan_profile.tsv; optional kraken2_bracken_abundance.csv
HUMAnN 3.9 profiles microbial pathways and gene families from filtered reads, stratified by MetaPhlAn community composition [3]. Reads are mapped to ChocoPhlAn SGB (Jun 2023) and UniRef90; pathway abundances are reported in reads per kilobase (RPK).
Tools used: HUMAnN 3.9; ChocoPhlAn SGB (Jun 2023); UniRef90
Output: pathabundance.tsv; genefamilies.tsv; per-sample HUMAnN logs
Pathway and gene-family tables are transformed (CPM, log-CPM, or CLR as agreed at kickoff). Library sizes, detection rates, and sample correlations are audited; PCA or NMDS ordination is reviewed for batch effects. Samples below agreed depth thresholds are flagged before testing [2].
Tools used: R vegan 2.6-8.1; custom Python/R audit scripts
Output: expression_qc_summary.csv; PCA/NMDS ordination plots; sample correlation heatmap
MaAsLin2 1.18.0 fits multivariable models with covariates (condition, batch, age, BMI, etc.) on transformed tables for pathway and taxonomic features as agreed at kickoff [12]. ANCOM-BC 2.4.0 is used for compositional taxonomic features when appropriate [13]. Benjamini-Hochberg q-values control FDR across tested features.
Tools used: MaAsLin2 1.18.0; ANCOM-BC 2.4.0
Output: da_results_<contrast>.csv with columns: feature, coef, stderr, pval, qval, N; MaAsLin2 coefficient plots
When matched shotgun metagenomes are available, HUMAnN expression estimates can be adjusted for gene copy number to distinguish transcriptional upregulation from DNA abundance changes [3]. This step is scoped at kickoff when paired DNA FASTQs or pre-computed metagenomic profiles are provided.
Tools used: HUMAnN 3.9 --taxonomic-profile; MetaPhlAn 4.1.0 on paired DNA
Output: pathabundance_copy_normalized.tsv; normalization log documenting paired sample mapping
Pathway heatmaps, taxonomic barplots, and differential-activity plots are exported at publication resolution. Commented scripts reproduce agreed pipeline stages within project scope. A Methods draft cites software versions and database builds [14]. Salmon 1.10.3 transcript-level quantification against de novo or reference assemblies is available when clients need gene-level counts beyond HUMAnN gene families, scoped at kickoff [15].
Tools used: R/Python plotting scripts; documented workflow archive
Output: PDF/SVG figures; R/Python scripts; README; Methods draft; final deliverable bundle
For human gut metatranscriptomes, Westreich et al. [2] recommend ribo-depleted, 100 bp paired-end sequencing with 40-50 million raw reads per sample, yielding roughly 5-10 million annotated reads and >90% accuracy on low-abundance transcript estimates. At least three biological replicates per condition are recommended for differential testing; fewer are flagged at kickoff. Environmental or low-biomass matrices may need project-specific depth targets. Pepkio confirms thresholds at kickoff based on sample matrix and study goals.
Yes, with caveats documented in the QC report. Samples with low RIN, insufficient reads after host and rRNA filtering, or high residual rRNA fractions may lack power for rare pathway detection [5]. Outlier samples in ordination are flagged before differential testing. Re-sequencing is discussed when yield threatens the study question.
Yes. Pepkio processes ribo-depleted Illumina FASTQs from NovaSeq X, 6000, NextSeq 2000, and HiSeq instruments using the standard workflow. MGI DNBSEQ-T7, G400, and G99 FASTQs are processed when scoped at kickoff, with adapter and QC validation in the report. Pre-built HUMAnN or MetaPhlAn outputs from either platform can be imported when upstream processing is complete.
A standard project (roughly 4-24 samples, one primary contrast, profiling through differential activity) typically completes in 5-8 weeks from data receipt. Multi-contrast designs, paired DNA+RNA normalization, assembly-based quantification, or >24 samples may extend the timeline. Milestone check-ins occur during QC, after profiling, and before delivery. Exact timelines are confirmed at kickoff.
When batch is known and not fully confounded with condition, Pepkio includes batch as a covariate in MaAsLin2 models [12]. PCA and correlation heatmaps are reviewed before modeling. rRNA depletion kit lot and extraction batch are recorded in sample_manifest.csv. Post-hoc correction beyond the design formula is scoped separately when required.
Yes. You retain full ownership of all code, scripts, and results. Pepkio delivers commented R and Python scripts with conda lockfiles or sessionInfo() exports so you can rerun agreed stages on Linux or HPC. Tables use standard .csv and .tsv formats. Deliverables are organized by pipeline stage with README instructions. R Markdown or Jupyter notebooks are available on request.
Yes. Checkpoint reviews occur after QC, after host/rRNA filtering, and before final delivery. Within agreed scope, you can review metadata, covariate choices, filtering thresholds, and contrast definitions before final statistics. A PhD-level scientific contact leads the project, coordinates milestone feedback, and records decisions in the shared project file.
Clarification of methods, QC thresholds, database builds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Methods drafts cover analyses Pepkio performed. Substantial new reviewer requests (additional contrasts, assembly-based requantification, or new covariate models) are scoped as separate milestones with updated pricing and timeline estimates.
No. Pepkio does not require co-authorship unless that is discussed and agreed in writing before project start. Standard statements of work are fee-for-service with no authorship conditions. Acknowledgment of bioinformatics support in Methods or Acknowledgments is standard practice.
No, but paired DNA improves interpretation. Metagenomic DNA reports gene presence and copy number; metatranscriptomics reports expression. Franzosa et al. [3] showed that copy-number normalization with paired DNA distinguishes transcriptional upregulation from abundance changes. Pepkio analyzes RNA-only projects and documents this limitation. Paired DNA normalization is scoped when metagenomic FASTQs or profiles are available.
Pepkio runs SortMeRNA 4.3.7 against SILVA 138 after experimental ribodepletion to remove residual rRNA reads [2, 16]. rRNA fractions before and after filtering are reported per sample. Samples with high post-filter rRNA or low mRNA yield are flagged in the QC report before HUMAnN profiling.
Yes, when scoped at kickoff. HUMAnN gene-family tables can be filtered to client-specified gene sets annotated against CARD, VFDB, or custom reference databases. Assembly-based Salmon quantification and co-expression networks are supported as custom extensions beyond the standard HUMAnN workflow. Annotation scope, reference versions, and feasibility are confirmed before analysis begins.
Tell us:
We'll tell you:
Contact us to start with a free consultation. Need everyday bench calculators? Try our free lab tools.
