Environmental Metagenomics
Shotgun metagenomics from any habitat: assembly, binning into MAGs, and taxonomic and functional profiling across samples.
Research Area
Environmental Science & Environmental Genomics
From a scoop of soil to a litre of seawater, environmental DNA carries a record of the community that lives there. We bring the computational depth to read it—metagenomics, microbiome profiling, eDNA and biodiversity, and functional analysis—for research, with detectability and relative-abundance limits stated as plainly as the results.
16S/ITS · shotgun metagenomics
eDNA · biodiversity · function
Detection, not census · no guarantees
Sample output
ProteobacteriaBacteroidetesActinobacteriaFirmicutes
Overview
Reading the genomics of whole environments
Environmental genomics studies the DNA of entire communities—the bacteria, archaea, fungi, and eukaryotes that share a habitat, plus the traces organisms shed into soil, water, and sediment. The questions that matter—who is there, in what relative proportion, how communities differ between sites or over time, and what they can potentially do—need methods built for compositional, sparse, high-dimensional data, not generic pipelines.
We bring those methods to your environmental data. From amplicon and shotgun profiling and metagenome assembly to diversity statistics, differential abundance, functional and pathway analysis, and eDNA biodiversity, we apply established, community-standard tools against trusted reference databases—and return results documented for reproducibility. This is research work: eDNA reports detectability rather than a census, and abundances are relative, with both limits stated plainly.
Applications
What we analyse in environmental genomics
The computational analyses that turn environmental sequence into ecology and function—from amplicon profiling and metagenomics to eDNA and bioremediation.
16S / ITS Microbiome Profiling
Amplicon profiling to ASV resolution with reference-based taxonomy for bacteria, archaea, and fungi.
Microbial Ecology & Diversity
Alpha and beta diversity, ordination, and differential abundance to quantify how communities differ and shift.
eDNA & Metabarcoding
Biodiversity detection and community profiling from environmental DNA, with rarefaction and richness—detection, not census.
Soil & Sediment Microbiomes
Soil, rhizosphere, and sediment communities linked to nutrient cycling, plant health, and habitat function.
Aquatic & Marine Microbiomes
Freshwater, marine, and aquaculture microbiomes across water columns, sites, and environmental gradients.
Functional & Bioremediation
Pathway and functional-gene profiling, including degradation pathways and community change over remediation timelines.
Comparative & Longitudinal
Cross-sample and time-series analysis to track community succession, treatment effects, and recovery.
The Pipeline
A representative environmental-genomics workflow
A transparent sequence from raw reads to interpreted ecology—each step suited to the data type and documented for reproducibility.
Adapted to your study: amplicon vs. shotgun, single survey vs. time series, one habitat vs. a gradient. We confirm the plan with you before any compute begins.
Intake & QC
Raw reads are quality-checked—read quality, adapter and primer removal, and contamination screening.
Tools: fastp · cutadapt · FastQC
Denoising / assembly
Amplicon reads are denoised to ASVs; shotgun reads are assembled and binned into MAGs where relevant.
Tools: DADA2 · MEGAHIT · MetaBAT2
Taxonomic profiling
Communities are profiled against curated references for bacteria, archaea, fungi, and eukaryotes.
Tools: Kraken2 · MetaPhlAn · SILVA
Functional profiling
Gene and pathway content is annotated to describe metabolic potential—including degradation pathways.
Tools: HUMAnN · eggNOG · KEGG
Diversity & statistics
Alpha/beta diversity, ordination, and differential-abundance testing quantify structure and differences.
Tools: phyloseq · vegan · ANCOM-BC
Comparison & modelling
Cross-sample, gradient, and time-series modelling relates community change to conditions and covariates.
Tools: MaAsLin2 · MMUPHin · DESeq2
Interpretation & reporting
Results become clear figures, documented methods and versions, and honest statements of detection limits.
Tools: ggplot2 · Krona · versioned manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Amplicon & QC
DADA2QIIME 2mothurcutadaptfastp
Taxonomy & databases
Kraken2BrackenMetaPhlAnSILVAGTDB
Metagenome assembly
MEGAHITmetaSPAdesMetaBAT2CheckM
eDNA & metabarcoding
OBIToolsDECIPHERBOLDvsearch
Function & pathways
HUMAnNeggNOG-mapperPICRUSt2KEGG
Diversity & stats
phyloseqveganDESeq2ANCOM-BCMaAsLin2
Comparative & longitudinal
MMUPHinFAPROTAXtime-series
Visualization
ggplot2KronaiTOLpheatmap
Key Databases
The reference resources we draw on
Environmental analysis is powered by curated taxonomies, marker references, and functional resources. We build on the community's authoritative, versioned resources.
- SILVA
- Curated rRNA gene reference for 16S/18S taxonomy.
- GTDB
- Genome Taxonomy Database for standardized microbial taxonomy.
- UNITE
- Reference database for fungal ITS identification.
- NCBI RefSeq
- Reference genomes and annotation across microbial species.
- KEGG
- Pathways and orthology for functional interpretation.
- eggNOG
- Orthology and functional annotation resource.
- BOLD
- Barcode of Life Data System for eDNA and metabarcoding.
- MGnify
- EBI metagenomics resource of analysed environmental datasets.
- PR2
- Protist Ribosomal Reference for eukaryotic 18S.
Choosing an Approach
16S/ITS amplicon vs. shotgun vs. eDNA metabarcoding
Different sequencing strategies answer different environmental questions. A quick orientation; we will help you match it to your study.
| Dimension | 16S / ITS amplicon | Shotgun metagenomics | eDNA metabarcoding |
|---|---|---|---|
| Target | Marker gene (rRNA / ITS) | All DNA in the sample | Marker gene for target taxa |
| Resolution | Genus / species (marker) | Species / strain & genes | Species detection |
| Function | Inferred only (e.g. PICRUSt2) | Direct functional potential | No |
| Relative cost | Low | Higher | Low–moderate |
| Best suited to | Community structure surveys | Function & MAG recovery | Biodiversity & species detection |
What You Receive
A complete, documented deliverable
Not just a table of taxa dropped in a folder—an interpreted picture of your communities and how they differ, documented so it reproduces.
- ASV / OTU tables with taxonomy and quality summaries
- Alpha & beta diversity metrics with ordination plots
- Differential-abundance results between groups or conditions
- Functional / pathway profiles (shotgun), where applicable
- Metagenome-assembled genomes (MAGs) with QC, where applicable
- Publication-quality figures & clearly organised results
- Reproducible methods with every tool & database version
Built for reproducibility, and honest about limits
Environmental analysis follows documented best practices—primer and contamination controls, negative and blank handling, rarefaction or compositional-aware normalisation, and appropriate statistics for sparse community data—so a diversity difference or a taxon shift is real signal, not a sampling artifact. eDNA and metabarcoding report detectability, not a definitive census; abundances are relative, not absolute counts; and functional profiles describe potential, not confirmed activity.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and results reproduce a year from now. We will also tell you honestly when a design or sample size won't support the conclusion you're after.
FAQ
Environmental genomics questions
What researchers ask before starting an environmental project.
We are a computational (dry-lab) team. Send us your sequencing data (FASTQ, or 16S/ITS amplicon or shotgun reads) from your core facility or a public repository—we do not process physical samples.
They indicate which taxa are detectable in a sample, and relative signal—not a definitive census or absolute counts. We report detections with their limitations stated plainly.
16S/ITS amplicon profiling is cost-effective for community structure; shotgun metagenomics adds functional potential and finer resolution. We help you choose based on your question and budget.
Yes—functional and pathway profiling (e.g. from shotgun data) suggests metabolic potential. These are computational predictions of potential, which need experimental validation.
We support the computational side: functional metagenomics, degradation-pathway profiling, and community shifts over time. Conclusions about remediation performance rest on your study design and validation.
Related
Services that power environmental work
Environmental & microbiome analysis
Have environmental sequencing data?
Tell us your sample types, sequencing approach, and question—we'll scope the analysis and state the limits honestly.