Specialist analysis for complex sequencing and omics research.
BiomeLens combines bioinformatics, study-aware statistics and biological interpretation to answer specific research questions. Each project is customised to the hypothesis, experimental design, metadata structure, dataset characteristics and evidence required, rather than simply applying a standard pipeline.
Explore the capabilities below. Core and optional analyses are agreed for each study; multi-factor models and multi-omics integration are included where the data support them.
Start with the question
Plan Your Microbiome Study
Connect the research question, sampling design, metadata and downstream analysis before generating data.
Data, analysis & deliverables+
Data we analyse
Research aims, proposed sample groups, available metadata and planned sequencing outputs.
Core analysis
Research-question refinement, sampling and replication considerations, metadata planning, sequencing-strategy considerations and a downstream analysis plan.
Advanced options
Confounder assessment, longitudinal or multi-factor designs, power considerations where assumptions are defensible, and planning for integrated datasets.
Interpretation
We identify what the proposed design can reasonably answer, which comparisons need support and where limitations should be addressed before data generation.
Typical deliverables
A scoped analysis strategy, metadata checklist, proposed comparisons and documented assumptions.
Research applications
New microbiome studies, pilot projects, grant planning and studies preparing for external sequencing.
Resolve microbial community patterns through design-aware comparisons, ecological context and clearly documented analysis.
Data, analysis & deliverables+
Data we analyse
16S or 18S rRNA gene and ITS amplicon reads, sample metadata and suitable existing feature tables.
Core analysis
Read-quality assessment, denoising and ASVs, taxonomy, community composition, alpha and beta diversity, ordination and contamination assessment where controls support it.
Advanced options
Design-aware PERMANOVA and differential abundance, core or indicator taxa, metadata associations, longitudinal comparisons and cautiously interpreted co-occurrence networks.
Interpretation
We assess community patterns alongside replication, dispersion, confounding and ecological context. Optional marker-gene functional predictions remain predictions; they are not equivalent to shotgun-derived functional profiles.
Typical deliverables
Quality summaries, annotated feature and taxonomy tables, statistical comparisons, clearly labelled figures and documented methods.
Research applications
Soil and plant microbiomes, environmental monitoring and health-associated microbiome research.
Move from microbial genome reconstruction to strain relationships, functional repertoires and comparative biological questions.
Data, analysis & deliverables+
Data we analyse
Microbial isolate sequencing reads, genome assemblies and associated source or phenotype metadata.
Core analysis
Read filtering, de novo assembly, quality and completeness/contamination assessment where relevant, annotation, species confirmation, average nucleotide identity and functional screening.
Advanced options
Core and pangenome analysis, SNP comparisons, phylogenomics, population comparisons, AMR and virulence-associated genes, plasmids, mobile elements, genomic islands and candidate horizontal transfer where supported.
Interpretation
Genome differences are assessed against phenotype, geography, source or ecology when the design permits. Gene detection alone does not establish resistance phenotype, pathogenicity or transfer events.
Typical deliverables
Genome quality reports, assemblies and annotations, gene presence/absence and genome-distance summaries, phylogenies and comparative figures as scoped.
Research applications
Microbial strain characterisation, comparative genomics, environmental isolates and research surveillance.
Examine community composition and functional potential, with genome-resolved and specialised analyses matched to your study.
Data, analysis & deliverables+
Data we analyse
Shotgun metagenomic reads, assemblies and sample metadata from microbial communities.
Core analysis
Read QC, host/background filtering where appropriate, taxonomic profiling, species or strain resolution where supported, gene-family annotation and functional/pathway comparisons.
Advanced options
Assembly and contig annotation, metagenome-assembled genomes (MAGs), genome-quality assessment, comparative MAG analysis, pathway reconstruction, CAZymes, AMR and virulence-associated gene screening.
Interpretation
Community structure and metabolic potential are linked to study metadata and appropriate statistics. Gene presence does not demonstrate expression, activity, pathogenicity or process rates.
Typical deliverables
Quality reports, taxonomic and functional tables, scoped assemblies/MAGs, comparative statistics, figures and a documented result package.
Research applications
Complex environmental and host-associated communities, biogeochemistry, resistomes and bioprospecting.
Investigate conserved and accessory genes, genomic relationships and functional differences across a defined genome set.
Data, analysis & deliverables+
Data we analyse
A defined set of microbial isolate genomes or suitable MAGs, with contextual metadata.
Core analysis
Harmonised genome QC and annotation, ortholog identification, core/accessory gene analysis, gene presence/absence and genome similarity.
Advanced options
Phylogenomics, SNP and population-structure comparisons, functional and metabolic repertoire comparisons, genomic-trait associations and candidate adaptation signals.
Interpretation
We examine conserved and variable features while accounting for genome quality, sampling and relatedness. Associations are evaluated as hypotheses, not proof of adaptation or causality.
Typical deliverables
Pangenome and ortholog tables, similarity summaries, comparative trees, functional comparisons and publication-oriented figures.
Research applications
Strain diversity, evolutionary questions, comparative metabolism and microbial trait research.
Use long reads and complementary short-read data to investigate genome continuity, plasmids and structural context.
Data, analysis & deliverables+
Data we analyse
Oxford Nanopore or other supported long-read data, optionally paired with short reads for hybrid analysis.
Core analysis
Read-quality, length and yield assessment, long-read or hybrid assembly, polishing and assembly-quality evaluation.
Advanced options
Circular reconstruction, plasmid resolution and structural comparisons where data support them, followed by genome annotation and selected comparative analyses.
Interpretation
We assess assembly continuity and uncertainty against coverage, repeat structure and read quality. Complete or circular genomes cannot be guaranteed for every dataset.
Typical deliverables
Read and assembly summaries, polished assemblies, supported structural findings and annotation outputs as agreed.
Research applications
Microbial genome reconstruction, plasmid context and genomes that are difficult to resolve with short reads alone.
Explore how microbial communities express genes under sampled conditions and how expression relates to functional potential.
Data, analysis & deliverables+
Data we analyse
Community RNA sequencing reads, sample metadata and reference genomes or paired metagenomes where available.
Core analysis
QC, host and rRNA filtering where appropriate, reference mapping or assembly, quantification, functional annotation and design-aware differential expression.
Advanced options
Pathway-level comparisons, organism-linked expression where resolution allows, longitudinal analysis and integration with paired metagenomic data.
Interpretation
Metagenomics describes functional potential; metatranscriptomics measures transcripts under the sampled conditions. Transcript patterns are not direct measurements of metabolic flux or ecosystem process rates.
Typical deliverables
QC and expression tables, functional/pathway summaries, differential analysis, figures and clearly documented methods.
Research applications
Microbial responses to environmental change, treatment comparisons and community-level expression research.
Connect expression differences with experimental design, biological pathways and the research question.
Data, analysis & deliverables+
Data we analyse
Bulk RNA-seq reads or suitable gene/transcript count matrices with experimental design and metadata.
Core analysis
QC, alignment or quantification, design-aware differential expression, functional annotation and enrichment analysis.
Advanced options
Multi-factor or repeated-measure comparisons where supported, expression clustering, co-expression analysis and integration with phenotype or environmental metadata.
Interpretation
Expression patterns are evaluated against replication, contrasts, batch effects and annotation coverage. Enrichment identifies supported patterns for further investigation.
Typical deliverables
Expression and differential-result tables, PCA, heatmaps, volcano plots, enrichment summaries and reproducible analysis documentation.
Research applications
Research on treatment responses, biological processes and condition-dependent expression; not clinical diagnosis.
Analyse suitable quantitative proteomics data with appropriate statistics, functional context and cross-omics interpretation.
Data, analysis & deliverables+
Data we analyse
Suitable protein-level quantitative matrices, sample metadata and upstream identification/quantification documentation.
Core analysis
Input and group-quality review, missingness assessment, justified normalisation, differential protein abundance and multivariate analysis.
Advanced options
Clustering, functional annotation, pathway enrichment and integration with transcriptomic, genomic or other compatible omics datasets.
Interpretation
Methods depend on the mass-spectrometry platform, quantification approach, missingness and study design. Protein abundance patterns are interpreted with these limitations.
Typical deliverables
Quality and missingness summaries, statistical tables, annotated comparisons and publication-oriented figures.
Research applications
Comparative protein-abundance research and multi-omics interpretation. We do not provide laboratory mass spectrometry; raw-file identification requirements need separate scoping.
Bring compatible datasets together to investigate relationships between genes, communities, expression and study metadata.
Data, analysis & deliverables+
Data we analyse
Compatible genomic, metagenomic, transcriptomic, metatranscriptomic or proteomic datasets, with environmental, agricultural or research cohort metadata.
Core analysis
Sample and identifier harmonisation, quality and design review, cross-layer annotation and a study-specific integration strategy.
Advanced options
Matched-sample associations, multi-layer pathway comparisons, integrative multivariate analysis and feature prioritisation where the dataset supports them.
Interpretation
We distinguish agreement and differences across biological layers while accounting for sample matching, scale, batch effects and confounding. Cross-layer associations do not establish causality.
Typical deliverables
Harmonised analysis tables, integrated figures, documented assumptions and a biological interpretation tied to the study aims.
Research applications
Studies linking community composition, functional potential, expression and measured environmental or host-associated variables.
Relate microbial communities to environmental variables through study-specific statistics, ecological comparisons and biologically interpretable models.
Data, analysis & deliverables+
Data we analyse
Microbial community, genomic, omics or ecological datasets with a clear study design and relevant metadata.
Core analysis
Hypothesis and design review, distribution and assumption checks, diversity analysis, multivariate analysis, appropriate hypothesis testing and effect-size interpretation.
Advanced options
Design-aware PERMANOVA; constrained ordination (CCA, RDA or dbRDA as appropriate); environmental association analysis; variation partitioning where justified; regression, mixed or repeated-measure models; and temporal/spatial ecological comparisons.
Interpretation
We evaluate effect sizes, uncertainty, multiple testing and model assumptions alongside statistical significance. Methods are selected for replication, dependence and the scientific question.
Typical deliverables
Analysis plans, model and comparison summaries, uncertainty estimates, reproducible code where agreed and interpretable statistical figures.
Research applications
Community–environment relationships, ecological monitoring, treatment comparisons and metadata-driven research.
Develop figures from statistically supported analytical results to communicate biological findings clearly, with appropriate uncertainty, context and publication-ready presentation.
Data, analysis & deliverables+
Data we analyse
Analysis outputs, genomic and microbiome tables, statistical results and existing figures that need scientific refinement.
Core analysis
Review the analytical evidence and biological finding, then select appropriate visual comparisons, scales, statistical annotations, readable labels and accessible colour choices.
Advanced options
Multi-panel layouts, ordination, heatmaps, community composition, volcano plots, genomic comparisons, phylogenies and pathway or network figures.
Interpretation
Figures are developed around statistically supported findings and their biological context. We assess the underlying comparisons and uncertainty so that visual emphasis reflects the strength of the evidence.
Typical deliverables
High-resolution and vector/editable outputs where suitable, journal-oriented dimensions, figure legends and reproducible plotting workflows as scoped.
Research applications
Research manuscripts, theses, presentations, project reports and communication of complex biological findings.
Extend suitable genome-resolved projects with MAG-based functional annotation, CAZyme analysis, functional gene mining and metabolic pathway interpretation. We use genomic context and comparative evidence to prioritise candidate functions and enzymes.
This work is computational. Predicted activity requires experimental validation by appropriate external partners; wet-lab enzyme validation is outside our services.
A defined scientific scope, input-quality checks, statistically appropriate comparisons, documented analytical decisions and a review discussion. For complex or incomplete datasets, we assess what remains answerable and explain the limitations.
Deliverables can include reproducible workflows, interpretable result tables, publication-oriented figures and methods documentation, as agreed for your project. See the project FAQs for practical details.
A quotation specific to your project
Scope and cost depend on the dataset, research question, project complexity, analytical requirements and deliverables.
BiomeLens provides bioinformatics and data analysis services. Laboratory sequencing, DNA/RNA extraction and wet-lab sample processing are not part of our services.
