Bioinformatics for omics data

From your measurements to biological interpretation


A list of changing genes, proteins or metabolites is only the beginning. The value of your omics data depends on whether the measurements can be trusted, how the study is structured, and what the molecular changes mean for your research question.

At Biogenity, we analyse transcriptomics, proteomics and metabolomics data as one connected workflow, and across layers when you have measured more than one. Every step is documented and checked against your study design, and we are open about what the data can and cannot support. You receive an accessible report with figures, tables and reusable outputs, so your team can choose the next experiment with confidence.

Jump to the part of the analysis that matters most to you:


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Statistics
quality control, normalization, batch assessment


Differential analysis
effect sizes, clustering, biomarkers


Interpretation
pathways, networks, multiomics integration

Prepare reliable measurements

Preprocessing and normalization

Meaningful comparisons start with a dataset suited to the analysis. We review measurement scales, missing values and feature coverage, then apply appropriate filtering, transformations and normalization. For RNA counts, protein intensities and metabolite measurements, those choices depend on how the data were generated and what has already been done. You gain a documented analytical starting point, with before-and-after summaries showing how processing affects sample comparability and the measurements retained for interpretation.



Quality control

Before interpreting molecular differences, you need to understand the quality of the measurements behind them. We examine sample signal, detected features, missingness, variability and agreement across the dataset, using checks appropriate to the assay. This helps identify samples or features that warrant closer investigation. You gain a clearer view of the strengths and limitations of your data, together with transparent evidence for quality-related decisions. A quality flag initiates review rather than automatically determining exclusion.

Outlier and batch effect assessment

An unexpected molecular profile can point to interesting biology, sample heterogeneity or a technical issue. We assess unusual samples using several diagnostics and examine their relationship with available metadata and batch information. This helps distinguish isolated sample concerns from systematic structure affecting a larger part of the experiment. You gain evidence to guide sample review and determine whether adjustment, stratification or further investigation is appropriate before drawing conclusions about your biological groups.



Batch correction

Studies measured across different runs, preparation batches or processing periods can contain unwanted technical variation. Where the experimental design allows batch and biology to be distinguished, we assess suitable correction and review its effects on the data. You gain more comparable profiles and before-and-after diagnostics for checking that technical structure has been reduced while specified biological patterns are retained. For some statistical analyses, including count-based RNA comparisons, accounting for batch in the model is the appropriate approach.


Discuss your data quality

Understand the structure of your data

Variance visualization

An overview of the dataset helps place individual findings in context. We use PCA and other suitable methods to summarize the strongest differences between samples, then compare those patterns with biological groups and technical metadata. You can see whether samples behave as expected, whether groups overlap, and which sources of variation need further attention. This provides an accessible starting point for interpreting a study and deciding which patterns deserve more detailed statistical or biological investigation.



Variance partition and mixed model analysis

Visual differences do not tell you how much variability is associated with each study factor. We model the contributions of treatment, subject differences, clinical variables and technical effects where the design supports them. Mixed models can also represent repeated measurements or hierarchical relationships. You gain a more quantitative understanding of what drives variation across genes, proteins or metabolites, helping you choose appropriate covariates and interpret the response of individual molecular features in the context of the wider study.

Annotation and ID mapping

Biological interpretation depends on knowing what the measured features represent. We connect supplied identifiers with suitable reference annotations, document mapping coverage, and retain ambiguous or unresolved matches. For genes and proteins, this supports traceable naming and pathway interpretation. For metabolites, we preserve the chemical identification level supported by the source information. You gain results that are easier to interpret and reuse, with visibility into which features can support downstream analyses and where further identity confirmation may be needed.



WGCNA module network analysis

When the dataset supports stable network analysis, WGCNA organizes correlated genes, proteins or metabolites into modules and relates those modules to study traits. Module summaries help reduce a complex dataset to patterns that are easier to investigate, while hub rankings highlight representative features. You gain a way to connect coordinated molecular behaviour with phenotype and prioritize candidates for follow-up. The strength of those conclusions depends on sample size, measurement quality and the consistency of the underlying correlations.


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Discover differences and response patterns

Differential analysis

Which molecular features differ between your study groups, and which changes are most relevant to your research question? We estimate the direction and magnitude of differences and assess their statistical support using methods appropriate to the data and experimental design. Results include effect sizes and adjusted p-values, allowing you to consider biological importance alongside uncertainty. You gain a ranked evidence base for prioritizing genes, proteins or metabolites, planning validation experiments and connecting molecular changes with the conditions being studied.



Clustering

Biological groups defined before an experiment may not capture all the molecular diversity in the data. Clustering explores similarities between samples or features to identify candidate subgroups and coordinated patterns. We examine the resulting clusters alongside stability measures and available metadata to help assess their relevance. You gain a way to investigate unexpected molecular subtypes, heterogeneous treatment responses or groups of features that behave similarly, providing new questions for targeted follow-up rather than relying only on predefined comparisons.

Longitudinal and paired design analysis

A single comparison can miss how a response develops. We analyse paired or repeated measurements to identify changes within subjects and assess whether those changes differ between groups. Depending on the design, this can reveal early responses, sustained changes or different trajectories over the study period. You gain a clearer understanding of response timing and within-subject variation, with statistical models that account for the relationship between repeated observations instead of treating every measurement as an independent biological replicate.



Biomarker and classification analysis

A focused molecular panel can be useful when the goal is to distinguish study groups or develop a candidate research signature. We rank features and assess panel performance and selection stability through internal validation, using a design suited to the available replication. You gain a shortlist of candidate genes, proteins or metabolites and evidence about how consistently they contribute to classification. The results help guide independent validation and assay development; discovery-stage performance does not establish clinical readiness.


Plan your analysis

Interpret the biological response

Enrichment and pathway analysis

Long lists of changing molecules become more useful when related to biological processes. We assess whether mapped genes, proteins or metabolites are associated with particular pathways or functional sets, taking measurement coverage and the relevant background into account. The results show which themes are supported and which molecules contribute to them. You gain an interpretable overview of the biology represented in your findings, helping prioritize mechanisms for discussion and follow-up without treating pathway association as direct evidence of causation or metabolic flux.



Feature set and signature scoring

Some research questions concern a coordinated programme rather than a single molecule. We summarize measured members of predefined feature sets into sample-level scores and document how well each set is represented. This allows you to compare pathway-related patterns across conditions, phenotypes or time points and examine variation between individual samples. You gain a more compact view of coordinated molecular behaviour. The meaning of each score remains tied to the measured layer: expression or abundance patterns provide indirect evidence about biological activity.

Mode of action

Understanding a response involves considering the direction of change as well as which pathways appear in a result list. We combine molecular changes with pathway context to describe coordinated patterns associated with your experimental condition. This helps compare how treatments affect the same biological processes and identify the molecules supporting those interpretations. You gain a concise, evidence-based account of the observed response and hypotheses for mechanistic experiments. The interpretation reflects the measured molecular layer and does not by itself prove a treatment mechanism.



Regulatory and upstream driver analysis

Observed gene or protein changes can provide clues about the regulators that may sit upstream of a biological response. Where suitable regulatory references and downstream measurements are available, we assess regulator–target relationships and directional consistency to identify plausible drivers. You gain ranked hypotheses with supporting target molecules, making it easier to select control points for experimental testing. This module is particularly relevant to transcriptomics and proteomics; metabolite measurements alone do not directly establish transcription-factor or protein-regulator activity.


Discuss your results

Connect molecular findings across omics

Network and correlation analysis

Molecules that vary together can reveal coordinated responses that individual comparisons overlook. We explore positive and negative correlations, identify network communities, and highlight connected features within the measured dataset. Where supported, we can also compare relationships between groups. You gain hypotheses about shared molecular programmes and candidates that may represent broader patterns. Correlation networks require interpretation alongside the study design, because co-variation can arise from biological, environmental or technical factors and does not establish direct interaction or causation.



Reactome interaction analysis

Pathway findings often connect through shared molecules and reference interactions. We use suitable identifiers and Reactome knowledge to explore those connections and highlight potential hubs linking affected processes. Transcript results can be interpreted through mapped protein references, while appropriate metabolite mappings support metabolic context. You gain a more connected view of your pathway findings and hypotheses about how processes may relate. These interactions come from reference knowledge; their inclusion does not mean that every interaction was measured in your experiment.

Multiomics integration

When several molecular layers are available for the same study, integration helps examine their relationships. We align suitable datasets and investigate shared sample patterns, contributions from each layer, and supported cross-layer associations. You gain a broader interpretation of the biological response and insight into where RNA, protein and metabolite evidence agrees or diverges. Those differences can help refine follow-up questions. Integration is selected to fit the available matching and study design, with associations interpreted at the level the data can support.



Reporting and scientific interpretation

Your results should be useful to the people making the next research decision. We assemble the selected analyses into a coherent report, connect interpretations to supporting figures and tables, and explain the methods and limitations that affect the conclusions. You receive an accessible overview together with detailed outputs for further review and reuse. This helps your team discuss candidate targets, choose validation experiments and communicate findings while preserving the path from each biological interpretation back to its underlying evidence.


Explore multiomics integration

Delivery and discussion

Delivery, post-consultation and discussion

At the conclusion of your bioinformatics analysis, we ensure a seamless transition from discovery to application. We deliver detailed reports and data, followed by a post-delivery consultation to discuss your study's insights and findings.


What to expect during post-consultation:

  • Discussion of the data
  • Explanation of the different steps in the data analysis
  • Q&A to the report(s)

Together, we ensure that you have the best prerequisite for your further research. The consultation can also be used to explore the next step, whether you are looking to expand the project with additional omics studies or validate the results.


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