From Data to Value in Hours
Our AI-powered analysis automates each step, rapidly delivering results as an interactive web application that can be explored and shared across all stakeholders.
- Identify Relevant Clinical Variables
Clinical information (e.g., age, sex, BMI, drug use) is used to identify the most relevant molecular changes. We compare AI-based predictions with supplied metadata to detect and correct labeling errors and select the minimal set of relevant clinical variables to preserve statistical power in subsequent steps. - Assess Quality
We perform quality control (QC) on each sample to identify library preparation problems and use a data-driven approach to detect samples that differ substantially from others in the experiment, ensuring more reliable downstream insights. - Normalize Across Samples and Call Peaks
To reveal true biological variation, we detect and reduce subtle technical noise that can arise during sample processing. We normalize DNA modification signals, filter out unreliable reads, adjust for known batch effects and call peaks when appropriate. - Find Differentially Modified Loci
Depending on your experimental design and research question, we select the most appropriate, statistically rigorous approach. We account for technical and biological variability, including cellular heterogeneity, and automatically fit an appropriate linear regression model to each gene. - Pinpoint Affected Pathways
We associate loci to genes, perform gene set enrichment analysis (GSEA) across all major pathway databases, clustering similar pathways to explore how groups of genes act together, even when individual genes are not statistically significant. - Build Diagnostic Classifiers
We identify the minimal set of biomarkers that best differentiate sample groups. By evaluating multiple machine learning algorithms and cross-validation strategies, we assess classifier accuracy for potential diagnostic, prognostic, or treatment applications.
Additional Value Out-of-the-Box
Epigenomic data contains a wealth of valuable information that often goes unexplored. To ensure you do not miss key insights, these analyses are included as part of our standard workflow.
- Differentially Methylated Windows
We perform data-driven detection of co-methylation regions and identify statistically differentially methylated regions (DMRs) using robust mixed effects linear regression models. These genomic locations contain epigenetic alterations that are the most relevant to a disease phenotype or environmental exposure. - Genomic Region Enrichment
We characterize canonical genomic regions, such as promoters, CpG islands, enhancers, chromatin accessibility regions that exhibit enrichment of differentially methylated loci or windows. This analysis allows us to infer potential gene expression changes resulting from epigenetic elements located at a distance from the regulated gene. - Ageing
We calculate carefully curated and validated epigenetic clock values by applying refined algorithms to DNA methylation data. This standardized measure of biological age acceleration provides a powerful tool for large-scale epidemiological studies, enabling the assessment of disease risk and lifespan prediction.
Get in touch to see how our intelligent analysis platform can redefine your multi-omics results.
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