oloMAP | oloBion

about

About oloMAP

oloMAP is our bioinformatics platform for AI-driven high-throughput metabolomics, lipidomics and proteomics data processing, and high-confidence feature annotation and identification (metabolites, lipids and proteins). It also generates advanced biostatistical reports, turning raw omics data into actionable insights, and enabling our clients to focus on biological interpretation and next steps.

By applying cutting-edge approaches such as compound and protein identification, chemical enrichment analysis, and pathway analysis, oloMAP delivers an in-depth view of the molecular composition of your samples alongside a high-level perspective on the biological mechanisms at play. Identification is powered by our database of over 1 million biomolecules.

From oloMAP comes oloMAP Portal, our web-based platform for omics data analysis and interpretation, accessible from any internet-connected device. It integrates statistical analysis with data visualization and interpretation, enabling in-depth biological exploration of omics data.

oloMAP Portal offers cutting-edge methods for single-omics and multi-omics data analysis, combining advanced statistics and multi-omics integration. Single-omics data analysis provides valuable insights into data structure and the main sources of variation, helping to identify key features and molecular mechanisms driving your phenotype. Multi-omics data analysis provides a more holistic view of biological processes, enabling the discovery of potential biomarkers and the identification of coherent patterns shared across datasets. Our Multi-omics Integration tool brings together multiple omics layers and also supports the integration of omics data with non-omics data.

oloMAP is a self-improving workflow that continuously updates its chemical and spectral libraries, as well as its statistical and visualization methods. This ensures that today’s discoveries fuel tomorrow’s advancements, enabling us to stay ahead in supporting innovative research.

Why olomap

oloMAP makes your life easier by helping you find new trends and discoveries in your omics data

Save time

Handling Complex and Large Volumes of Data: Metabolomics, lipidomics and proteomics data generate enormous amounts of information.

Indentifying key biomolecules: Omics studies require identifying potential biomarkers among thousands of variables.

Integrating your data: Overcome the challenges of correlating your metadata and clinical data with omics results.

Enhance reliability

Advanced biostatistics: Interpreting omics data involves multiple comparisons and statistical tools.

Technological Advances: Mass spectrometry technology is constantly advancing, requiring the incorporation of the latest standards and developments in the field to remain at the forefront.

Gain Flexibility and Control

Unified and Secured Access: Tailored permissions ensure that only authorized personnel access specific datasets, enhancing security and collaboration.

Interactive Visualization: Immediate feedback and dynamic updates enable quick adjustments and deeper analysis during data exploration.

Extensive customization: Allows personalization of interface and functionalities to enhance user experience.

workflow

OMICS Data Processing

oloMAP annotates the molecules identified through our mass spectrometry instruments and combines them with your study design as well as external data to perform statistical analysis. These results are delivered through oloMAP Portal, our interactive platform for omics data visualization and interpretation, offering a seamless and insightful experience to uncover hidden patterns and extract meaningful biological insights. If you already have omics data and are unsure how to handle or interpret it, don’t hesitate to contact us. Our team is here to help you turn complex data into clear, actionable knowledge.

SERVICES

Our Bioinformatics Services

At oloBion, we offer cutting-edge bioinformatic services to support researchers and industries, including Pharmaceutical & Biotechnology, Biomedicine & Health Research, Agriculture & Food Science, Cosmetics & Dermatology, Nutraceutical & Nutrition, Veterinary & Animal Science and Environmental & Ecology. With our state-of-the-art technology and expertise of our team, we help you handle data processing, enabling you to discover new trends and insights in your metabolomics, lipidomics and proteomics data while supporting your research objectives.

We offer both single-omics and multi-omics analyses on oloMAP Portal, our interactive platform, as well as Custom Analysis tailored to your specific needs. Single-omics analyses apply advanced statistics to identify key features and enriched pathways, while multi-omics analyses use data integration methods to provide a holistic view of biological processes.

N-omics Integration

N-omics integration explores the relationship between two or more omics datasets using DIABLO, a Multiblock sparse Partial Least Squares Discriminant Analysis (sPLS-DA) framework implemented in the mixOmics R package. DIABLO aims to identify coherent patterns shared across datasets that change according to different phenotypes, enabling the discovery of robust biomarkers and improving the understanding of the molecular mechanisms underlying the studied condition (e.g. disease).

Two-omics Integration

Two-omics Integration explores the relationship between two omics datasets using the sparse Partial Least Squares (sPLS) method implemented in the mixOmics R package. Pairwise analysis of the data identifies sets of correlated features across the two datasets, providing insight into their shared structure and major sources of variation.

Machine Learning

Machine Learning (ML) is a branch of artificial intelligence that focuses on developing models and algorithms that can learn from data and generalize to unseen data, and thus, perform tasks without explicit instructions. One of the core types is supervised learning, which is a technique where a model learns from labelled data to make predictions on new data. The most common algorithms are classification and regression.

Fatty Acid Analysis

Fatty acid analysis is based on determining the number of carbons and double bonds in the fatty acids of a lipid. This composition can provide valuable insights into the biological roles of different fats in health and disease.

Data Distribution

Data distribution is represented using box plots for continuous variables and bar plots for categorical variables. A box plot, also known as a box-and-whisker plot, is a graphical representation that summarizes a set of data. It displays the distribution of a dataset along with its central tendency and variability. It provides a visual summary of the key characteristics of the data, including the median, quartiles, and potential outliers. A bar plot is a graphical representation used to display categorical data, where each category is represented by a rectangular bar, and the length of the bar corresponds to the frequency or value of the category it represents. It is commonly used to compare different discrete categories.

Functional Enrichment

Functional enrichment analysis is a method used to identify which biological functions, pathways, or processes are over-represented in a set of genes or proteins. This analysis is essential for interpreting high-throughput data, such as those from genomics, transcriptomics, or proteomics studies. In proteomics, this analysis helps in understanding the biological significance of the identified proteins by highlighting specific functions or pathways that are more prevalent than would be expected by chance.

Advanced Statistics

Advanced statistics refers to the application of sophisticated statistical techniques to analyze data, extract insights, and draw meaningful conclusions. N-way ANOVA and Repeated Measures ANOVA are included in these wide range of techniques.

Box Plot

A box plot, also known as a box-and-whisker plot, is a graphical representation that summarizes a set of data. It displays the distribution of a dataset along with its central tendency and variability. It provides a visual summary of the key characteristics of the data, including the median, quartiles, and potential outliers.

Identified Analytes

Identified Analytes provides a comprehensive overview of all analyte classes detected in your study, categorizing compounds by chemical class and superclass. The report features a detailed table of class counts alongside clear distribution charts to visualize relative abundances, enabling deeper insight and interpretation of your omics dataset.

Chemical Enrichment

Chemical enrichment analysis is used to assess significant alterations in chemical classes between two experimental groups. This technique plays a crucial role in understanding patterns of expression in various biological conditions or disease states across different fields of biological research, including lipidomics and metabolomics.

Ridgeline Plot

The Ridgeline plot displays the distribution of log2(FC) for metabolite classes when comparing between two groups.

Hierarchical Clustering

Hierarchical clustering is a method of cluster analysis that creates a hierarchical representation of the clusters in a dataset. The method starts by treating each data point as a separate cluster and then iteratively combines the closest clusters until a stopping criterion is reached. A dendrogram is a tree-structured graph used to illustrate the hierarchical relationships among the clusters. The result of a clustering is presented either as the distance or the similarity between the clustered rows or columns depending on the selected distance measure.

Volcano Plot

The Volcano plot is a graphical method typically used to visualize the significance and magnitude of differential expression or other statistical measures between two experimental conditions. It integrates a measure of statistical significance from a statistical test such as Welch’s t-test (the p-value) with the magnitude of the change in expression or abundance between the conditions (the fold change).

PLS-DA

Partial Least Squares Discriminant Analysis (PLS-DA) is a supervised method for classification and regression that combines dimensionality reduction with discriminant analysis. It is used to model high-dimensional data by constructing latent variables (LV) as linear combinations of the original predictor variables. These latent variables explain the relationship with the response variable and aim to find a lower-dimensional representation of the predictors that best discriminate between classes or predict categorical outcomes.

PCA

Principal Component Analysis (PCA) is a dimensionality reduction method used for exploratory data analysis, visualization and data processing. It involves transforming a set of correlated variables into a smaller set of uncorrelated variables, known as principal components (PCs). These principal components are ordered by their ability to explain the variability in the data, with the first component accounting for the highest amount of variance. PCA can be used to simplify complex data sets, identify patterns and relationships among variables, and remove noise or redundancy from data.

Pathway Analysis

Pathway analysis is a method used to identify which biological pathways are significantly enriched given a set of analytes (metabolites, proteins or genes). It is essential for understanding the biological significance of changes in analyte levels, providing insights into the metabolic alterations associated with diseases, treatments, or physiological conditions.

Correlation

Correlation is a statistical measure that expresses the degree to which a pair of continuous variables are linearly related. It quantifies how changes in one variable are associated with changes in another variable. The most common correlation measures are the Pearson’s correlation coefficient, which is the covariance of two variables divided by the product of their standard deviations, and the Spearman’s correlation coefficient, which measures the monotonic relationship between two variables using ranked values.

Custom Bioinformatics

Bespoke bioinformatics pipelines designed to meet customer requirements. Deliverables include interactive visualizations, detailed reports and reproducible code—all optimized for customer dataset and research goals.

Enhance your analysis with:

Biological Interpretation Report

Our comprehensive biological interpretation connects your metabolomics, lipidomics and proteomics data with the underlying biology, highlighting relevant mechanisms and potential biomarkers to support your next decisions.

After molecular identification, data preprocessing and statistical analysis in oloMAP Portal, our scientific experts review your results to identify key molecules and build a clear, structured narrative around the main findings. These molecules are linked to biological pathways, mechanisms and phenotypes, always tailored to your study objectives. In addition, relationships between different biological layers (e.g. metabolomics, proteomics, lipidomics) and their individual contribution to the study context are revealed using our proprietary Multi-omics Integration tool.

What will you find in your report?

• Detailed biological interpretation of key features, pathways and mechanisms, including their known roles in physiology, disease or mechanism of action.

• Integration of multiple omics layers to gain a holistic view of biological processes and identify shared patterns that change across different phenotypes.

• An executive summary of the main biological findings considering the context of your study.

• Actionable recommendations, including hypotheses to test, potential biomarkers to follow up, and suggested next experiments.

The entire narrative is accompanied by high-quality figures, either exported from oloMAP Portal or created specifically for the report. The result is a comprehensive interpretation that translates complex omics outputs into biologically meaningful insights, tailored to your specific project and questions.

How we work

From Samples to Insights

Contact and

Proposal

Send Samples
to the Lab

Sample Reception

and Processing

Mass Spectrometry

Analysis

Gain Insight into

oloMAP Portal

Standard Delivery Time:
6 weeks
Fast-track option available

Minimum Sample Amount:
Consult our experts for guidance
on sample requirements

Discount:
Associated with the number of
samples and multi-omics analysis

contact

Get in Touch!

Partner with us to explore the power of nutritional analysis in your field. Our team is ready to guide you through the process and deliver insights tailored to your research or industry needs.

Contact us today to discuss your project or schedule a consultation. Together, we’ll advance innovation through bioinformatics!