Analyze most common Transcriptomics, Metabolomics, Proteomics data formats and integrate the results into one research project

Technological advancements in biomedical sample analysis have revolutionized our ability to decode the complexities of biological systems. High-throughput sequencing, mass spectrometry, and imaging techniques now provide unprecedented levels of molecular detail. However, these advancements have also led to a proliferation of multiple data formats across omics disciplines, presenting significant challenges in data integration and interpretation. The power of cloud computing, no-code data analysis and easy meta-data standardization are pivotal in overcoming these challenges. By leveraging the UniApp, you can harmonize multi-omics data, enabling cross-platform analysis and holistic understanding to reveal complex biological interactions at unprecedented scales.

Key highlights

Transcriptomics, metabolomics, proteomics

Multi-omics analysis refers to the integrative analysis of multiple types of “omics” data to gain a comprehensive understanding of biological systems. “Omics” refers to the collective technologies used to explore the roles, relationships, and actions of the various types of molecules that make up the cells of an organism. The UniApp platform provides extensive analysis tools for transcriptomics, proteomics and metabolomics. By combining these data types, you can achieve a more holistic view of the biological processes and mechanisms at play.


State of the art transcriptomics toolkit, aimed a integratable results


1. RNA Sequencing (RNA-Seq): Quantifying gene expression by sequencing RNA transcripts.
2. Microarray Analysis: Measuring gene expression levels using microarray chips.
3. Single-Cell RNA Sequencing (scRNA-Seq): Analyzing gene expression at the single-cell level to understand cellular heterogeneity.
4. Transcriptome Assembly: Reconstructing transcript sequences from RNA-Seq data.
5. Differential Gene Expression Analysis: Identifying genes with significantly different expression levels between conditions or groups.

Overcome most data integration challenges: the meta-analysis toolkit

One of the questions encountered in multi-omics or large data studies pertains to data integration. While data integration is preferable in specific cases, we strongly believe in meta-analyses, which focus on aggregating and analyzing summary statistics or effect sizes derived from individual studies. The key benefits of meta-analysis over data integration are summarized as follows:

  • Aggregated Evidence: Meta-analysis synthesizes results from multiple studies, increasing statistical power to derive robust conclusions about treatment effects, risk factors, or biomarkers.
  • Reduced Bias: By pooling data from multiple studies, meta-analysis can mitigate biases inherent in individual studies, such as publication bias and selective reporting of outcomes.
  • Generalizability: Meta-analysis evaluates consistency and variability across studies, providing a broader perspective on the generalizability of research findings across different populations, settings, or methodologies.
  • Efficiency: Conducting a meta-analysis can be more time-efficient and cost-effective compared to collecting and integrating raw data from multiple sources.

To efficiently conduct meta-analysis, two factors are critical: the ability to analyze individual datasets and integrate these results quickly, and specific sets of analysis and visualization options to derive meaningful insights from your meta-analysis. Learn more about UniApp’s specific features that support large meta-analysis studies in our client success stories or use case section.

Related modules

Cloud compute power

Leverage the power of cloud computing to drastically reduce analysis runtimes without specific hardware and tap into the potential of performing large data studies

Data visualizations

Explore your data with interactive and easy customizable data output visualizations without any coding or scripting

Project management tools

Build your research paper storyline with interactive figure panels readily to present while keeping track of data files, versions and tasks

Data organization

Gain control over your valuable data files in a single secured environment, allowing you to easily share data and collaborate

Text Size Adjustment

Proud to accelerate biomedical research at our clients

Styled Divider
Image Row
Amsterdam UMC Universität Göttingen Universität Innsbruck VIB UZ Leuven KU Leuven Medizinische fakultät Heidelberg Inserm


Great and fruitful collaboration with top specialists from diverse complementary disciplines.

Prof. Dr. Kristl Claeys, University Hospitals Leuven, Belgium

In your professional career, you need to be attentive to change and chance. When you see it coming, you should not hesitate, but act! Unicle is the change and provided us the chance to tremendously uplift and accelerate our project.

Prof. Dr. Andreas Pircher, Medical University Innsbruck, Austria

Meticulous, committed and responsive professionals. My experience with the quality of their work has been highly satisfactory. They pay attention to details from the beginning to the end of the analysis.

Prof. Dr. Anna Rita Cantelmo, Inserm Institute Pasteur of Lille, France

Finally a tool for biologists to apply bioinformatics without scripting, allowing us to focus on thinking

Prof. Dr. Max Mazzone VIB / KU Leuven, Belgium

Want to know more?

Curious to learn more about our services and how we can support your research needs? Explore our resources section or contact us directly. Whether you're looking for specific information or general guidance, we're here to help you every step of the way. Contact us today to discover the full potential of our solutions.