Optimized OMICS Data integration ONTOFORCE


Omics data for optimized drug development: addressing opportunities and integration challenges

We're covering the opportunities omics data and multi-omics research opens up for life sciences companies and touching on some of the many challenges that organizations face when working with omics data.

27 February 2023 5 minutes

By providing a wealth of information about the biological processes underlying diseases and potential drug targets, omics data plays a crucial role in the life sciences industry. Across the drug development timeline, omics data and multi-omics research approaches allow biopharma organizations to make informed, data-driven decisions related to disease understanding.

While omics data is essential in discovering new connections and associations and enables many benefits across drug development, there are still many challenges prevalent when it comes to managing and working with omics data. In this article, we’ll cover the opportunities omics data and multi-omics research opens up for life sciences companies and we’ll also cover some of the many challenges that organizations face when working with omics data.

OMICS data opportunities for the life sciences industry


Omics data refers to large-scale data generated from various technologies, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics. These technologies provide insights into the molecular basis of biological processes and can be used to study the genetic and molecular underpinnings of diseases, ultimately contributing to the development of safer and more effective treatments.

The use of omics data in the biopharmaceutical industry has grown rapidly in recent years, driven by technological developments and reduction of associated costs, computational methods, and data analytics. Omics technologies enable the generation of large-scale datasets that provide a more comprehensive and detailed view of of the molecular and cellular processes in a biological system than traditional approaches. One factor driving the growing use of omics data is the increasing availability of public databases and platforms for data sharing and analysis. This has facilitated collaboration between researchers and companies, as well as enabling more efficient and cost-effective data analysis.

However, overall, the growing popularity of omics data for biopharmaceutical organizations reflects its potential to transform the way drug discovery and development is approached. By leveraging the power of large-scale data, organizations can accelerate the development of both more effective and more personalized treatments, leading to better patient outcomes and improved healthcare overall.


By leveraging the power of omics data, life sciences companies can significantly reduce the time and cost of drug discovery and increase the chances of success in bringing new and effective drugs to market.

Omics data has several specific applications within R&D that help accelerate timelines:

  • Identification of new drug targets: Omics data can help researchers identify new drug targets by providing a better understanding of the molecular mechanisms underlying diseases, and also identify specific mechanisms for certain patient populations. By analyzing large-scale genomic, transcriptomic, proteomic, and metabolomic datasets, researchers can identify novel biomarkers and pathways that can be targeted with drugs.
  • Predictive modeling: Omics data can be used to build predictive models that can be used to identify potential drug candidates and predict their efficacy and safety. By combining multiple omics datasets, researchers can develop more accurate and comprehensive models that can be used to better prioritize drug candidates and reduce the risk of drug failure.

Overall, omics data can help researchers to develop more effective and safer drugs, all while reducing the time and cost of research and development. By leveraging the power of large-scale data, the pace of innovation can be accelerated, transforming the way drug research and development is approached.

Addressing omics data challenges


Despite the benefits of omics data in drug research and development, there are several challenges associated with the use of this type of data. One major challenge is the sheer volume of data generated by omics technologies, which requires sophisticated computational and analytical tools for processing, storage, management, and analysis. On top of this large volume, the data is often complex and heterogeneous, requiring interdisciplinary expertise in biology, statistics, and computer science to properly interpret and analyze.

Another challenge is the lack of standardization and quality control across different omics platforms and data types, which can result in data inconsistencies and errors. Additionally, omics data can be subject to various biases and confounding factors, such as sample selection and environmental influences, which can complicate the interpretation of results.

For many who work with omics data, there is one thing in particular that continues to introduce challenges for research and analysis: integration. Combining and integrating data from multiple omics studies can be difficult due to differences in data types, formats, and platforms used. Specifically, each omics platform generates data in a different format, with different characteristics and biases, which makes it challenging to integrate and analyze data across multiple platforms. On top of this, there is also technical variability due to differences in experimental protocols, sample preparation, and instrument performance which can affect the quality and comparability of data generated across different omics platforms.


These integration challenges highlight the need for careful planning and data management strategies in order to effectively integrate data from multiple omics platforms for research and analysis purposes. There are a few approaches and process to consider that can help facilitate smoother integration:

  • Standardization and annotation of data: Numerous resources are still lacking proper meta-data annotations. Standardizing data formats and processing methods can make it easier to integrate data from multiple platforms. For example, standardizing metadata and using consistent normalization methods can reduce technical variability and improve data quality.
  • Quality control: Quality control is critical in omics data integration. Different omics platforms can generate data with different levels of accuracy and precision, and performing quality control checks can help identify and remove low-quality data that can bias downstream analysis.
  • Imputation of missing data: When integrating omics data, it is common to have missing data, which can affect the accuracy of downstream analysis. Imputation methods can be used to fill in missing data, although caution should be taken to ensure that imputed data is biologically plausible.
  • Statistical methods: Integrating omics data requires the development of appropriate statistical methods that can handle the high dimensionality and complexity of omics data. Machine learning algorithms, such as principal component analysis (PCA) and partial least squares regression (PLS), can be used to identify patterns in multi-omics data.
  • Network analysis: Network analysis can be used to identify interactions between different omics layers, which can provide insights into biological processes and pathways that are perturbed in disease. Integrative network analysis methods, such as consensus clustering and module preservation analysis, can be used to identify common patterns and interactions across multiple omics platforms.


“Integrating data from multiple omics studies to render it useable for exploration and analysis remains difficult for many organizations,” says Bérénice Wulbrecht, ONTOFORCE VP Solution Enablement. “As the use of multi-omics approaches in research continues to ramp up for many biopharma companies, ensuring efficient operations for managing multi-omics data will be vital.”

We discussed solutions for integrating omics data, along with other topics related to multi-omics research approaches during our recent webinar with ZS Associates, a management consulting and technology firm focused on transforming global healthcare. We detailed how you can optimize your omics data management and multi-omics research. Watch the recording >>>