Accelerate the progress of basic research

Maximize your opportunities to discover novel biomarkers and promising targets by using a comprehensive analysis environment. For Pharma & Biotech

Rapid biological data exploration

Advanced self-service analysis for your multiomics data products. Quickly identify and validate the effects your biomarkers and target candidates have on treatment outcomes.

Instant access to analysis

Fast track your understanding of your drug candidate’s mechanism of the action using a streamlined approach to statistical analysis.’s data products offer no-code, guided analysis apps that enable you to instantly ask and answer your biological questions. Below are some examples of how you can quickly understand and correlate genomic information (e.g. gene expression, mutation status, copy number):

Perform unsupervised clustering analysis on RNA-seq data to identify patient and sample segments for further downstream analysis cluster analysis

Create cohorts — using the point-and-click function — to analyze and compare profiles of responder versus non-responder samples cohort builder

Generate a cox survival analysis report, via R or Python scripts, to identify and validate your molecular candidates cox survival analysis

Run pathway analysis and access rich annotation information on your selected gene and protein variants pathway analysis

See analysis app demo data product - Overview data product - Data engineers data product - Data scientists data product - Researchers

Single Cell Gene Expression

In this demo, we’re going to show you how you can use a no-code analysis app to look for genes that are differentially expressed in cancer cells.

How Top Organizations Are Using

A Cell Therapy Biotech is deploying an array of proprietary & public-domain data products — enabling users to investigate & discover gene expression markers with respect to cell types.

An Oncology Biotech Company is combining OMICS data and manufacturing information to optimize cell yields.

Productizing your data

Your data analysis process can be dramatically streamlined when you productize your data. By turning your multiomics assays into analyzable data products, you gain fast access into crucial information that will promptly reveal potential drug targets and biomarkers. Furthermore, the data products will be readily available throughout your drug development lifecycle for quick references.

Curious what productizing your multiomics data looks like? Below are two examples.

TCGA Pan-Cancer Atlas And The Immune Landscape Of Cancer

This data product is a combination of data from two sources: TCGA Pan-Cancer Atlas and the Immune Landscape of Cancer. It has 10,967 samples from 33 cancer types. Examples of analysis apps available within this data product:

Gene Annotation

This data product deploys gene annotation information from NCBI. It contains a pathway analysis app that services other data products, such as the TCGA one above.

Types of data that can be turned into data products’s data product framework will integrate any combination of datasets into a simplified, flexible model – perfect for use within apps or as a dataframe to export to other tools.

Some examples of potential data sources, types, and formats:

Data type examples
  • DNA-Seq (VCF, MAF)
  • RNA-Seq (bulk and single-cell, spatial transcriptomics)
  • Proteomics
  • Flow cytometry
  • Compound screening
  • Immune repertoire
  • Clinical trials (outcomes and biomarkers)
  • Longitudinal studies
  • Annotation, ontology, pathways
  • Machine behavior and maintenance
  • Drug response & pKa studies
  • Biomanufacturing yields
  • Knockdown studies (RNAi, CRISPR)
  • Meta genomics
  • Gene expression
  • DNA methylation
  • Somatic mutations
  • Germline variants
  • High content screening
Data source examples
  • cBioPortal
  • TCGA
  • dbGaP
  • GEO & ArrayExpress
  • Clinical trials
  • UK Biobank
Data format examples
  • CSV
  • JSON
  • XML
  • SQL
  • Any form of tabular data (CSV/TSV)

Talk To Our Experts

If you don’t see the data types or sources you’re looking for in the lists, chances are we can accommodate them.

Talk to our experts to discover how to turn your omics data into data products!

Get A Demo

Harmonize your multiomics data using data mesh

Build a data mesh of biological assay data products to promote data harmonization and maximize your chances of discovering novel biomarkers and promising targets.

Seamlessly integrate both historical and emerging data, such as genomic and mouse models, to inform and enhance your biomarker and target candidates

Instantly compare your proprietary data against public datasets so that you can see how your drug candidates compare to hundreds of other drugs

Quickly identify and evaluate how your therapeutic candidates’ activities correlate with different drugs across multiple therapeutic areas

Data mesh in a box offers an out-of-the-box solution to help you accelerate your data mesh implementation. Talk to our experts to learn more about our offerings!

Promote cross-organizational collaboration

Access your data mesh of multiomics data products and collaborate efficiently using’s two-sided analysis environment. - Analysis Platform and Developer Studio

Analysis Platform offers a customizable Analysis Platform to support your unique business and research needs. Leverage the enterprise features to drive collaboration and innovation.

For researchers

  • Ask and answer your biological questions with confidence – by using self-guided, no-code analysis apps within each data products to perform advanced analytics, such as clustering analysis and generating R reports
  • Promptly identify, store, and share your useful data artifacts (UDATs), such as biomarker and target candidates, with your fellow researchers and collaborators

For data scientists and bioinformaticians

  • Demonstrate the value of your data science in basic research – by making your work accessible to researchers and other domain experts across the organization
  • Save hours from performing one-off analyses – by repurposing your work as reusable analysis apps across multiple data products

For leadership

  • Promote a consistent view of reliable, harmonized multiomics data – by providing a platform that presents a single source of truth
  • Boost efficiency and drive innovation – by streamlining the data analysis process and making data science accessible across your organization

For strategic IT

  • Simplify your data governance process as your experimental data grows – by streamlining your data management and access
  • Stay compliant with regulatory and legal requirements without compromising data use

Developer Studio

To help you build high quality data products, offers a Developer Studio that uses a familiar, Jupyter notebook-based setting. Leverage the pre-built templates to streamline your data product creation.

For data scientists

  • Access reliable and harmonized multiomics data from data products for your R & Python algorithms and ML/AI models
  • Collaborate more efficiently when your fellow data scientists and data engineers use the same tool to access data and build data products

For data engineers

  • Deliver quality and harmonized multiomics data to data scientists and promote data reusability – by standardizing how nomenclatures get mapped into the data products
  • Advance data literacy when you use terminologies that researchers understand – by partnering with domain experts to standardize the terminologies being mapped into the data products

For quality controlled analysts

  • Rapidly reproduce any reports and analyses to support regulatory submissions and publications – with instant access to all the versioned data, software packages, and analysis methods
  • Streamline ​​the auditing procedures when you gain transparency into the data mapping process and source code

For leadership

  • Set a lasting foundation that promotes consistent quality controlled outputs as your experimental data grows and new data types emerge
  • Significantly free up your resources time so that they can focus on what they do best – by streamlining and automating manual repetitive work

More ways to use helps you overcome data challenges across your drug development process. For Preclinical

To evaluate potential success of a treatment, using data from cell lines and model organisms. For Clinical Trials
Clinical Trials

To stratify heterogeneous patient populations and develop diagnostics for stratification, toxicity, and response to treatment. For Regulatory Review
Regulatory Review

To produce quality controlled reports for reviews. For RWE/RWD
Real World evidence

To connect clinical endpoint biomarkers with real world data. Data Security In Clinical Research Environment

Data Security in Clinical Research Environment is hosted entirely within your secure network and/or your secure cloud. Source data storage and access is tightly controlled within your network, and the platform is compliant with standards such as GxP, GDPR and HIPAA.

Learn More About Security

Explore Resources For Life Sciences for clinical data management and analysis ebook

Data Harmonization for Translational Research

A practical guide to help you resolve clinical trial data challenges using the data mesh approach.

Promote transparency in the data analysis process

Promote Transparency In The Data Analysis Process

Gain visibility into peer-reviewed work. Demonstrate integration of python-based machine learning algorithm into the platform.

Iterative Analysis Allows Faster Discoveries

In the course of one evening in 2019, Dr. Radovich found three significant results from his own dataset in Thymoma (TCGA).
Maximize your chances of clinical success

From a 30-minute demo to an inquiry about our data mesh in a box offering, we are here to answer all of your questions!