Enhance in vitro studies and de-risk in vivo studies using readily analyzable preclinical data products.
Rapid exploratory and confirmatory analysis
Advanced self-service analysis for your preclinical data products. Identify clinical success as early as possible to improve future clinical trials and avoid late-stage failures.
Instant access to analysis
Promptly link in silico hypotheses and in vitro results to in vivo settings using a streamlined approach to statistical analysis. Tag.bio’s data products offer self-guided, no-code analysis apps that enable you to instantly ask and answer your research questions. Below are some examples of how you can translate your in vitro findings into in vivo models:
Perform unsupervised clustering analysis on flow cytometry data to identify cell-type subpopulations and detect anomalies
Create cohorts — using the point-and-click function — to compare samples from various immunocompromised mouse strains with xenografts from human donors
Generate a cox survival analysis report, via R or Python scripts, to identify genes critical to the survival of your selected cell lines
Run pathway analysis and access rich annotation information on your selected gene and protein variants
See analysis app demo
How Top Organizations Are Using Tag.bio
A Top 30 Nonprofit Biomedical Research Institution is analyzing 1000s of flow cytometry samples from various immunocompromised mouse strains with xenografts from human donors.
A Top 40 Nonprofit Institute is turning 3 data sources into data products: longitudinal, genetic and app-generated data.
Productizing your data
Your data analysis process can be dramatically streamlined when you productize your data. By turning your preclinical data into analyzable data products, you gain fast access into crucial information that will move your drug candidates to clinical development faster. Furthermore, the data products will be readily available throughout your drug development lifecycle for quick references.
Curious what productizing your preclinical 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
Tag.bio’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
- SPARK
- 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 preclinical data into data products!
Harmonize your preclinical data using data mesh
Build a data mesh of in silico, in vitro, and in vivo data products to promote data harmonization and improve the success of future clinical trials.
Seamlessly integrate both historical and emerging data, such as flow cytometry samples and DNA/RNA sequencing, to inform and enhance your lead candidates
Instantly compare your proprietary data against public datasets so that you can identify scientific trends and correlations across multiple studies
Quickly perform exploratory and confirmatory research to reduce the toxicity effects and improve the efficacy of your preclinical studies
Data mesh in a box
Tag.bio 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 preclinical data products and collaborate efficiently using Tag.bio’s two-sided analysis environment.
Analysis Platform
Tag.bio 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 research 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 lead candidates, with your fellow researchers and collaborators
For data scientists and bioinformaticians
- Demonstrate the value of your data science in preclinical 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 preclinical 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 research 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, Tag.bio 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 preclinical 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 preclinical 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 research 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 Tag.bio
Tag.bio helps you overcome data challenges across your drug development process.
Basic Research
To identify novel targets, pathways, biomarkers, and signatures.
Clinical Trials
To stratify heterogeneous patient populations and develop diagnostics for stratification, toxicity, and response to treatment.
Regulatory Review
To produce quality controlled reports for reviews.
Real World evidence
To connect clinical endpoint biomarkers with real world data.
Data Security in Clinical Research Environment
Tag.bio 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.
Explore Resources For Life Sciences
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
