Streamlined Proteomics and Transcriptomics Data Analysis for Drug Discovery in Oncology
Analyze omics data and explore biomarkers and pathways with no coding expertise required.
Goal
Parabilis Medicines, a clinical-stage biotech in Cambridge (MA), is developing first-in-class degrader therapies against “undruggable” intracellular targets in oncology. To support their ERG degrader program and additional discovery projects, the biology and proteomics teams needed a way to rapidly interpret growing volumes of transcriptomics and proteomics data from CDX and PDX models.
The goal was to replace spreadsheet-based workflows with an interactive platform that would let scientists explore multi-omics data themselves, systematically compare compounds and tumor models, and quickly reveal pathway-level effects, biomarkers, and potential toxicity signals, all while using consistent, reproducible analysis standards across the organization.
Challenges
Increasing data complexity
Large RNA-seq and proteomics studies from CDX and PDX models became difficult to analyze and compare across conditions.
Slow, manual workflows
Scientists depended on Excel and bioinformaticians, limiting flexibility and slowing down exploration and decision-making.
Lack of standardization
Each study followed a different analysis workflow, making cross-study comparisons and collaboration challenging.
Results with Omics Playground
Faster analysis
Scientists now review full studies in under 30 minutes and explore data independently.
Clear biological insights
On-target and off-target effects, pathways, and biomarkers can be identified across multiple datasets.
Consistent, collaborative workflows
All datasets follow a unified pipeline, improving reproducibility and teamwork across biology, proteomics, and bioinformatics.
Learn about Multi-Omics analysis in Omics Playground