Overview
One of the world's top-10 pharmaceutical companies, renowned for its life science breakthroughs, tasked its computational biology team with a very concrete mission: speed up early target identification and validation. On paper it sounded doable, but in practice they were drowning in omics data. The torrents of information coming from high-throughput experiments were massive, varied, and unstructured, turning what should have been a pipeline into a bottleneck. In short, the raw biological data wasn't translating into the clear, actionable insight they needed for drug discovery.
That's where Kanda stepped in. Together, Kanda and the client built a tailor-made omics platform, which comprises a suite of focused tools that consolidates data, enforces a common language, surfaces patterns, and provides intuitive visualizations. The new system lets computational biologists and their research partners handle complexity and extract meaningful findings faster than ever before.
The Challenge
Data Bottlenecks in the Hunt for Novel Drug Targets
Computational biologists transform complex omics data, or biological data made up of genomic, transcriptomic, proteomic, and metabolomic data, into discoveries. However, their effectiveness is dependent on the quality and accessibility of that data. The company's R&D teams were struggling with a disconnected data environment that created significant roadblocks to innovation.
Data Fragmentation and the Integration Hurdle
The most valuable omics datasets were not gathered in a single repository, they were scattered across isolated silos: some on internal servers, others with third-party vendors, and plenty on public sites like GEO. Adding patient information from electronic health records or clinical trial databases only made alignment nearly impossible.
Because the pieces were so fragmented, stitching together the different omics layers presented a significant challenge. Without a single, unified view, hunting for new, viable drug targets turned into a slow, cumbersome process that ate up time and resources.
Manual Annotation and Data Integrity Risks
The process for annotating datasets was reliant on manual, script-based workflows that were both inefficient and prone to error. This lack of standardization led to inconsistent labeling and compromised data quality, forcing scientists to spend valuable time cleaning and harmonizing data rather than analyzing it.
A Bottleneck for Single-Cell Data Discovery
Specifically for single-cell omics data, there wasn't a single system that collected all datasets together with their metadata in a standardized format. Without a central location and standard labeling, finding exact and relevant datasets was extremely difficult, leading to duplication of effort and wasted time. Furthermore, researchers did not have an easy workflow for requesting the ingestion and processing of new publicly available datasets. This placed the burden of locating data and coordinating with data science partners directly on the investigators, slowing their research progress.
The Solution
An Integrated Suite for End-to-End Genomics Data Management
Kanda developed a suite of four powerful web applications that form a comprehensive omics data platform. While each tool addresses a specific need, they share common backend services and are strategically integrated to create a seamless user experience.
Metadata Annotator
This tool provides an automated, centralized catalog for standardizing the organization and labeling of metadata. It guides data owners through a web-based interface with a controlled vocabulary, ensuring all information is consistent, searchable, and adheres to FAIR (Findable, Accessible, Interoperable, Reusable) data principles. This directly replaced the error-prone manual annotation process.
Dataset Transfer Tool
The Dataset Transfer Tool streamlines the movement of large datasets from external partners and internal staging areas through a guided, intuitive workflow. Its tight integration with the Metadata Annotator allows users to attach metadata at the moment of upload, ensuring data is securely transferred and correctly cataloged without data loss or manual hand-offs.
Visualization Platform
This interactive, web-based tool empowers scientists to visually explore complex biological relationships within high-dimensional omics data. The platform bundles a full suite of advanced visualizations, aligning computational specialists and bench biologists with a shared visual language. The tools spark new hypothesis ideas and translate complex results into something that any multidisciplinary team can comprehend.
Single-Cell Dataset Hub
Acting as a live search engine, the hub catalogs every single-cell dataset the company owns, whether sourced internally or pulled from public repositories. It scrapes sources like GEO and Rancho daily and crawls internal storage buckets hourly, so the catalog stays up-to-date without manual effort. It has an easy-to-use request system that allows researchers to submit requests for datasets to be added and processed, and which sends email alerts once their request has been processed and is ready to view in the hub.
Results & Impact
A Breakthrough in Neurodegenerative Disease Research
The platform's impact was immediate and profound, fundamentally reshaping the client's research operations and accelerating target discovery pipelines.
The most significant achievement occurred within just one month of launch, when the client's computational biology team identified a novel target for a neurodegenerative disease. This remarkable success is a clear testament to the platform's ability to accelerate scientific progress.
Other key impacts include:
Accelerated Target Identification and Validation
The platform delivers a single, centralized hub for clean, standardized data and straightforward visualization tools. It cuts down the hours spent wrangling data, letting teams dive straight into analysis and hypothesis testing to speed target identification and validation.
Enhanced Data Integrity and Reproducibility
The platform’s automated pipelines and consistent annotation scheme gently nudge users toward good data management habits. In practice, this means the data is well-governed and the whole process stays transparent. So researchers end up working with clean, trustworthy data, boosting confidence in the results they produce. And because the data is easy to reproduce, it hits the golden standard in computational biology: reproducible research.
Promoting Cross-Functional Collaboration
The platform provides a common space for data access and interpretation, helping research teams break down silos. With shared workspaces and easy-to-use visualizations, it brings computational, translational, and clinical scientists together to boost collaboration and speed data-driven decisions across the drug-development pipeline.
Conclusion
By transforming a convoluted, manual process into a unified, intelligent platform, Kanda helped this pharmaceutical leader unlock the value hidden in its omics data. The system not only speeds up the R&D cycle, as proven by the swift identification of a new disease target, but also encourages a more data-driven, collaborative culture across the organization. This partnership has created a solid foundation for future innovations and keeps the company competitive in the quickly-evolving fields of precision medicine and drug discovery.
