Unified Code & No-Code Discovery

Data types in this case study: RNA-Seq, WES

Case Study Highlights

In this case study we present how Sonrai Discovery supports both coding and non-coding researchers to work together on the same data in one centralized location. Sonrai’s integration with Jupyter Notebook and Nextflow allows bioinformaticians and coding researchers to create bespoke pipelines while non-coding researchers can utilize the platform for data analysis without the need to code.

0% Faster

Time to Initial Data Analysis

Our client’s Researchers can now complete initial data analysis 42% faster due to the no-code environment in Sonrai Discovery.


x0 Increase

In Prototyping Speed

Prototyping speed doubled with Sonrai Discovery and Jupyter, enabling rapid creation, testing, and iteration of diagnostic algorithms, accelerating innovation.


0% Reduction

Time to Achieve Regulatory Compliance

Simplified processes and adherence to ISO 13485 standards reduced the time needed to achieve regulatory compliance.

The Client

A prominent US-based diagnostic company with satellite locations across Europe faced a significant challenge in its biomarker discovery and development process. The scarcity of bioinformaticians and the growing need for expedited access to analyses among research scientists was a critical issue. Despite possessing valuable data, they needed help harnessing it effectively for biomarker discovery.

Lab Infrastructure

  • Workstations and Local Servers: Relied on powerful local workstations and servers for data storage and analysis.
  • Multiple Data Repositories: Data stored in various locations (servers, external drives, cloud services) without centralization.
  • Traditional Bioinformatics Tools: Web-based software was used for specific analysis challenges, such as differential expression analysis.
  • Manual Pipelines: Analytical pipelines were configured and run by bioinformaticians, making the process time-consuming and error-prone.
  • Siloed Systems: Various disconnected systems and platforms for different stages of the analysis pipeline.
  • Version Control Issues: The lack of a centralized data management system led to multiple versions of the same datasets, causing confusion and inconsistencies.
  • Resource Bottlenecks: High dependence on a small team of bioinformaticians created bottlenecks and delays, especially when key personnel were unavailable.
  • Skill Gaps: Research scientists lacked coding and data analysis skills, exacerbating delays in data analysis.
  • Inability to Scale: Infrastructure and manual processes couldn’t scale efficiently with increasing data volume and complexity, leading to delays and bottlenecks.
  • Slow Turnaround: Prolonged analysis time due to manual interventions and high dependency on bioinformaticians.

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"Prior to partnering with Sonrai, our teams operated in isolated silos, often struggling to bridge the gap between coding expertise and biological insights. Now, our research scientists perform their analysis without needing extensive coding skills, while our bioinformaticians continue to explore ideas in a code-based environment. This duality allows us to approach the same data from different angles, accelerating discovery."

The Challenge


The challenge stemmed from a resource-intensive workflow handled by a small team of bioinformaticians, which didn’t meet the urgent data analysis needs of research scientists. The departure of crucial bioinformaticians created irreplaceable gaps, further hindering progress. Researchers relied heavily on bioinformaticians to access and process data, working in isolation from each other. This lack of communication led to unclear methodologies and multiple data versions, resulting in slow and inefficient discovery and development processes.

  • The bioinformaticians were overwhelmed by various ongoing projects, leaving them limited bandwidth to assist researchers in real time.
  • While highly skilled in coding and data analysis, the bioinformaticians found it challenging to bridge the gap between their coding expertise and efficiently translating the researchers’ data analysis needs. While the researchers understood the biology, they needed help articulating their data analysis requirements in a way bioinformaticians could quickly grasp.
  • The challenging nature of their work sometimes meant that bioinformaticians operated in silos. They only occasionally had the time or means to understand the needs and concerns of the research scientists fully.
  • Research scientists needed quicker answers to their questions and the ability to perform initial data analysis independently. Relying on bioinformaticians resulted in delays.
  • The bioinformaticians conducted their analyses on local machines. This approach created problems related to data version control, unclear and non-reproducible methodologies, and the isolation of analysis insights that only the performer understood.
  • A centralized data management and analysis tool with great integration capabilities was needed to suit both the researchers’ and bioinformaticians’ requirements.

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"This transformative approach ensures that we're not just keeping up with industry standards but are setting new benchmarks in innovation and efficiency."

The Strategy

Sonrai Discovery unified the client’s diverse data sources. This code-free interface empowered researchers to explore vast datasets, construct cohorts, and perform analyses while integrated Jupyter Notebooks enhanced code-based analysis for bioinformaticians. This cohesive environment now fosters collaboration between bioinformaticians and research scientists, allowing both teams to explore and analyze data effectively and communicate in real-time.

  • Sonrai Discovery empowered research scientists to perform initial data analysis within a no-code environment, significantly reducing their dependence on bioinformaticians.
  • By integrating with Jupyter Notebooks, Sonrai enhanced the collaborative aspects of the data analysis process, ensuring that bioinformaticians and researchers could work together more efficiently.
    Sonrai’s solution improved version control, minimizing discrepancies and ensuring that analyses were replicable and traceable.
  • Sonrai’s capabilities facilitated the integration of diverse data modalities, creating a unified data source that enhanced the accuracy and efficiency of biomarker discovery.
"We can now validate our hypotheses more efficiently - without deep coding expertise. Being able to independently test ideas means that we can swiftly innovate, validate biomarkers efficiently and play a more active role in diagnostic development."


This partnership moved our client closer to creating innovative and life-saving diagnostic solutions. By promoting inclusivity, efficiency, collaboration, and dynamic use of skills, the dual environment provided by Sonrai Discovery overcomes obstacles related to varying skill sets, interdisciplinary collaboration, and bottlenecks, accelerating decision-making and optimizing the analytical process. Increased efficiency in bioinformaticians’ work reduced the need for additional hires, resulting in significant personnel cost savings.

Efficient Biomarker Discovery
Data Processing Time Reduction: 35%
New Discoveries: x3

Research scientists can efficiently discover and validate novel biomarkers without extensive coding skills, accelerating biomarker development.

Seamless Collaboration
Data Version Issues: Eliminated

The partnership fostered seamless collaboration between bioinformaticians and research scientists. They could work together more effectively, with researchers conducting initial analyses and bioinformaticians fine-tuning algorithms as needed.

Rapid Prototyping
Data Analysis Throughput: x2
44% increase in simultaneous analyses

The combined use of Sonrai Discovery and Jupyter Notebook within Sonrai’s cloud environment allowed for rapid prototyping. The company could swiftly test new diagnostic algorithms using real-world data, leading to more accurate and effective results.

Enhanced Insights icon
Custom Script Development
Pipeline Efficiency up by 35%
Error Rates: Decreased by 20%

With the flexibility to develop custom quality scripts using Jupyter Notebooks within Sonrai’s environment, the diagnostic company improved data accuracy and ensured the reliability of their diagnostic tests.

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Streamlined Regulatory Compliance
Regulatory Compliance Time: Reduced by 34%

Our client achieved streamlined regulatory compliance, benefiting from Sonrai’s ISO 13485 accreditation. Sonrai’s ISO-accredited Quality Management System (QMS) ensured efficient documentation and audited their data processes, ensuring strict adherence to industry regulations.

Adherence to FAIR Data Principles
Data Retrieval Time: Reduced by 41%

Sonrai’s capabilities align with the FAIR data principles by making data findable, accessible, interoperable, and reusable, ultimately enhancing diagnostic and biomedical application research and development processes.

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