Data Science & Machine Learning

Challenges with Diagnostics Data Processing Pipelines

Diagnostic companies rely heavily on the ability to process data efficiently and at scale in order to achieve both commercial growth and technological advancement. However, many of these companies face significant challenges in building and maintaining the infrastructure necessary to process data effectively. Diagnostic pipelines are generally set up on local/rudimentary infra that limits commercial growth and potential to scale to other geographies. In this blog post, we will explore some of the key challenges associated with diagnostics data processing pipelines and discuss strategies to address them.

Key Challenges with Diagnostics Data Processing Pipelines

Diagnostic companies are increasingly using advanced techniques such as next-generation sequencing (NGS), liquid biopsy, and multi-omics analysis to identify conditions ranging from cancer recurrence to early signs of diseases such as endometriosis. Consequently, these companies are now dealing with larger volumes of data than ever before. This data comes in various formats, including structured data from databases, unstructured data from log files, and semi-structured data from APIs. Handling such large and diverse data volumes can strain the engineering, and bioinformatics resources in traditional data processing settings resulting in long processing times, high costs, and limited scalability. Let’s take a deeper look at the challenges involved in setting up automated scalable data processing pipelines.

1. Infrastructure Limitations

One of the primary challenges for diagnostic companies lies in their local or rudimentary infrastructure, which restricts commercial growth and scalability across different geographies. Limited infrastructure makes it challenging to manage and run pipelines used in diagnostic assays efficiently. Moreover, the lack of scalable infrastructure hampers data storage capabilities, impeding the company's ability to handle large volumes of data effectively.

2. Limited In-house Engineering Resources

Building and maintaining scalable infrastructure for data processing requires specialized engineering expertise. However, many diagnostic companies lack the necessary engineering talent in-house. This is particularly true for biology-focused teams, as finding professionals with both biological and engineering expertise can be challenging.

3. Bioinformatics Challenges

Even when data processing pipelines are in place, many companies struggle with optimizing these pipelines for efficiency. A lack of bioinformatics expertise makes it difficult to fine-tune pipelines, resulting in long processing times from sample to report. In addition, inefficient pipelines lead to high processing costs per sample, limiting the company's scalability and profitability.

4. Manual Processes

Manual processes further hinder scalability. Relying on just manual interventions not only limits the company's ability to scale its operations efficiently but also increases the likelihood of errors.

Elucidata’s Solution to Address These Challenges

Organizations need to implement real-time data processing capabilities in their diagnostic pipelines. This may involve using stream processing frameworks to ingest and process data in real time. By processing data as it arrives, organizations can detect and respond to issues more quickly, minimizing downtime and optimizing system performance. At Elucidata, we understand the challenges diagnostic companies face when it comes to managing data processing pipelines efficiently. Our technical experts backed by Elucidata’s data & AI-cloud platform- Polly, offer a comprehensive solution designed to address the unique needs of diagnostic workflows.

User Journey on Polly

1. Custom Pipelines Tailored to Your Needs

Polly offers help to build data processing pipelines customized to your data and analysis requirements. You can from a suite of 30+ scientifically validated bioinformatics pipelines to process a host of multi-omics data types or use our expertise to develop and deploy customized pipelines tailored to your omics data type & analysis requirements. Whether dealing with different data types, sources, tools, or pipeline complexities, we offer the flexibility to adapt to your needs. This results in reduced processing times, lowered costs, and enhanced efficiency in data management and analysis.

2. Scalable & Cost-effective Cloud Computing Infrastructure

With Polly, diagnostic companies can seamlessly host, run, and manage their pipelines, from sample to report. Polly's scalable cloud computing infrastructure enables companies to efficiently process millions of samples across different modalities while optimizing costs. For instance, we can process 4000 bulk RNA-seq datasets per week at 50% of the usual costs, enabling significant cost savings without compromising performance.

3. Built-in Engineering and Bioinformatics Expertise

With our in-house engineering and bioinformatics expertise, strategically located in cost-effective regions, we provide efficient support tailored to your requirements. Whether you need assistance with infrastructure management, pipeline optimization, or any other area, our dedicated team can provide support efficiently and affordably.

Case in Point: How Elucidata Optimized the Diagnostic Workflow for a Women’s Health-based Startup and Accelerated their Sample to Report Generation by 2X

A San Francisco-based women’s health startup partnered with Elucidata to revolutionize menstrual health research. They aimed to understand the menstrual microbiome's biological characteristics and develop a diagnostic kit for uterine diseases. The startup faced challenges such as insufficient bioinformatics resources, computational infrastructure, and an information management system.

Elucidata provided solutions by developing customized pipelines for RNA-seq data processing, infrastructure for data analysis, and an information management system.

This collaboration leveraged Polly’s custom pipelines and infrastructure, and accelerated the development of diagnostic kits for predicting endometriosis by 2x and reduced costs by 50% resulting in significant annual savings of ~$1.6M. Read the case study here.

Conclusion

Efficient data processing pipelines are crucial for diagnostic companies to thrive in an evolving healthcare landscape. Overcoming challenges such as infrastructure limitations, engineering expertise shortages, and bioinformatics complexities is essential. Collaborations like the one between Elucidata and NextGen Jane showcase how optimized pipelines can accelerate diagnostic kit development and improve patient outcomes.

Connect with us or reach out to us at info@elucidata.io to learn more.

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