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Laboratory Software

Up in the Cloud: Advancing High-throughput Data Analytics

| Author / Editor: Alec Westley* / Marc Platthaus

Streamlined data acquisition and analysis

High-throughput technologies only increase the importance of automation and standardization to simplify processes and eliminate errors. Laboratory automation software can be incorporated into digital data management platforms, enabling scientists to optimize each individual process, and then couple them together into efficient and integrated workflows. This has significant benefits in terms of data integrity and quality control. With complex workflows, monitoring is key to ensuring correct and reproducible conditions. When stages are executed independently, upstream issues can easily affect downstream results. By ensuring immediate and easy data access and visibility, integrated digital platforms enable scientists to evaluate and adjust processes in real time, and ensure compliance with regulatory requirements.

Processes can be streamlined further by selecting workflow-specific apps, a feature which gives platform solutions their versatility. Some apps can improve efficiency by providing templates based on industry best practice. Others can be used for monitoring, for example, ‘bioreactor optimization apps’ that track reactor conditions and cell growth data. Apps can also be installed to support data analysis. Some platform providers allow users to develop and share their own apps to accelerate progress for the wider scientific community. For example, Thermo Fisher Platform for Science software now includes integration with R Shiny a cloud-based data visualization and analytics package. Integrating data visualization and analytics tools directly into a platform, enables scientists to develop their own data analysis apps and publish them to the platform. When providers integrate data analytics directly into the platform, they streamline the scientific workflow and make data immediately actionable.

Well-organized and sharable data

With an ever-increasing amount of multi-dimensional data to handle, a major challenge faced by data management systems is how best to organize a combination of structured, unstructured and reference data. By using cloud-based informatics platforms to organize multi-dimensional data into a single digital ecosystem, scientists can associate unstructured data with structured data, making it much easier to search and mine. Well organized and accessible data speeds the process of reporting and identifying trends. Data can even be cross-referenced with information from upstream and downstream processes, enabling end-to-end workflow visibility and, ultimately, better decision-making.

Using a single integrated digital ecosystem will also facilitate data sharing, which is becoming ever more important in the changing R&D landscape. Successful innovation in biotechnology increasingly depends upon collaborations, with partnerships between different organizations becoming more common. Cloud-based systems are ideally suited to collaborative working approaches, allowing easy data sharing and retrieval even with large and multi-dimensional datasets. Storing data in the cloud allows users to have real-time access, which can speed up the R&D process, both by increasing efficiency within an organization and by facilitating rapid sharing with external partners. Platform software allows users to adapt the type of ‘collaborative ecosystem’ that’s used, allowing organizations to grant full project-level or specific data-level visibility, as appropriate, depending upon the needs of each user.

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