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Laboratory Software

Up in the Cloud: Advancing High-throughput Data Analytics

| Author / Editor: Alec Westley* / Marc Platthaus

Fig.1: Data Analysis is becoming more and more important for research and development.
Fig.1: Data Analysis is becoming more and more important for research and development. (Source: Thermo Fisher Scientific)

Remarkable recent developments in high-throughput research techniques allow scientists to collect more data than ever before. However, this presents significant challenges in terms of data storage, management and analysis. Cloud-based informatics platforms offer a potential solution, bringing together all aspects of laboratory management into a flexible and scalable system, designed to support future technologies as they become available.

Major recent advances in high-throughput technologies have resulted in a massive increase in the amount and complexity of data that can be generated. Modern techniques (such as next-generation sequencing, quantitative polymerase chain reaction, mass spectrometry and synthetic biology) have led to a huge expansion in the volume of information produced, with multi-dimensional data now generated on an unprecedented scale. This ‘big data’ revolution clearly has great potential for scientific advancement. The goal is to translate this dramatic increase in data into a similarly remarkable rate of progress and development.

There are significant challenges to make this a reality. High-throughput techniques often involve complex workflows, and organizations must be able to continually monitor and optimize these processes. The large amount of complex data produced must also be efficiently stored, organized and analyzed. With collaboration playing a greater role in the biotechnology landscape, it is becoming increasingly important to store complex data in a format that is accessible. Successful laboratories must be capable of addressing these challenges to data management, storage and sharing.

Traditionally, many laboratories and research organizations have employed on-site data management tools to organize their workflow data. But while digital systems have largely replaced the paper-based approaches of the past, many of these platforms have been introduced as point solutions for specific processes and are employed in isolation, not as part of an integrated whole. As a result, many data management platforms are still essentially fragmented and are unable to address the challenges posed by expanding high-throughput workflows. Furthermore, as the volume and complexity of data increases, there is a corresponding increase in the cost of maintaining data management systems on-site, which require greater storage capacity and computing power [1]. Ongoing resource investment is needed to maintain systems, perform upgrades and ensure continued compliance with evolving regulatory standards. Further investment of time and resources are needed to ensure data integrity and security of these systems. The current reality is that many laboratories employ fragmented on premises data management systems which incur a significant cost, and do not yield the benefits of digitization in terms of increasing efficiency and research quality. This fragmentation also increases the risk of data integrity and security issues.

A cloud-based solution to expanding R&D data

Cloud-based informatics platforms offer an affordable solution, overcoming the problems of on-site data management by providing organizations with elasticity, and facilitating collaboration and sharing. The most advanced platforms are much more sophisticated than simple data storage systems, managing all stages of research and development workflows, integrating with data acquisition and analysis systems, to create a connected digital ecosystem. These platforms provide the underlying data management infrastructure that organizations need to automate the process of data collection and analysis, store this data in an organized and searchable format, and ultimately apply the appropriate analytics. Through the platform, organizations have access to the insight they need to streamline workflows and optimize results.

Platforms are designed to be flexible — their major advantage is their “modular” nature, allowing them to be adapted to meet individual organizations’ needs. In addition to elements such as lab automation software and electronic laboratory notebooks (ELNs), extra versatility can be gained from adding workflow-specific applications (apps) which can be seamlessly integrated with the platform [2].

Additional Information
 
Artificial Intelligence

It can be challenging to integrate new laboratory systems with existing technology [3] — however, the modular and flexible nature of cloud-based informatics platforms should make the transition relatively simple. In any system update, data integrity is a significant concern and scientists must retain access to workflow-critical information throughout the transition. The advantage of modular cloud-based systems is the ease in which new features can be brought on-stream with minimal disruption. Organizations can choose either to completely change their existing system, or to integrate specific features without the need to replace their fundamental infrastructure. By implementing capabilities in a modular way, organizations can test, and if required validate, only the new solutions.

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