From legacy system to Discngine Assay: 5 steps to enhance assay data management
Efficient management and analysis of plate-based assay data are critical for accelerating compound discovery in life science research.
However, outdated or inefficient software solutions often hinder the progress of research teams, leading to fragmented data, limited analysis capabilities, and cumbersome research workflows. In that case, an ideal solution would be migrating to a new software - but it often seems a daunting and lengthy process, full of hurdles. This is why it is usually perceived as the last option by research organizations.
Recent development and deployment standards advancements have transformed these transition projects into easy and straightforward processes, replacing the once-scary and frightening experiences that migration projects were known for. Today, life science research organizations have more confidence in migrating to new, more efficient software, often working with external collaborations to aid their efforts.
As a European leader in Life Science research informatics, Discngine develops Software-as-a-Service (SaaS) solutions and successfully guides research-driven organizations through the migration processes. In this article, we will outline five proven steps, streamlined and tested with our customers, for a smooth transition to Discngine Assay - a SaaS solution that allows lab teams to capture, manage and analyze plate-based assay data for an efficient and data-driven compound discovery.
Step 0: Recognizing the Need for Change
Recognizing the need for change is the first crucial step in every software migration project, being for plate-based assay data management and analysis or other types of scientific enterprise software. Different factors and motivations can drive this need, but the most common ones are:
Security
Outdated providers and software solutions may not meet the latest security requirements, exposing valuable research data to potential security risks. As time passes, the vulnerability to data breaches and costly consequences only increases. Once this concern is taken seriously, a curtail step is to select providers certified with ISO 27001 or other relevant security standards, ensuring robust information security management and peace of mind for innovative discoveries.
Scalability
Another pressing issue is the lack of scalability in old, on-premise solutions. As research progresses and data volumes grow exponentially, these outdated systems can become bottlenecks, slowing down crucial processes. Transitioning to a cloud environment is critical. Cloud-based solutions offer the scalability needed to accelerate research operations without compromising present and future efficiency. Additionally, the cost-effectiveness of cloud solutions makes resource management more economical, enabling organizations to allocate their budgets more strategically.
Flexibility or modularity
The limitations of older solutions in handling newer data types, methods, and workflows can significantly hinder research progress. Research-driven organizations require flexible and adaptable IT ecosystems to keep pace with the latest advancements.
Automation
Lack of automation in data analysis and management can present a considerable challenge, particularly when dealing with large amounts of data like it is the case with High-Throughput Laboratories. The time-consuming manual processes can impede researchers' progress and delay critical insights.
Step 1: Scoping the project
Once the decision is made to change the solution, the first step is to scope the project. This involves identifying all active and/or relevant projects, systems and research-related data that the migration process will impact. This step ensures that no critical data or processes get lost during the transition and prevents any disruption in the ongoing work in the Lab.
In the context of migrating to Discngine Assay, this is illustrated by:
Identify active projects: which legacy data must be reprocessed for the migration.
Identify relevant projects: which legacy results will be migrated (without data being reprocessed).
Identify systems that are currently connected to the old assay data management software.
Decide for other projects: whether to migrate or archive them.
Define your assays, their data model, and the way they are calculated in the new application Assay.
At this step, we have:
the projects for which data will not be migrated
the projects for which results will be migrated as is
the projects for which raw data will be reprocessed
the active projects for which results are still being created
Step 2: Preparing the migration
This part is composed of several parts that can be run in parallel.
In the first part, we need to prepare Assay to be able to analyze the plate-based screens results, with the help of key users:
Migrate all plate layouts from active projects ( This can be done very quickly and easily in the application or Discngine can provide services to help you accelerate this step).
List the raw data formats and calculations used in active projects in the application, and make sure there are equivalents in Assay (again, Discngine can provide services to help accelerating this process)
Create run templates in Assay for each plate-based assay template existing in the current solution. To avoid unnecessary work, verify if the templates already exist in Assay. Keep in mind that it doesn’t necessarily follow a 1:1 rule.
At this stage, Discngine can support reading raw data directly from the previous solution.Automatic QA/QC can also be included in the analysis templates that are being created.
Verify the accuracy of the obtained results in Assay (compared to those obtained in the previous solutions after the data processing).
Example of a Plate Layout view in Assay
In the second part, we need to prepare Warehouse to receive the historical and new results data. Warehouse is a central repository included in Assay, that enables you to integrate data from multiple data sources in a consistent and structured format. In Warehouse, you will define all your assays and test the migration of existing data that will not be reprocessed.
For faster results, Discngine can help automate this step.
Finally, in the third part, we need to prepare the move of existing integrations to the legacy system to Assay. Here, Discngine can help integrate Assay with the rest of the customer’s informatics ecosystem.
All major projects start with a meeting... Get in touch with one of our experts!
Step 3: Onboarding the users
After completing the configuration of Assay, it is time to onboard users onto the new solution. This step is critical to ensure seamless tool adoption within a laboratory. Scientists will undergo comprehensive training to become familiar with the new interface and workflow of the Assay application. Key users that have been part of the setup phase can play an important role in increasing the adoption of Discngine Assay.
At Discngine, this stage is taken very seriously, and we provide personalized onboarding for key users conducted by our expert team. If additional users need onboarding, we provide recorded training sessions and tutorials. Moreover, our support team is always available to address questions our users may have during the transition period.
Step 4: Production Rollout
It is the last step of the migration project and one of the most important.
The previous steps ensure that the new application is ready to be fully integrated into the Lab and that the lab team is ready to welcome the new solution.
To successfully complete the migration, it is necessary to:
Coordinate with the customer. We will need a transitory period, where scientists should only add new data using the legacy system (hence, they shouldn’t modify existing data)
Migrate the legacy data that won’t be reprocessed in Warehouse
Reprocess all past and ongoing project data using the Autopilot mode: a feature allowing users to automate most of their analysis, often transforming a process that lasts hours into a few minutes.
Publish the results to Warehouse (automatically or with a comparison to legacy results)
Perform the last “delta” migration in coordination with the customer. We will need a short period where the users should not use the legacy system nor the new one. Results that have been created since point 1 will be migrated directly to Warehouse or reprocessed using Assay. This “delta” migration will ensure the legacy system and Assay are up to date.
Example of a Cross Runs Visualization in Assay
Step 5: Celebrate & Enjoy Discngine Assay!
The migration is complete! It is essential that users only use the new application from this moment; removing user access to the old application is still the best way to accomplish this. Feedback and user experience are valuable; we recommend gathering and analyzing it after a while to help you improve the usage of the solution.
Transitioning to a new platform can come with questions or challenges, and if any difficulties arise or there are inquiries, the Discngine support team is ready to provide assistance. Discngine Assay can also incorporate a sample management module developed by us, reflecting your laboratory's inventory and enabling your staff to track compounds throughout their lifecycle within the laboratory meticulously.
A few words about factors influencing a migration project
Although the described steps might seem like a lot of work at first, the migration project can be completed quickly if well-prepared. As user adoption is a key success factor of this kind of project, at Discngine we recommend involving users, either all or a significant part of them, in the decision to change software solutions, in scoping the project, in configuring Assay and in accepting it.
In addition, Discngine has the experience and expertise to execute projects like this rapidly. However, we must emphasize critical points that directly influence the project duration, such as:
The number of different types of assays realized in the Lab (because it is mandatory to define them in Assay) will directly add to the project duration. This differs from the amount of historical data, which is not a problem because everything will be automatically processed.
Resource allocation is always an essential factor. The availability of members involved in the project will directly influence its duration, especially when decisions need to be made or during the onboarding step (Step 3).
Conclusion
In conclusion, migrating to advanced, modern software solutions like Discngine Assay is crucial for lifting barriers and accelerating compound discovery in life science research. With the proper provider support and a well-executed migration plan, research-driven organizations can embrace the future of data-driven compound discovery and achieve greater workflow efficiency. Discngine's commitment to guiding and supporting its customers throughout the migration process makes them a reliable partner in the journey toward improved plate-based assay data management and analysis.
If you would like to explore the benefits of Assay with a case study of a migration project realized with a Global leader in cosmetics, feel free to contact us.