Building Resilience: A Short Guide to Implementing a Robust Bioinformatics Platform
How to ensure success when developing a reliable and scalable biological data environment
The goal of any bioinformatics platform is to function as a centralized infrastructure of tools and resources that support analytical and computational processes to manage and evaluate biological data. These platforms must be robust, able to perform consistently and scale up with the lab. Bioinformatics platforms can vary widely in design and scope, but there are some common implementation and maintenance strategies that will help keep your platform robust.
Define the launch objectives and system requirements
What is the main purpose and function of the bioinformatics platform in your research? Are there features, software, operating systems, or bioinformatics pipelines that must be included to handle specific data types? What are the inputs (e.g., RNA-seq data) and the outputs (e.g., data visualization and statistical analysis)? How will the data be stored?
Having a well-defined concept to build upon is crucial for understanding where the project is heading. Compatibility between operating systems, software, and bioinformatics pipelines is paramount for seamlessly producing and analyzing data. Educate yourself on the differences between storage infrastructures such as local storage, cloud computing, or a hybrid system. These choices can affect the throughput and scalability of your platform.
A knowledgeable, well-trained user base with access to accurate documentation will increase both user productivity and confidence in their results.
Provide documentation, user training, and feedback mechanisms
Who will be using the platform and how will they be trained and onboarded? How will software and training materials be stored, accessed, and reviewed? Will there be a dedicated support team or help desk? How will users provide feedback regarding bugs and errors?
A knowledgeable, well-trained user base with access to accurate documentation will increase both user productivity and confidence in their results. The ability to rapidly provide feedback to support teams will facilitate timely responses and minimize downtime. Set a schedule for documentation review to ensure materials are updated.
Perform system testing and quality assurance
How will the system and software be benchmarked and analyzed? Which quality assurance tests will be performed prior to launch? How often will the system undergo maintenance and performance testing?
Predefine the maximum workload that the platform can perform by using stress tests with high data volume and user counts. Testing for data integrity, quality control, stress, and reproducibility will ensure that the data is valid, and the retrieval and analysis results are consistent between runs.
Validate security protocols and system monitoring
Is the platform open to the internet? What security measures (e.g., firewalls, data encryption, etc.) will be implemented? How will security and user activity be monitored?
The continued success of your platform launch hinges upon predictive planning.
Verification of the user authentication process can help determine any user-based security concerns. Verify that sensitive tasks requiring high-level security clearance are locked to specific user roles. Routine security reviews and penetration testing can identify any technical issues or suspicious activity.
Scaling up and staying flexible
How can your platform remain flexible? When is the appropriate time to scale up your infrastructure? How will upgrades to the platform’s existing infrastructure be addressed?
User count, computational demands, and storage requirements will increase over time and must be scaled up appropriately through a variety of methods such as scalable storage solutions (e.g., cloud-based), database optimization, and load balancing mechanisms for systems and servers. Platform flexibility can be achieved through modular architecture design and plug-n-play hardware, allowing for digital components to be added or removed as needed to facilitate new workflows or data types.
Planning ahead is critical
The continued success of your platform launch hinges upon predictive planning. By answering the hardest questions during planning, you’ll develop a platform that provides a robust backbone for your research needs.