Empowering research teams: The path to accessible immune repertoire data

B and T cell repertoire mining has taken tremendous technological leaps in recent years. Immune repertoires contain a wealth of information that can prove useful for developing treatments for many different diseases and conditions. The tools used for analysis and interpretation of immune repertoire sequencing (Rep-Seq) data are crucial for the conversion of raw data into insights that can guide the development of novel treatment strategies. It has become increasingly clear that the analysis is at least as important as the quality of the data itself!

While Rep-Seq is a powerful technology used by leading pharma, biotech, and academic organizations, many still struggle to extract its full potential. Due to the scale and complexity of Rep-Seq data, researchers in charge of the interpretation of data, are often not able to analyze it. In other words, biological questions need to be converted into computational approaches; a process where critical nuances tend to get lost in translation. To fully reap the benefits of Rep-Seq data, analysis and interpretation need to be in the same hands: those of the researcher.

The challenges of accessible Rep-Seq data analysis

As discussed in our white paper, the exploration of immune repertoires requires specific analysis tools. Substantial bioinformatics expertise is usually necessary to apply these processing and analysis tools with the right parameters. On the computational side, these analyses are quite demanding, and can require dedicated computing clusters to function properly. Setting up and running infrastructure and analyses is a non-trivial task and requires significant technical expertise. This poses a problem, as it introduces a disconnect between the person who performs the analysis and the one who interprets the biological relevance of the findings (e.g., an immunologist). The result is an ongoing back-and-forth between bioinformatician and researcher, where time is lost on explaining and refining analytical approaches, and knowledge is lost in translation.

Depending on the budget, scale and complexity of the workflow, organizations can choose to invest in: (1) the development of an in-house analysis solution, (2) ready-to-use technologies installed on-site, or (3) comprehensive Software as a Service (SaaS) solutions. Developing analytical methods in-house that facilitate researcher access, comes at the cost of development, hiring risks, training time, setting up data storage and computational infrastructure, and potential security and data access concerns. External solutions are often more refined in terms of their user experience, and provide a more comprehensive package, as they have been developed by considering the needs of larger numbers of users with diverse workflows. In addition, active development and dedicated support teams ensure smooth resolution of issues and futureproofing.

To fully “democratize” the analysis of Rep-Seq data, the solution must contain the following important properties:

• Ease of use. Users of all skill levels should find the software easy to use and the onboarding quick and stress-free
• Solid workflows and algorithms. The software should offer sensible, and tested defaults for workflow parameters and methods, while allowing tweaking for advanced users
• Powerful and scalable computations. Rep-Seq data is complex and vast in scale, and it requires matching computational infrastructure
• Maximized data security. Data must reside in a secure location, and only be accessible to those with the right permissions

The advantages of democratized data analysis

Democratizing the analysis of Rep-Seq data means that scientists and collaborators have access to tools that enable them to directly extract value from all available data, avoiding bottlenecks that would otherwise slow down innovation and development. This benefits the entire organization:

Time savings. When data management and analysis are performed by the same person, workflows are streamlined and efficient. In any analysis there are common steps (e.g., raw data processing, sequence annotation) that are better done by those who know the data best: the researcher. However, to make this possible, there needs to be a solution with an intuitive, graphical user interface and great user experience. By democratizing the processing of Rep-Seq data, you minimize the need for analysis requests and back-and-forth communication between different departments. This means: less time lost in waiting for analyses to be performed, and more time spent on moving projects forward.

Improved outcomes. Data democratization allows all team members to bring their best skills to the table. By empowering those with first-hand experimental knowledge to categorize and explore the data, intangible biological insights are added to the analysis. “The panning round was too stringent? The PBMC count was different? Gene usage pattern looks familiar?”. By allowing researchers themselves to explore the data, critical domain knowledge can provide the context to find better insights.

Continuity. A bioinformatician leaving the team can cause significant issues and delays, as a new employee inherits a complex analysis infrastructure and workflows that were set up by others before them. By placing accessible, powerful tools in the hands of researchers, analyses can continue uninterrupted, and momentum is kept.

Standardization. The right platform will make it easy to share data among researchers and exchange analysis insights. Moreover, reproducibility is improved, as analyses are standardized and better documented. Data sharing and standardized analyses streamline the work, help with traceability and lead to an easier path towards resolving patent disputes, or publishing.

If your organization is serious about immunomics data analysis, you must give careful consideration as to how your teams are empowered to analyze and share Rep-Seq data at scale.

Implementing a solution for accessible data analysis

Successful implementation of a Rep-Seq data analysis solution begins with involving the right stakeholders into the selection process:

• Researcher. Nobody understands the research question better than those performing the lab work. Involving researchers early in the process ensures you capture the most important use cases. Include users of all skill levels to ensure everyone feels comfortable with the solution and minimize onboarding time.
• Project manager. Different projects can have different requirement. By including project managers, you ensure that all (sequencing) data types from different projects are considered. They can also provide input on, for example, which species need to be supported, or if access to external databases is needed.
• Data analyst. Include your data analyst(s), or (bio)informaticians to evaluate the alignment with current algorithms and methods, and the support of advanced functionalities.
• IT team. They ensure that the platform solution integrates well with any existing data architecture and complies with the safety requirements. This is key to foster improved collaboration and interaction between team members and departments.

The IGX Platform empowers everyone in your team to do their own analysis

The IGX Platform is an intuitive solution that takes the complexity out of managing and analyzing Rep-Seq data. It’s developed by immunologists, computational biologists, data scientists, and software engineers. It enables any scientist to perform their analyses and takes the critical step forward in democratizing immunomics analyses.

The IGX Platform:

• Removes the complexity of managing and analyzing immune Rep-Seq by offering an intuitive, code-free environment
• Adapts to your workflows with its technology-agnostic infrastructure
• Empowers teams to work independently without reliance on bioinformaticians
• Standardizes data management and analyses so teams can contribute and exchange data more easily

Remove analytic bottlenecks and bridge the gap between Rep-Seq data and research insights with the IGX Platform.

Ready to get started?

Democratized Rep-Seq data analysis requires a system that provides oversight and governance. Most importantly, it should provide an easy way for researchers to analyze the data to answer their biological questions, accelerate discovery process, and uncover new insights and opportunities for your organization. It enables the right people to explore Rep-Seq data at any time, across departments and teams, to make decisions without unnecessary barriers.

If you are looking for ways to streamline data analysis and antibody discovery workflows, and increase productivity, talk to our experts to discuss your research needs. Our team is ready to help compare available bioinformatic solutions, discuss best practices, provide a tailored proposal, and assist you every step of the way until your researchers are running their workflows smoothly on the IGX Platform.

Contact us to schedule a live demo!