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The Benefits of Automation for Antibody Discovery Workflows

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At the recent Bio-IT World 2022 conference, Andrew LeBeau, associate vice president of product integrations at Dotmatics, gave a talk on the benefits that automation can bring to antibody discovery workflows. We spoke with LeBeau to find out more about these benefits and how Dotmatics’ approach meets one of the key themes of the conference, FAIR data principles.

Ruairi Mackenzie (RM): What are the existing issues with data handling in antibody discovery?

Andrew LeBeau (AL): For all research organizations, finding and managing data across siloed enterprise and local systems is a critical challenge. For antibody discovery specifically, there is added complexity due to the diversity of antibody-based entity types created, which poses additional challenges for informatics systems in managing definitions and supporting workflows.

Disconnected workflows force manual data handling, which is labor-intensive, error-prone and hugely inefficient.


Antibody discovery requires sophisticated software analysis and visualization tools to maximize workflow efficiency and innovation. Unifying researchers’ favorite software applications within a central IT infrastructure is an issue for most organizations looking to increase efficiency and collaboration across the entire process from initial candidate selection and refinement through to animal trials and translational medicine.


RM: How are FAIR data practices incorporated into the Dotmatics antibody workflow?


AL: Dotmatics was FAIR before FAIR was a thing. Dotmatics was founded on the concepts of providing users access to all the relevant data at any time, and to be able to reuse that data. Dotmatics Browser is the cornerstone of this, aggregating data from multiple sources (Dotmatics and non-Dotmatics, local and online) and presenting it to users in an easily consumable format. Browser is also able to send data to other applications, such as Geneious Biologics and GraphPad Prism.


RM: What’s the advantage to researchers of centralizing data management?


AL: It allows researchers to find the information they need to make timely, well-informed decisions, meaning that research projects proceed at good pace and are based on objective interpretation of data, rather than subjective views based on incomplete data.


RM: How do you balance the benefits of automation with the goal of keeping your users in control of their workflows?


AL: There is no conflict here. We focus on automating processes that require no scientific intellectual input – processes such as data wrangling from multiple sources, copying and pasting, etc. These are tasks that don’t inherently add value to the data (they in fact can cause degradation in data quality through inadvertent errors being introduced by manual data handling). These activities are necessary to present data to researchers for them to apply their scientific knowledge and experience to make well-informed decisions. Users remain in control of the key elements of their workflows – we just remove the unproductive, labor-intensive tasks that prevent them from fully focusing on applying their skills and knowledge.


Andrew LeBeau was speaking to Ruairi J Mackenzie, Senior Science Writer for Technology Networks