Navigating the Future of Bioinformatics: A Deep Dive with Evan Floden, Seqera

About the Interviewee

Evan Floden is the Chief Executive Officer (CEO) and one of the Co-Founders of Seqera.

Evan Floden, B.Sc., I.M.Sc., Ph.D., serves as the Chief Executive Officer (CEO) and Co-Founder of Seqera, a leading company focused on advancing scalable and reproducible data analysis through its Nextflow workflow engine. His vision is rooted in empowering scientists and researchers to apply cutting-edge software tools to solve complex biological challenges.

Dr. Floden holds a Bachelor of Science in Biotechnology from Victoria University of Wellington, an International Master’s in Bioinformatics, Biomathematics, and Computational Biology from Alma Mater Studiorum – Università di Bologna, and a Doctor of Philosophy in Biomedicine from Universitat Pompeu Fabra. His multidisciplinary academic background equips him with a comprehensive understanding of biomedicine, bioinformatics, and biotechnology, enabling him to address the needs of Seqera’s customers and partners effectively.

Throughout his career, Floden has contributed to pioneering projects, including developing a bioscaffold platform for tissue regeneration at Aroa Biosurgery and supporting the Rfam and Pfam databases for RNA and protein families during his time at the Sanger Institute. A dedicated lifelong learner, he remains committed to exploring new technologies and methodologies in the field of life sciences.

The Discussion

Driving Forces Behind a Career in Bioinformatics and Leadership at Seqera

[Dex Marco]: It’s such a pleasure to have you here with us today, Dr. Floden. So, Evan, with more than a decade of Bioinformatics experience being a research assistant, a development scientist, a senior bioinformatician, and postdoctoral researcher, what would you say drove you towards this field, and what brought you to eventually Co-Found and become the CEO of Seqera?

[Evan]: My journey into computational biology and bioinformatics was somewhat preceded by my time working in the lab. I initially got involved in lab work straight after my undergraduate studies, where I was conducting research on yeast. Over time, I noticed the field becoming increasingly tech-focused, with much more automation and robotics being integrated into our lab work. This shift sparked my interest in bioinformatics. I have to admit, I wasn’t necessarily the most adept with my hands when it came to lab work, and after spending many hours conducting experiments, I realized that I might be better suited to focusing on programming. Slowly, I began to immerse myself in coding.

I eventually pursued a Master’s in Bioinformatics, where I learned the fundamentals of programming, particularly Python and algorithms, which truly ignited my excitement for the field. That was around ten years ago, and I haven’t looked back since.

[Dex Marco]: So, your background is in bioinformatics and biomedicine, correct?

[Evan]: That’s right, absolutely. My academic background is in molecular biology, which is quite standard for those in bioinformatics, but I always had the ambition of working in biotech. After my undergraduate studies, I gained early experience with a small startup—there were only about five people at the time—that was developing a medical device. It was a biologic, and that experience laid some of the groundwork for my later work at Seqera, especially in terms of creating a company and understanding the product development lifecycle. I also gained invaluable insights into bringing a product to market, including navigating FDA approval, which was highly educational.

[Dex Marco]: How was the transition for you, moving from academia to the corporate world, especially with Seqera being in the corporate space?

[Evan]: I’d say I’ve made the transition twice. After working at the startup, I returned to academia to pursue my Ph.D., and it was from there that I was able to co-found Seqera and re-enter the commercial world. Bioinformatics as a discipline is quite unique in that it maintains strong roots in academia—most of the software bioinformaticians rely on is developed and maintained by academic researchers. So, there’s always been a connection between academia and industry in this field. The work environments, of course, differ in terms of incentives and structures, but I’ve found both settings rewarding. Who knows, maybe one day I’ll return to academia again.

[Dex Marco]: I love that you highlight the seamless transition between academia and the corporate world—it’s great to see how transferable the skills are.

Pioneering Nextflow: The Journey from Concept to Impact

[Dex Marco]: Nextflow has revolutionized the way scientific workflows are managed and deployed. Could you walk us through the inception of Nextflow and Seqera? What were the key challenges you faced in the early stages, and how did you envision these technologies transforming the field of computational biology and bioinformatics?

[Evan]: Nextflow’s inception was around 2013. At the time, my co-founder, Paolo, and I were part of a computational lab focused on developing computational methods. The primary focus of the lab was on multiple sequence alignment—essentially, how to line up thousands of sequences and extract meaningful information from them. We were working on studies involving an enormous number of sequences and combinations, which presented significant challenges in terms of computational power and workflow management.

Paolo, who had a software engineering background, believed that we could apply software engineering principles to improve workflow management. In computational biology, workflow management involves numerous steps—often as many as 50 or 60. Each step might require a different tool, software, or script—some developed in-house, others downloaded from platforms like GitHub. The challenge is to integrate all these disparate elements into a coherent process, essentially turning them into a unified piece of software.

The timing and emerging technology was perfect.. Containerization tools like Docker were emerging, allowing us to encapsulate all the dependencies needed for a workflow. Cloud computing was also becoming more prevalent, enabling greater scalability and flexibility. Platforms like GitHub were rising in popularity. Git enabled scientists to not only publish papers but to share complete analyses and collaborate on tool development. This collaborative aspect has been incredibly powerful and continues to be a fundamental part of Seqera’s ethos.

These three technologies—containerization, cloud computing, and Git—came together to address the problem of workflow management. Nextflow started as an open-source project and gained traction organically. Over time, as more users adopted it, especially within larger organizations, the commercial potential became clear. That led to the founding of Seqera, which allowed us to provide extended functionality, keep building out the software, and offer commercial support.

[Dex Marco]: I’m enthused with your emphasis on how Seqera’s founding was a natural evolution, driven by the growing adoption of the platform and the need for enhanced functionality and commercial support within larger organizations.

The Power of Open Source in Advancing Scientific Research

[Dex Marco]: Seqera has embraced open-source principles through Nextflow, promoting transparency and collaboration in scientific research. How do you see the role of open source and open science evolving in the life sciences sector, and what impact has this philosophy had on the adoption and success of your technologies?

[Evan]: Open source and open science are related concepts, but they serve slightly different roles. Open source is just a license—it makes software transparent and available for use by others. However, open science is a much broader movement. It not only involves making code open source but also focuses on making data more accessible, sharing methodologies for how analyses are conducted, and even providing insights into the underlying infrastructure. Another important aspect of open science is open publishing, which has gained significant momentum as journals are becoming much more open.

Seqera operates in both realms. On the open-source side, we have Nextflow, MultiQC, one of the most popular tools for reporting bioinformatics analyses, and Wave, a container technology.

The impact of these technologies really shines through the open-science side, particularly through our community engagement.

A prime example of this is the International nF-Core community, which now  over 10,000 members. These are domain experts who collaborate on running, building and maintaining pipelines. Some of these pipelines have had contributions from hundreds of people, with each person bringing their expertise to solve specific scientific problems. This collaborative approach provides scientists worldwide with high-quality tools off the shelves, significantly reducing the time spent re-inventing analysis tools. Our belief is that the  open-science model greatly expedites scientific progress.

[Dex Marco]: By enabling broad collaboration it enhances the value and accessibility of the work, demonstrating the profound impact of diverse contributions on problem-solving and code quality. Wonderful! Onto the next question.

Data-Driven Transformation in Computational Biology and Bioinformatics

[Dex Marco]: The advent of Next-Generation Sequencing (NGS) and the explosion of data have significantly reshaped computational biology and bioinformatics. From your perspective, how has this data revolution altered the landscape of these fields, and what role does Seqera play in addressing the challenges and opportunities presented by this shift?

[Evan]: NGS (Next-Generation Sequencing) has led to a dramatic increase in the volume of data, which is changing the industry for the better but also comes with its own set of challenges. When you’re dealing with terabytes of data, even simple tasks like moving that data can become complicated. You encounter this concept of ‘data gravity,’ where the data’s size and importance make it harder to move, especially for researchers working remotely who need quick access. This has led to shifting the analysis and compute processes to the data itself, which has been enabled by advancements in automation and workflow management.

These workflow management technologies have been widely adopted in fields like genomics and transcriptomics, but I’d say this approach is also being adopted more broadly. NGS is highly interdisciplinary—you need biologists to prepare experiments, bioinformaticians to run the computational analyses, statisticians, and often infrastructure experts to set up the environments for all this to work smoothly. Handling these diverse needs in a unified way has become crucial.

In terms of the impact NGS has had, areas like drug discovery and development have been transformed rapidly. High-throughput screening and predictive modeling have become integral. We’re also seeing precision medicine evolve from a promising concept into something that’s actually being applied. NGS is becoming routine, both in terms of diagnostics and predictive technologies, for example, understanding which proteins are expressed on the surface of cells, which allows for the creation of highly targeted therapies.

[Dex Marco]: This isn’t part of the standard set of interview questions, but I’d like to get your perspective on something. I interviewed another CEO who mentioned that as precision medicine continues to gain traction, all diseases will eventually become ‘rare diseases’ because they’ll be highly specific to the individual. What’s your take on that?

[Evan]: Absolutely, that’s already beginning to happen. We’re seeing it with some of the highly publicized cases like Moderna’s work on melanoma, where each patient is fully sequenced—both their normal tissue and tumor—and this information is used to create a specific set of mRNAs tailored to that individual. This approach has been proven in some cases, though there are still challenges, particularly around cost. Another challenge is how these treatments will be approved, as it’s not just  about approving a single small molecule anymore. In some cases, we’re approving an entire approach, or even a machine learning model used for prediction. It’s an exciting time with a lot of opportunities to make a big impact.

[Dex Marco]: Your perspective on the evolving landscape of treatment approval, including the role of machine learning models, no doubt underscores the exciting and transformative potential of these developments in personalized medicine.

AI’s Emerging Role in Life Sciences: Insights from Seqera

[Dex Marco]: Artificial intelligence is increasingly becoming a driving force in life sciences, from drug discovery to personalized medicine. How do you see AI intersecting with the work you’re doing at Seqera, and what potential do you believe AI holds for the future of bioinformatics and computational biology?

[Evan]: I’m not expecting the creation of AGI—Artificial General Intelligence—anytime soon, but if we were already at that stage, it would certainly change the landscape for bioinformatics. That said, we’re in a very exciting time, with significant changes happening now. For us, Nextflow language is  syntax for writing pipelines, and what I see increasingly is the central role of software in life sciences. This importance is only going to grow.

The way software is developed is also evolving rapidly with AI, particularly with large language models (LLMs). Some of the really exciting developments are in how you actually write code. Instead of manually coding or even using something like GitHub Copilot, you can now use natural language to generate code. This is just as applicable to scientific analysis as it is to traditional software development, and it’s already starting to make a significant impact.

Beyond that, one of the more practical applications is in experimental design. Robotics, combined with LIMS (Laboratory Information Management Systems), can now handle a lot of the high-throughput screening and liquid handling. This allows for the automation of experimental design in ways we couldn’t have imagined a few years ago. Companies like Recursion have invested heavily in using LLMs to drive these types of experiments, which is incredibly exciting for the field.

And, of course, we can’t ignore the advancements in prediction, especially with technologies like AlphaFold for protein structure prediction. It’s fundamentally changing the game. I have colleagues from my time in the lab, and speaking with them now, it’s incredible how reliant they’ve become on AlphaFold for their work. It’s becoming a fundamental part of the scientific process, and this is just the beginning of a broader shift toward more computational science.

[Dex Marco]: It’s fascinating to hear how bioinformatics is becoming a fundamental tool for individuals without a background in the field. This shift towards computational science underscores a significant transformation in how scientific research is conducted. It seems we’re just at the beginning of a broader movement that will increasingly integrate computational tools into the scientific process, expanding their accessibility and impact.

Seqera’s Vision: Empowering the Future of Bioinformatics

[Dex Marco]: Seqera’s mission is to simplify complex data analysis pipelines in the cloud and empower scientists and developers across various sectors. How does Seqera enable these advancements in open science, data management, and AI, and what future developments can we expect from your team to further support the life sciences community?

[Evan]: Advancing science for everyone through software is really central to our mission at Seqera. The future of life sciences will increasingly be collaborative, and the ability to share resources, tools, workflows, and data is essential to that vision. Platforms like Seqera and community resources like nf-core are key enablers, allowing for this sharing, standardization, and even the integration of AI into scientific workflows. In fact, many of the nf-core pipelines we support are essentially AI-driven pipelines.

We also see a major opportunity in supporting the next generation of life scientists. As more scientists come into the field, we believe they will be computationally adept, even if they don’t necessarily have a computer science background. Our goal is to empower them by making these tools more accessible, so they won’t need to write code. Instead, they’ll be able to describe what they want to do—something like ‘run this analysis on this data’—and the system, supported by AI, will handle the rest.

A key development for us has been our recent acquisition of Tiny Bio, a company with deep expertise in generating workflows and interacting with scientific data using human language. While it’s still early days, we’re incredibly excited about the potential of this technology. It’s a huge step toward democratizing data science and making it more streamlined, so that even those who aren’t deeply involved in the field can still leverage cutting-edge tools and analysis.

[Dex Marco]: The vision of making data science more streamlined and user-friendly resonates with the broader goal of empowering more people to engage with complex analyses effortlessly. It’s exciting to think about how this will shape the future of data science and make its benefits more widely available.

Future Vision for Seqera: Essential Qualities and Skills for Continued Success

[Dex Marco]: Evan, given your extensive experience and expertise at the intersection of bioinformatics and software development, how do you envision your future role as the CEO of Seqera, the home of Nextflow? What key qualities or skills do you believe will be most essential to drive the company’s success and maintain its position as a leading provider of innovative solutions, from simplifying complex data analysis pipelines to enabling scalable, reproducible workflows in the cloud, to empower scientists and bioinformaticians across the life sciences industry?

[Evan]: My role has definitely evolved as the company has grown. We started with just two people, and now we’re up to around 100. As the company grows, your role changes, and you have to adapt and grow with it. One thing that remains constant is the importance of people. Managing and inspiring a team, setting a clear strategy, and ensuring it’s executed effectively are critical aspects of the job.

At the same time, I remain deeply involved in driving our technology and product strategy, especially with the rapid changes happening in the field. It’s essential that we stay ahead of these developments, but equally important is ensuring that we do this in close collaboration with our customers. With over 125 customers in the life sciences space, including 13 of the top 20 big pharma companies, it’s vital that we remain attuned to their current needs and anticipate how their requirements will evolve in the future.

[Dex Marco]: I can see two concepts that are central to the Seqera approach: the human-in-the-loop aspect and customer centricity. There’s no replacing people with AI—AI is a tool to enhance human expertise, not replace it. I can see that at Seqera, it’s about enabling more people to use the technology effectively and leveraging collective intelligence to solve problems faster.

[Evan]: Absolutely, we’re focused on using AI to help people do more and do things better, but never to replace them. Our goal is to bring more minds into the fold and expedite problem-solving through collaboration and innovation.

Engr. Dex Marco Tiu Guibelondo, B.Sc. Pharm, R.Ph., B.Sc. CpE

Editor-in-Chief, PharmaFEATURES