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Inside OHMX.bio: The future of bioinformatics and multi-omics

An Interview with Steven Verbruggen, Head of Bioinformatics at OHMX.bio Interviewed by Joannes van Cann
Joannes van Cann: Can you briefly introduce yourself? What attracted you to bioinformatics, and how did you end up joining OHMX.bio?

Steven Verbruggen: Honestly, what first attracted me to bioinformatics was that I was quite clumsy in the wet lab! However, I always loved mathematics and programming. Discovering the combination of programming and biotechnology at university was a real eye-opener for me, and I knew I wanted to invest my time there.

During my time at university, I already collaborated with our founder, Gerben Menschaert. OHMX.bio was still a startup needing extra help on the bio-IT side. Since I was already working on many of the techniques the company utilized, it was a natural transition to join the team.

Joannes: Which omics technologies have shaped your career the most?

Steven: During my PhD, I focused heavily on ribosome profiling and its interaction with mass spectrometry-based proteomics. From there, we also introduced Oxford Nanopore sequencing. Those two sequencing technologies, ribosome profiling and Oxford Nanopore, were definitely the most prominent in shaping my academic career before I joined OHMX.bio.

Joannes: When a partner wants their data analyzed, how does a typical project unfold step-by-step?

Steven: It depends on the scope of the work. For projects that include a wet lab phase upfront, we coordinate closely with our wet lab technicians and project managers to understand how the experimental phase went and gather necessary background data.

For dry lab-only projects, we interact directly with the partner right from the start to understand the origin of the samples, the data, and the broader research context. In any scenario, we invest a lot of time in communication, discussing results, presenting data, and explaining actionable next steps.

Joannes: How long does a typical bioinformatics trajectory take, and what influences the turnaround time?

Steven: It varies quite a bit. For standard, well-planned pipelines, the turnaround can be as quick as a few days. However, projects that require significant custom work, adapted workflows, and close communication to meet highly specific research needs, can span across multiple weeks.

Joannes: What types of data do you most frequently analyze, and what are the most common analyses requested?

Steven: As a company with deep roots in sequencing, we frequently handle genomic data (both short- and long-read), as well as RNA-Seq and ribosome profiling data. We are also increasingly working with proteomics mass spectrometry data and now also often combine both into a multi-omics strategy.

When it comes to common analysis requests, we often perform differential analysis to determine the effect of a specific treatment or condition across a full transcriptome, translatome, or proteome. We also frequently perform variant calling to identify structural variants or small nucleotide polymorphisms, as well as methylation profiling to pick up and analyze methylation patterns. Ultimately, if a collaborator needs a custom analysis, we will find a solution for it.

Joannes: Do you ever combine multi-omics data with clinical metadata, for instance, for biomarker discovery?

Steven: Yes, absolutely. Biomarkers can be identified across the transcriptome, translatome, or proteome. It is incredibly valuable to compare our findings with existing biomarkers and link them to available clinical metadata to identify meaningful correlations.

Joannes: Let’s talk about AI. What are the biggest challenges in processing large omics datasets, and how does AI fit into that?

Steven: The biggest challenge isn’t just having massive amounts of data; it’s figuring out what to do with it and extracting the most useful insights. AI can definitely help filter and contextualize this information.

However, AI can still make errors. You absolutely need a trained bioinformatician to verify the output, provide a layer of control, and steer the overall process. AI isn’t just for massive datasets, either. Even with smaller datasets, like count data, AI can help identify up- or down-regulated pathways and build a broader picture by crawling public repositories for comparable conditions.

Joannes: So, you wouldn’t agree that bioinformaticians will be replaced by AI in five years?

Steven: I would not agree with that. AI is already helping bioinformaticians, and that will continue. But there will still be plenty of work for bioinformaticians to drive the analysis and work with the AI.

Joannes: What advice or tips do you have for partners when it comes to generating and handling their data?

Steven: First, partners often underestimate data size. At the end of a project, we deliver a massive amount of data, so you need to be prepared to handle it.

Second, always think upfront about what you want to achieve with the results. Consider your experimental design and ensure you have the right number of replicates for valid statistics. Having a solid foundation is the best way to successfully tackle your research question. We are always happy to consult with collaborators upfront to set the project up for the highest chance of success.

Joannes: Given the volume of data, how can partners optimize data storage on their end?

Steven: Think about storage solutions early on. You can use local hard drives, but virtual cloud platforms like Google Cloud or AWS are highly effective. We frequently assist our collaborators in determining the most financially viable option based on how long they plan to store the data and who they intend to share it with.

Joannes: With constant advancements in the field, how do you see multi-omics evolving in the coming years?

Steven: The real acceleration of multi-omics research is happening right now. We’ve seen significant independent evolutions in proteomics and transcriptomics over the last few years, but now the focus is heavily on combining them. The next few years will be incredibly exciting. It’s a field where OHMX.bio is investing a lot of internal research to stay at the forefront of this multi-omics wave.

Joannes: What is the most satisfying part of your role?

Steven: Solving puzzles. We often receive highly specific, custom questions from partners that require days of deep thought, and sometimes I need to sleep a few nights on it to come up with a fitting solution. However, when in the end, a completely new, optimized custom method tackles the unique problem, it is incredibly rewarding.

Joannes: Finally, if there were one task in your job that you wish AI could completely take over, what would it be?

Steven: Definitely the routine or more tedious tasks. Having AI fully handle things like fixing code bugs or executing standard, routine pipelines would be a great use of the technology!

Joannes: Thank you very much for this interview, Steven.

Steven: It was a pleasure

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