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In the research corridors of i3S – Instituto de Investigação e Inovação em Saúde da Universidade do Porto, Portugal's largest health sciences institute, a quiet transformation is taking place. Bruno Cavadas operates the institute's bioinformatics core facility, providing computational support across cancer, neurobiology, and infectious disease research. While single-cell experiments remain relatively rare due to their substantial costs, Cavadas has positioned his platform to maximize the scientific impact of each precious dataset that comes through his door.
Cavadas' journey into bioinformatics began, as he puts it, "by accident." With an integrated master's degree in bioengineering, he initially dabbled in programming during his studies "not heavily, just something that I fancied." Yet what started as casual interest evolved into necessity when, twelve years ago, his research institute's most powerful computer was his personal laptop.
"I would do next generation sequencing alignment on my laptop using external drives," Cavadas recalls. The cluster available had merely 50 gigabytes of storage, forcing him to develop automated scripts that would process genomes sequentially, analyze, delete, move to the next. This creative problem-solving under constraint would prove prophetic for his future role.
The i3S Bioinformatics platform was created in 2022 following external evaluation recommendations. What began as addressing criticisms about lacking bioinformatics facilities has evolved into something far more significant: a democratization of computational biology within one of Europe's premier research institutes.
Operating as a free service for i3S researchers, Cavadas' platform removes financial barriers that often limit experimental scope. "Since they are not paying, they can do more things with the data that would probably be limited if they wanted to do," he explains. This model has created an unexpected dynamic where resource constraints drive innovation rather than limitation.
The platform's success has created its own challenges. Cavadas describes encountering two distinct user types: senior researchers seeking complete analyses without learning curve investment, and PhD students eager to acquire skills but lacking computational resources. This dichotomy reflects a broader challenge in modern biology, the gap between experimental capability and analytical expertise.
"Most of the time, they don't have the resources to run a couple of algorithms," Cavadas notes about the eager learners. "That is where platforms like Nygen can help, because some of them have published results that they want to check." The observation highlights how no-code platforms could address the middle ground between complete dependency and full technical mastery.
Perhaps most striking is Cavadas' approach to staying current despite overwhelming demand. "Sometimes actually I give work to myself," he admits, describing how discovering relevant papers for one project often sparks insights applicable to others. This serendipitous knowledge transfer reading for one researcher and applying findings to another demonstrates how a single expert can serve as a knowledge broker across diverse research domains.
Among his most intriguing current projects involves single-cell analysis on a mouse species lacking reference genome annotations. "How do you do a single cell where you don't know where the genes are located?" The challenge extends beyond technical complexity to fundamental questions of comparative biology. When canonical markers like PTPRC (CD45) aren't annotated, how does one differentiate immune cell populations?
This project exemplifies the frontier challenges that specialized cores encounter problems too niche for standardized platforms yet too complex for individual researchers to tackle alone.
Cavadas observes a recurring pattern: researchers generating "ton of Excel tables with a ton of information" but lacking direction for interpretation. "They just come to me and say, what do I do?" This highlights a crucial distinction between data generation and insight extraction a gap that requires human expertise rather than computational power alone.
His philosophy emphasizes objectivity: "I'm less biased because I'm not expecting to find a specific cluster." This external perspective proves valuable in preventing confirmation bias that can skew biological interpretations.
While acknowledging AI developments, Cavadas remains pragmatically focused on immediate needs. "I haven't used it even for programming or anything else because I prefer to do everything myself," he explains, citing overwhelming workload as a barrier to exploring new technologies.
His vision for platform expansion depends heavily on institutional priorities: whether groups develop internal bioinformatics capacity (shifting his role toward pipeline development) or maintain their current wet-lab focus (requiring continued consulting emphasis).
What emerges from Cavadas' experience is a model of democratized access to sophisticated bioinformatics. By removing cost barriers and providing expert consultation, his one-person platform enables experimental researchers to explore hypotheses that might otherwise remain untested. The approach suggests that strategic investment in bioinformatics infrastructure can amplify research impact far beyond the initial investment.
As research becomes increasingly data-intensive, the i3S model offers a compelling alternative to both fully outsourced bioinformatics and complete in-house capability building. Sometimes, revolution comes not through massive transformation but through the dedicated expertise of one person, armed with little more than creativity, determination, and an external hard drive.
The i3S Bioinformatics Platform exemplifies how strategic core facilities can transform institutional research capacity. For more information about their services and approach, visit their scientific platforms page.
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