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Proteomics is accelerating. Can our current tools keep up?
Proteomics is on the verge of a transformation. The depth and scale of experiments have expanded dramatically, yet the way we analyse this data often feels stuck in the past. Research teams across industry and academia are generating more proteomics data than ever before, and they’re also hitting the same roadblocks:
- Scaling analyses beyond a few datasets feels impossible without custom engineering.
- Reproducing results is a struggle, with workflows often tied to individuals rather than systems.
- Every new dataset requires ‘just a little’ tweaking, creating bottlenecks that slow discovery.
This isn’t simply an inconvenience, I believe it’s a fundamental challenge and bottleneck for the future of biomarker discovery and drug development. The ability to process and interpret proteomics data should not be the rate-limiting step in scientific progress. Yet, for a large number of proteomics teams, it still is.
Are we reinventing the wheel?
For decades, the default approach to bioinformatics has been do-it-yourself. A talented scientist builds a set of scripts, assembles an analysis pipeline, and—after weeks (or months) of work—the analysis is deemed complete. But science doesn’t stand still. A new batch of samples arrives, an unexpected trend in the metadata emerges, or a deeper understanding of technical variation prompts a re-evaluation. Suddenly, what was once a well-built and thoroughly thought-out solution becomes fragile, no longer aligning with the evolving experiment. Each new iteration demands manual intervention, tweaks, and workarounds—transforming a once-powerful pipeline into a bottleneck. Instead of accelerating discovery, the workflow itself becomes a burden, locking research teams into an endless cycle of maintenance rather than progress. Compounding the challenge, scripts are often tightly tied to the scientists who develop them, embedding critical expertise within individuals rather than the system. When those scientists move on to new roles, their knowledge and context go with them, further slowing progress and increasing the risk of errors.
I know because I have lived it. And over the last decade in particular, I have observed the same challenge play out across hundreds of organizations, regardless of their team size, location, or whether they operate in an academic or industry setting. In many cases, this isn’t just inertia; it stems from necessity. Scientists and data scientists require flexibility to adapt, refine, and compare cutting-edge methodologies, ensuring the most effective analytical approaches are identified for specific workflows. This agility is critical for research, where iterative improvement and methodological exploration drive innovation.
However, once a pipeline is validated and adopted for routine use, its ad hoc nature becomes a limitation. Workflows designed for experimental flexibility often fail to scale, lacking the robustness required for industrial processes, where reproducibility, efficiency, and integration across teams are paramount. What was once an essential research tool now becomes a bottleneck, preventing the transition to high-throughput, scalable solutions needed for drug discovery, biomarker validation, and beyond.
Other data-intensive fields have already navigated similar challenges and adopted scalable solutions. In finance, real-time automated systems process billions of transactions daily with precision and security. The healthcare sector has transitioned from fragmented patient records to interoperable, now utilizing AI-enhanced clinical decision-making platforms that improve efficiency and reduce error. Yet, proteomics, despite its increasing role in drug discovery and biomarker development, continues to rely on workflows that are inherently fragile, labor-intensive, and prone to disruption.
The potential for automation and AI in proteomics is undeniable, but the field is not yet positioned to take full advantage of these advancements. Unlike in finance, logistics, or enterprise data systems, where AI seamlessly integrates with existing frameworks, proteomics still operates within fragmented, manually intensive workflows that resist scaling. The challenge is not just about adopting AI but about building the right architectures to support it. Without scalable, standardized data infrastructure, AI remains an abstract promise rather than a practical tool for proteomics.
As Aaron Levie, CEO of Box, recently noted in a Y Combinator discussion, “If we could use AI to automate more, we could build more. If we could build more, we could lower the cost of things. If we can lower the cost of things, we can actually lift up anybody’s lifestyle.” The lesson here is clear: automation and scalable infrastructure are the foundation upon which AI can drive transformation. Other data-driven industries have already embraced this shift, yet scientific research—and proteomics in particular—continues to rely on bespoke, manually operated pipelines that require continual intervention.
The real question is not just whether proteomics needs to evolve, but whether we are ready to build the systems that will make this evolution possible.
The shift we need
It’s time to evolve proteomics analysis workflows toward a more scalable, researcher-friendly, and AI-ready future, one that empowers scientists today while laying the foundation for seamless integration with future advancements in automation and intelligence. By embracing standardized, interoperable, and intelligent systems, we can create robust solutions that empower scientists to focus on biological insights rather than technical hurdles. This approach enhances flexibility, ensuring that researchers have the tools they need while streamlining workflows for greater efficiency and impact.
Imagine a world where:
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Data processing is seamless and efficient, freeing scientists to focus on discovery rather than troubleshooting scripts.
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Experimentalists and bioinformaticians collaborate effortlessly, leveraging robust, validated analytics pipelines that accelerate insights.
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Workflows are designed to evolve and scale, adapting to new datasets without breaking, ensuring continuous progress and reproducibility.
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Infrastructure is AI-ready, enabling automation and machine learning to enhance discovery without requiring teams to overhaul their existing workflows.
The life sciences have already seen this shift happen in other domains. Genomics moved from manual data handling to scalable, cloud-based platforms that accelerated discoveries and improved reproducibility. Proteomics is next.
Where do we go from here?
The question isn’t whether proteomics needs to evolve—it’s whether we’re ready to embrace the shift so that the true power of proteomics can be unlocked. Forward-thinking teams are already moving toward scalable, collaborative, and future-proofed systems. The challenge is no longer if we should move beyond fragile workflows, but how quickly we can make it happen.
The future of proteomics isn’t about incremental improvements to legacy approaches. It’s about rethinking how we structure data analysis altogether. The tools exist. The need is clear. The only question is: Are we ready to leave outdated workflows behind?
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This blog post was produced by Assoc. Prof. Andrew Webb using a combination of original notes from discussions and insights. Final compilation was completed with assistance from ChatGPT. Any errors or omissions are unintentional, and the content is provided for informational purposes only. The views, thoughts, and opinions expressed in this text belong solely to the author, and not necessarily to the author's employers, organization, committees or other group or individual
