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My Journey as a Bioinformatician in a Proteomics Core Lab

Nivedita Som, THSTI

Two years ago, I stepped into the Proteomics Core Lab at the Translational Health Science and Technology Institute (THSTI) in Faridabad, fresh out of an M.Tech in Computational Biology. I was excited, nervous, and a little overwhelmed. My academic training had prepared me well in bioinformatics and computational biology, but proteomics? That was a whole new world.

Unlike genomics or transcriptomics, which are now standard in many molecular biology labs, proteomics felt niche, complex, and fast-moving. The learning curve? Steep. But the journey? Incredibly rewarding.

So, What Is Proteomics and Why Does It Matter?
If genomics tells us what could happen, proteomics tells us what is happening right now, at the protein level. Proteins are the workhorses of the cell, responsible for nearly every biological process. Proteomics involves the systematic, large-scale study of proteins to provide a comprehensive view of their structure, function, and role in regulating biological systems. It’s essential for understanding disease mechanisms, drug resistance, and cellular signaling pathways. Unlike genomics, proteomics is more complex as protein expression changes over time and in response to environmental conditions.

From Theory to Mass Spec
At the heart of proteomics lies Liquid Chromatography-Mass Spectrometry (LC-MS), a powerful tool for identifying and quantifying proteins in complex samples. When I joined the lab, my understanding of mass spectrometry was strictly theoretical. Fast forward a few months, and I found myself not just learning how the instruments worked, but also assisting with data acquisition and processing the raw output from these complex machines.

Why the Wet Lab Matters - A Lot
Here’s something that often gets overlooked in bioinformatics: the quality of the data we analyze depends entirely on the skill and care taken in the wet lab. From protein sample preparation, extraction, and digestion to chromatography and MS analysis, every step in the experimental workflow influences the final data. If sample prep goes wrong or chromatography isn’t optimized, even the most advanced algorithms won’t save the results. Over time, I’ve come to truly appreciate the collaborative nature of proteomics, where wet-lab precision meets computational analysis.

The Data Challenge
One of the first hurdles I faced was understanding what “raw data” actually means in proteomics. Each mass spectrometer brand Thermo, Sciex, Bruker, and others, generates proprietary file formats like .raw, .wiff, or .d, etc. Before any meaningful analysis can begin, the data needs to be converted, cleaned, normalized, and structured, each step involving specialized tools, settings, and quality checks. Proteomics datasets are enormous, often several gigabytes per run, so efficient data handling, storage, and version control quickly became essential parts of my daily workflow.

Tools, Code, and Going Downstream
Over time, I became proficient in a variety of platforms and programming languages, especially R and Python, for tasks such as data preprocessing and quality control, statistical analysis, visualization, and workflow automation. Then came the downstream bioinformatics, including functional annotation and enrichment analysis, pathway mapping, protein-protein interaction (PPI) networks, and antimicrobial resistance (AMR) protein identification, etc to name a few.

Always a learner
Even after two years in this role, I still feel like a student. Proteomics is evolving rapidly, with constant improvements in mass spectrometry technology, analysis algorithms, and data interpretation strategies. While many great tools exist, there’s still a pressing need for more intuitive, user-friendly platforms, especially for experimental biologists who may not have a strong computational background. As someone working at the intersection of computation and biology, I see huge potential for innovation, developing tools that make proteomics more accessible, scalable, and impactful across diverse research fields.

From Data to Discovery
Working in a proteomics core lab isn’t just about crunching numbers. It’s about translating complex, high-dimensional data into actionable biological insights. Whether it's identifying disease biomarkers, uncovering drug resistance mechanisms, or mapping cellular pathways, proteomics provides a real-time lens into the inner workings of the cell.

As a bioinformatician, I see my role as a translator, bridging the gap between raw data and biological understanding. But this translation is only possible with collaborative teamwork, where computational accuracy and experimental rigor go hand in hand.

Final Thoughts
Looking back, this journey has taught me far more than just technical skills. It’s shown me the value of collaboration, curiosity, and continuous learning. Proteomics is no longer a mystery to me; it’s a powerful language, and I’m grateful to be learning how to speak it fluently.

If you're just starting out in proteomics, be patient, stay curious, and never stop learning. The field is wide open and full of possibilities.