Bioinformatics: The invisible work in the shell

In bioinformatics, most of the real work happens in the shell: aligning reads, parsing FASTQs, fixing scripts at 2 am. But none of that survives. Papers capture figures, not the commands that created them. Lab notebooks rarely include the actual terminal history. We talk about reproducibility, but if the shell work disappears, what are we actually reproducing? Relevant reading: Wilson G. et al. (2017). Good Enough Practices in Scientific Computing. PLOS Computational Biology

Fun fact: The average human genome has ~3 billion base pairs. But ask any bioinformatician, and they’ll tell you, the real challenge isn’t the billions of bases, it’s the one missing comma in your script that crashes the entire pipeline. Bioinformatics is basically the art of: Teaching computers to understand biology Convincing biologists that computers don’t hate them And making peace with error logs that are longer than your thesis. 😅 At the end of the day, it’s not just about sequences, it’s about finding patterns, meaning, and insights that drive real-world discoveries in healthcare, drug design, and personalized medicine. And yes, we still secretly celebrate when the code runs on the first try.

I bet you surely faced this....! Navigating the Frontiers of Bioinformatics: The Real-World Challenges of Protein-Protein Docking. As bioinformaticians, we leverage code and algorithms to decode the secrets of life. A cornerstone of this work is protein-protein docking, predicting how two proteins will bind. It's a journey filled with complex challenges that demand both technical skill and biological understanding. Here are some key hurdles we face: Structural Data: Crucial protein structures aren't always available, when studying unrevealed protein. While tools like AlphaFold are game-changers, we must still distinguish between predicted and experimental structures. Software Accessibility: Powerful simulation software often comes with prohibitive costs, limiting access for many. Accessible, high-performance open-source tools are a critical need. Scoring Variability: Docking runs generate thousands of poses. Inconsistent scoring functions make it difficult to confidently identify the correct "native" interaction without extensive validation. Computational Load: Incorporating flexibility and MD simulations requires immense processing power, often necessitating HPC clusters. This is a fundamental challenge that dictates research feasibility. Despite these hurdles, pursuing an understanding of protein-protein interactions remains a most exciting area of science. Each problem brings us closer to breakthroughs that could redefine medicine. What are some of the biggest challenges you've faced? #Bioinformatics #ComputationalBiology #ProteinDocking #StructuralBiology #DrugDiscovery #Science #Innovation #Technology #DataScience #LifeSciences

Navigating the Frontiers of Bioinformatics: The Real-World Challenges of Protein-Protein Docking Protein-protein docking is at the heart of computational drug discovery — but it’s far from easy. #Bioinformatics #ComputationalBiology #DrugDiscovery #ProteinDocking #AIinBiotech #StructuralBiology DeepMind Schrödinger CureVac Novartis Roche Pfizer Genentech Biogen

Bioinformatics: The Catalyst Transforming Biology Through Big Data Biology has officially entered the data-driven era. With affordable genome sequencing and ever-evolving AI tools, bioinformatics is pushing the boundaries of what’s possible in science and healthcare. Unprecedented Data Growth Technologies like next-generation sequencing, mass spectrometry, and high-throughput imaging are producing complex datasets—spanning genomics, proteomics, and beyond—outpacing traditional analysis methods and demanding innovative computational solutions. Bridging Biology and Data Science Bioinformatics fuses biology, computer science, statistics, and data engineering—empowering researchers to extract meaningful insights and model complex biological systems at scale. Revolutionizing Fields Across the Spectrum From precision medicine and accelerated drug discovery to integrative systems biology, ecological monitoring, and pandemic surveillance—bioinformatics is reshaping multiple disciplines. Stunning Market Growth Reflects Real Impact The global bioinformatics market is booming—valued at USD 25.8 billion in 2024, with projections soaring to nearly USD 95 billion by 2032 (CAGR ~16.9%). Fortune Business Insights This blog offers a clear walkthrough of how bioinformatics is transforming research and industry, ideal for professionals, academics, and lifelong learners seeking to understand life sciences in the big data age. Read it here: https://lnkd.in/duYy4jzK #Bioinformatics #BigData #AIinBiotech #PrecisionMedicine #TechInBiology #LifeSciences

✨What is bioinformatics? Bioinformatics is the application of information technology to biology. It deals with the creation and advance of database, algorithms, computational and statistical techniques, and to solve problems arising from the management and analysis of biological data. Computational tools and methods are used to manage, analyze and manipulate, deluge of biological data. It is interdisciplinary and uses the techniques and concepts of informatics, statistics, mathematics, chemistry, physics, molecular biology, genetics linguistics and artificial intelligence. In bioinformatics, the data stored in databases. A biological database is a bank of organisation and persistence biological data, usually associated with computer software, designed to update, query and retrieve. #Bioinformatics #Biotechnology #Python #Selflearner

NEW: A Rice team aims to explore a way to ditch the silicon and create computers entirely from single-celled organisms. This is the emerging science of synthetic biology. Elizabeth Rayne reports: #computer #computers #biology #biosciences #syntheticbiology #science

On every bioinformatics projects I’ve worked with, I see two kinds of scientists: 1️⃣ Those who want to do the analysis themselves. They’re not trained bioinformaticians, but they’ll roll up their sleeves, learn the tools, and take responsibility for the results. With the right support and reproducible pipelines, they thrive. 2️⃣ Those who never want to touch the stats, pipelines, or code. They’d rather focus on experiments, hypotheses, and biology - leaving the computational work to specialists. Neither type is “better.” Both are essential. But supporting them requires very different approaches. For the first group, you need dependable, reproducible tools they can run confidently on their own. For the second, you need reliable infrastructure and experts who can deliver reproducible results they can trust. That’s what I carried from my time at UMass into building Via Scientific: reproducibility is the common ground. It doesn’t matter which type of scientist you are - reproducible pipelines make the science stronger for everyone.

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When being a “full-stack bioinformatician” stops being a strength… In bioinformatics, wearing many hats can feel like a superpower – until it starts slowing the science. Every Docker build, every cluster tweak, every edge-case rerun pulls time away from the real goal: chasing biology. The truth? Doing less often delivers more. In his latest piece, our founder Alper Kucukural shows why specialization – not generalization – moves science forward, and how the right infrastructure frees computational biologists to focus on what only they can do. 👉 Read the full article: The Many Hats Problem in Bioinformatics: Why Doing Less Can Deliver More https://hubs.li/Q03HYCym0 #Bioinformatics #ComputationalBiology #DataScience #LifeSciences #Biotech #ScientificComputing #TeamScience #ViaScientific #ViaFoundry