Parashar Dhapola’s Post

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Lead Bioinformatician and Founder @ Nygen | PhD in Computational Genomics

Sharing some thoughts on the state of academic bioinformatics software development 🧬💻 Have you noticed how bioinformatics tool adoption often follows a "rich get richer" pattern? While we're seeing incredible algorithmic innovations from labs worldwide, there's an interesting reality: a tool's success often depends more on its origin and publication venue than its technical merits. Here's what I'm observing: 1. High-impact journal publication remains the primary "discovery mechanism" for new tools 2. Tools from established labs with recognized "brand names" tend to dominate, regardless of newer alternatives 3. The gap between academic proof-of-concept and production-ready tools remains significant (see attached diagram) This raises important questions for our community: - Should academic developers focus on seeding new ideas or building production-ready tools? - How can smaller labs position their innovative tools for better visibility and adoption? - How should PhD students work closely with ecosystems like Bioconductor, @Bioconda, scverse and nf-core in bridging the academic-production gap? For bioinformatics developers: Consider whether your goal is to introduce a novel method or create a widely-adopted tool. These paths require different strategies and expectations. What's your experience with this? Have you seen promising tools struggle with adoption despite strong methodology? Let's discuss how we can better bridge this gap between academic innovation and production adoption. #Bioinformatics #SoftwareDevelopment #AcademicResearch #DataScience

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HARI PRAKASH

Senior Manager retired at Union Bank of India

1w

Great advice

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