What is Paired Ends?
Paired ends is a term from genomics: two DNA sequencing reads from opposite ends of the same DNA fragment, mapped back together to reveal structure you couldn’t see from either read alone. That’s a decent description of what I’m trying to do here: take ideas from genomics, statistics, AI, and data science and see what they look like when you map them back against each other.
In practice, posts here tend to bioinformatics methods and tools, computational biology papers worth reading, programming in R and Python, applied machine learning, and commentary on how AI is changing research and science broadly.
Where did this come from?
Paired Ends is the continuation of Getting Genetics Done, which I started in 2009 as a place to write about genetics, statistics, and bioinformatics. That blog ran for over a decade and accumulated a lot of readers who, I think, were looking for the same thing I was: practical explanations of methods that actually work, written by someone who uses them.
Who writes this?
I’m Stephen Turner. I’m Associate Professor and Assistant Dean of Research at the University of Virginia School of Data Science, where I build and expand our research infrastructure, work with our faculty on aligning their research interests and expertise to funding opportunities, and facilitate collaborations across UVA, UVA Health, other universities, as well as industry and government partners.
Before coming back to UVA I spent 6 years in industry as principal scientist at Colossal Biosciences, before that as a consultant in the defense and national security communities, and before that I was faculty here at the UVA School of Medicine in public health for 8 years.
My background is in genomics and bioinformatics, but my interests have wandered into applied machine learning, biosecurity, and the ways AI is reshaping scientific practice, for better and sometimes for worse. More about me and contact info at stephenturner.us. You can also find me on Bluesky (@stephenturner.us) and Twitter (@strnr).
Where do I start?
Some posts that represent what this blog is about:
Technical blogging for growth and learning: Why scientists at any career stage should write a technical blog, what to write about, and how to get started.
Learning in Public: A short essay on why writing about what you're learning is valuable, and why doing it publicly makes the learning stick.
Staying Current in Data Science and Computational Biology: The third installment in a series going back to 2012, covering how to keep up with the field in 2026 via RSS, Bluesky, newsletters, and other sources.
Bluesky for Science: A practical guide to finding your scientific community on Bluesky, with starter packs for genomics, bioinformatics, R, and Nextflow.
Python for R users: Resources and advice for experienced R developers who want to get more serious about Python.
Moving from academia to biotech: Honest, practical advice on making the jump from an academic career to industry biotechnology.
Moving from biotech to academia: The companion piece: what it’s actually like to return to academia after spending time in industry.
The Modern R Stack for Production AI: A survey of the R ecosystem for building with LLMs, covering everything from API clients to RAG, evals, and computer vision.
From Protocol to Pipette: What Two New RCTs Tell Us About AI Biosecurity Risk: A review of two rigorous 2026 preprints measuring how much LLMs actually help novices with biological tasks, in silico and in the lab.
Structured AI Integration as Quality Control for Peer Review: The case for using AI as a consistency layer in peer review, drawing on decades of research showing low inter-rater reliability in manuscript and grant evaluation.
I also organize some of my posts under tags. For example, every Friday I write up a short weekly recap of things I read this week, all filed under the Papers tag (even though most of what I’m posting about these days isn’t a paper). I also have tags for topics like biosecurity, AI, R, and others.
Do I need a paid subscription?
No. Everything here is free. You’re welcome to join as a paid subscriber, but you’ll get everything you’d get as a free subscriber, with an extra dose of appreciation and gratitude.
