Chatomics! — The Bioinformatics Newsletter
Why Subscribe?✅ Curated by Tommy Tang, a Director of Bioinformatics with 100K+ followers across LinkedIn, X, and YouTube✅ No fluff—just deep insights and working code examples✅ Trusted by grad students, postdocs, and biotech professionals✅ 100% free
You're overthinking your bioinformatics tools
Hello Bioinformatics lovers, Merry Christmas if you celebrate it. If not, Happy New Year! (Most of you should celebrate it). Today, let's talk about choosing bioinformatics tools. Pick a tool and run You're comparing DESeq2 benchmarks for the third week in a row. Your data is still sitting there, untouched. Sound familiar? Here's the uncomfortable truth: You don't need the perfect tool. You just need to start. The real cost of tool paralysis I've watched researchers spend more time debating...
What's your single goal for 2026
Hey bioinformatics lovers, You can't reach a destination without a map. This week's newsletter arrives late because norovirus took down my entire family—kids with stomach bugs, everyone exhausted. But between taking care of kids sessions, I kept thinking: we're closing out 2025. How did that happen? More importantly, what did you actually accomplish this year? Here's the uncomfortable truth: Most of us will hit January 1st with vague intentions ("learn more deep learning," "get better at...
1,000 applicants, 1 job. Here's how to win
Hello Bioinformatics lovers, Tommy here. Welcome, all the new subscribers! Christmas is right around the corner. And I know many of you are in the job market. Here is how to win in a brutal market. Show not tell A single bioinformatics position now gets over 1,000 applications. I’m not exaggerating. Your CV probably looks like everyone else’s: Python, R, RNA-seq, pipelines. The hiring manager sees this 999 more times. Why would they call you? Here’s the truth: they don’t care about skills on...
Why struggling is your secret weapon in bioinformatics
Hello Bioinformatics lovers, Last week, I shared my dream lineup of 10 courses for learning bioinformatics, and the response surprised me. Instead of diving in, many of you asked the same question: "Which one should I start with?" Here's what you need to hear: You're overthinking it. The real learning doesn't happen when you're comparing syllabi or waiting to pick the "optimal" course. It happens when you struggle through your first analysis, when the code breaks, when you can't figure out...
The multi-omics mistake that’s drowning your real signal
Hey Bioinformatics lovers, Tommy here. Thanksgiving is around the corner. If you are thankful to someone, it is a good time to express your appreciation! We will discuss multi-omics integration methods and their pitfalls today. Sometimes, Less is More. You’ve finally got the data. RNA-seq data. Methylation arrays. Proteomics. You’re ready to integrate everything and unlock biological insights that single-omics approaches miss. Then you hit the wall: How do you actually combine these data...
AI ported 6,000+ lines of Python in 8 hours. Here’s the catch
Hello Bioinformatics lovers, AI is eating the world. I have been using it for my bioinformatics coding for a while. I want to share my experiences. Eight hours. That’s how long it took me to port an entire bioinformatics package from Python 2 to Python 3 using AI. Over 6,000 lines of code. Multiple modules. Complex functionality for ChIP-seq and RNA-seq integration. Could I have done it manually? Sure. probably in weeks. But here’s what you really need to know: AI didn’t just save me time. It...
I barely passed linear algebra. Now I use it daily.
Hello Bioinformatics lovers, Tommy again! Can you believe we are at the end of 2025? Focus on the most important things to do so you can achieve what you wanted to do at the beginning of 2025. Today, we will talk about linear algebra. Linear algebra is so useful! I have a confession: I nearly failed linear algebra in college. I scraped by with a D, thought "when will I ever need this?", and promptly forgot everything. Calculus wasn't much better. Fast forward to today. I'm staring at a Seurat...
Your reference genome is lying to you
Hello Bioinformatics lovers, Tommy here. It is Halloween, and I wrote this little story. Hope you enjoy my life lesson as much as my bioinformatics posts :) Today, we will talk about references and other choices you make that may affect your bioinformatics analysis results. So many choices to make before you do the analysis You just picked GRCh38 for your analysis. That one choice—before you’ve written a single line of code— already shapes what variants you’ll find, what genes you’ll miss,...
Why most single-cell annotation benchmarks are missing the point
Hello Bioinformatics lovers, Tommy here. I made a 40-minute video to show you how to do RNAseq analysis end-to-end. Watch it here! I was recently interviewed by Pure Storage: Data Cleaning ‘Janitorial Work’ is Key to Unlocking Life Sciences Breakthroughs Today, we will talk about single-cell cell type annotation. Granular cell type annotation Your model might be accurate — but is it biologically meaningful? Everyone’s benchmarking single-cell annotation models these days. You train on a...
Why KNN Isn’t as “Simple” as Everyone Says (Especially in Single-Cell RNA-seq)
Hello Bioinformatics lovers, Tommy here. KNN looks simple. But in single-cell RNA-seq, it’s an art disguised as an algorithm. Everyone talks about k-nearest neighbors (KNN) like it’s the easiest algorithm in machine learning. In theory, it is: You classify a point based on the majority of its k closest neighbors. Just one hyperparameter—k. What could go wrong? A lot, actually. The Hidden Complexity Choosing k is a balancing act: Small k → low bias, high variance. Large k → high bias, low...
Why Subscribe?✅ Curated by Tommy Tang, a Director of Bioinformatics with 100K+ followers across LinkedIn, X, and YouTube✅ No fluff—just deep insights and working code examples✅ Trusted by grad students, postdocs, and biotech professionals✅ 100% free