Recent Posts
Social•Jan 25, 2026
Billionaire Sid's Cancer Escape Highlights Access Inequality
Fantastic read. How a billionaire saved himself (for now) from cancer. Not everyone has resources like Sid. https://t.co/73Oj3KF4RP This imposes a bigger question on how we can bring therapeutics to every patient. https://t.co/NyekRQUoND
By Ming Tang
Social•Jan 25, 2026
Batch Effects Create False Cross‑Chromosome Variant Signals
Batch Effects Are Hiding in Your Variant Calls I thought my QC was solid. Then I found thousands of "variants" that weren't real. The signal? Variants on different chromosomes showing linkage disequilibrium. That's impossible in real biology. https://t.co/QK4BVSpbsh
By Ming Tang
Social•Jan 23, 2026
TSniffer Enables Unbiased De Novo RNA Editing Detection
TSniffer: unbiased de novo identification of RNA editing sites and quantification of editing activity in RNA-seq data https://t.co/uQRXdipF88 https://t.co/yeOxSvxrl2
By Ming Tang
Social•Jan 23, 2026
Learning Skills Turns AI Advice Into Useful RNA‑seq Tools
Claude keeps suggesting outdated tools for my RNA-seq analysis. Then I learned about skills. Now it actually helps instead of creating work. https://t.co/yiFbouglo2
By Ming Tang
Social•Jan 23, 2026
Sequencing Artifacts Masquerade as Rare Disease Mutations
1/ Your genome report says you have a disease-causing mutation. Reanalysis 13 months later: it was a sequencing artifact. MedSeq found 164 "rare variants" appeared in >10% of their patients. Population databases missed them all. https://t.co/S0hc1TRr18
By Ming Tang
Social•Jan 22, 2026
KNN in scRNA‑seq: More Art than Science
Everyone talks about KNN (K nearest Neighbor) like it’s a simple algorithm. But in practice, especially in single-cell RNA-seq—it’s art, not science. https://t.co/BCreDmtKwZ
By Ming Tang
Social•Jan 21, 2026
Highly Cited Cancer AI Models Rarely Reach Patients
1/ Your cancer prediction model has 1,000 citations, but It's never been used on a patient. "Some models are wrong, yours are useless." A Cambridge researcher analyzed why most clinical AI tools die in academic journals instead of helping people. https://t.co/mIXS32qDr3
By Ming Tang
Social•Jan 20, 2026
Mapping Tumor‑Immune Spatial Interactions in Solid Cancers
Decoding the spatial dynamics of tumor and immune cell interactions in solid cancers https://t.co/AHgt77Ee2t https://t.co/1Xxkt45JlD
By Ming Tang
Social•Jan 20, 2026
Use Repetition to Slash Bioinformatics Analysis Time
Are you ready to level up your bioinformatics skills? Let’s talk about repetition—a key concept that can save you hours in real-world data analysis. https://t.co/2Dtc9hz46e
By Ming Tang
Social•Jan 20, 2026
Intronic Reads in 10x 3′ UMI Data Explained
You’re analyzing 10x Genomics single-cell RNA-seq and notice lots of intronic reads. Wait—wasn’t this a 3′ UMI-based assay for mature mRNA? Let’s unpack why introns show up—and why they matter. 🧵 https://t.co/cDeb8dfLAS
By Ming Tang
Social•Jan 19, 2026
Study Finds Frequent BRCA1 Test Errors, Risking Unnecessary Surgeries
1/ Women scheduled surgery after being told they had rare BRCA1 variants. The genetic test was wrong. University of Exeter analyzed 50,000 samples to find out how often this happens. The results should worry anyone who's downloaded their 23andMe raw data. https://t.co/F1QALvi4H4
By Ming Tang
Social•Jan 18, 2026
Stop Wasting Hours Matching Sample IDs Across Assays
1/ How many hours do bioinformaticians lose matching sample IDs across assays? Too many. And it’s avoidable. Let’s talk about why this happens—and how to stop it. https://t.co/ZIcF1bUzFF
By Ming Tang
Social•Jan 17, 2026
DUD‑E Bias Inflates Deep‑learning Virtual Screening Results
Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening https://t.co/NEAe3zdQrq
By Ming Tang
Social•Jan 17, 2026
T‑bet+ CD8 T Cells Predict Neoadjuvant PD‑1 Response
Antigen-specific profiling identifies T-bet+ melanoma-specific CD8+ T cells associated with response to neoadjuvant PD-1 blockade https://t.co/VSwMpdqjqO https://t.co/DXc8P28oIZ
By Ming Tang
Social•Jan 17, 2026
One Year In, Code Works—Beware Self‑Deception
1/ A year into bioinformatics, your code starts to work. But that’s also when it gets dangerous. Because now you can fool yourself. https://t.co/7QCMSpI30A
By Ming Tang
Social•Jan 17, 2026
SIAE Variant Linked to Autoimmunity Lacks Control Evidence
1/ Eight patients had a genetic variant. Zero controls did. That 2010 Nature paper claimed SIAE variants increased autoimmune risk 8-fold. The mouse data was clean. The functional assays checked out. https://t.co/tFKXjXEr1y
By Ming Tang
Social•Jan 16, 2026
Longevity Gene Claim Debunked: Study Proves Wrong
1/ A 2010 Science paper claimed they'd found genetic variants strongly tied to living past 100. New York Times covered it. Media went wild. One problem: The results were wrong.
By Ming Tang
Social•Jan 16, 2026
Bioinformatics Blends Code with Intuition for Insight
1/ Bioinformatics isn't just code. Intuition plays an important role too. You run the stats, but you feel when something’s wrong. That feeling is a clue. https://t.co/pV5SxvYuFL
By Ming Tang
Social•Jan 15, 2026
Apply NMF to Single‑Cell RNA‑seq with Our Tutorial
Non-negative matrix factorization is a commonly used technique in genomics data analysis. Read my tutorial on how you can use it for single-cell RNAseq data https://t.co/2SA1JdLfkT https://t.co/6kI1gcqyOe
By Ming Tang
Social•Jan 15, 2026
Twitter Transformed My Bioinformatics Career and Keeps Me Current
Bioinformatics is a fast-moving field, how to stay current? 👇 The answers are different in different times. I read Stephen's post around 2012 and I hopped on Twitter; followed a bunch of bioinformaticians, Journals and professors. It changed my career trajectory. https://t.co/QYV5vKFQgP
By Ming Tang
Social•Jan 15, 2026
AI Explains Single‑cell Clustering, Not Just Runs It
1/ I've analyzed dozens of single-cell datasets. I still google "Seurat clustering parameters" every single time. Last week I tried something different. I asked Claude Code to explain my clustering results instead of just generating them. https://t.co/AMQpheqhYC
By Ming Tang
Social•Jan 14, 2026
Using Claude Code Wrong? Real Genomics Workflow Works
1/ Bioinformaticians get mediocre results with Claude Code and blame the tool. You are doing it wrong. Here's what actually works for genomics analysis: https://t.co/GDOq7SPzla
By Ming Tang
Social•Jan 12, 2026
Six Essential Bioinformatics Workflow Tools Simplify Analysis
6 links on workflow to make your life easier 🧵 Bioinformatics analysis involves a lot of steps, 6 links on workflow to make your life easier: 1. over hundreds of workflow tools and engines https://t.co/R29TTEYSMB
By Ming Tang
Social•Jan 12, 2026
AI Rebuilt My RNA‑seq Pipeline in Minutes
1/ I wasted hours debugging an RNA-seq pipeline. The next day, I rebuilt it in 45 minutes using Claude Code.
By Ming Tang
Social•Jan 11, 2026
Free Tutorial: From FASTQ to GSEA in Bulk RNA‑seq
Free tutorial: bulk RNAseq analysis from fastq to GSEA analysis (watch the full playlist) https://t.co/v0UHRSqJ63 https://t.co/LtdxPNYG5Y
By Ming Tang
Social•Jan 11, 2026
Resumes Boast AI Bioinformatics, but Real Expertise Missing
Looking at bioinformaticians’ profiles these days, you'd think everyone has decades of experience in cutting-edge single-cell and AI-driven bioinformatics. But something’s missing… 👇 https://t.co/yvmvcwXWyr
By Ming Tang
Social•Jan 10, 2026
Massive Single‑cell Data: Deeper Insight or Just Bigger Counts?
1/ Another single-cell study drops. 500,000 cells sequenced. More UMAP plots. More clusters. But here’s the question: Are we learning more—or just counting better? 🧵 https://t.co/nuYRT0GTAq
By Ming Tang
Social•Jan 10, 2026
Claude Code Boosts Bioinformatics Efficiency Tenfold
learn how to use Claude Code. It's changing how I am working as a bioinformatician. If you know what you want, it 10x my efficiency. It is scary to see its power. but like any technology, it will cause disruption. We just need to...
By Ming Tang
Social•Jan 10, 2026
Sequencing Depth Drives Artifacts, Confounds scRNA‑seq Analyses
1/ Per-cell sequencing depth is a major technical effect in scRNA-seq. Different depths change what the data looks like and create artifacts that propagate into clustering, DE, and downstream modeling. And depth heterogeneity itself becomes the signal your methods pick up. https://t.co/RlAD1ONGVX
By Ming Tang
Social•Jan 9, 2026
Linking DNA Mutations to Gene Expression in Single Cells
Genotype-to-phenotype mapping of somatic clonal mosaicism via single-cell co-capture of DNA mutations and mRNA transcripts https://t.co/6Lss1ukbvp https://t.co/bGwnmQEYfI
By Ming Tang
Social•Jan 9, 2026
AI Accelerates Answers, but Domain Knowledge Solves Problems
1/ AI tools are useless if you don't know what you're looking for. I use Perplexity for search. But the AI didn't solve my IGV bug - my domain knowledge did. The AI just helped me find the answer faster. https://t.co/zi2e2FbO5O
By Ming Tang
Social•Jan 8, 2026
Dataset in Hg19 Forces Costly Remapping to Hg38
1/ Found the perfect ChIP-seq dataset on GEO. Then saw "hg19" in the methods. Now you need to remap everything to hg38 before you can integrate it with your data. And the authors didn't share their processing pipeline. https://t.co/3Ui4dyEnkb
By Ming Tang
Social•Jan 8, 2026
Bioinformatics Evolves Fast—Static Recipes Become Outdated
1/ Bioinformatics moves fast. If you rely only on recipes from books, you’ll soon find they’re obsolete. Let me show you why. 🧵 https://t.co/aYztybAnOu
By Ming Tang
Social•Jan 7, 2026
Perturb-Seq Maps T Cell Regulators and Immune Traits
Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits https://t.co/tMc4efSMxe https://t.co/XUdax0qxn1
By Ming Tang
Social•Jan 7, 2026
Small P-Values Aren't Always Biologically Meaningful in Bioinformatics
1/ Bioinformatics is NOT just statistics. The p-value is small, but is it biologically meaningful? Let’s talk. 🧵 https://t.co/iRgiOXPR8v
By Ming Tang