Recent Posts
Social•Feb 16, 2026
AI Subtyping Boosts HR+/HER2‑ Breast Cancer Therapy
Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2− breast cancer: The LINUXtrial https://t.co/UF4Lt3g5QK https://t.co/LWuJYD2qGh
By Ming Tang
Social•Feb 16, 2026
Essential Bioinformatics Curriculum: Top Courses to Master
10 courses for my dream bioinformatics curriculum: 1. Unix Commands with Greg Wilson https://t.co/83VHvTqbMt 2. statistics and R with Rafael Irizarry https://t.co/ofYSIiChoW https://t.co/1GogvjAbAA
By Ming Tang
Social•Feb 15, 2026
Squidiff Predicts Cell Development and Perturbation Responses
Nature Methods: Squidiff: predicting cellular development and responses to perturbations using a diffusion model from single cell data https://t.co/MqJxhiRJDD https://t.co/cV0IwwFABZ
By Ming Tang
Social•Feb 15, 2026
Pick a Bioinformatics Tool and Just Start
🧵 Stop searching for the "perfect" bioinformatics tool. You're wasting time. Here's why picking something and moving forward beats endless comparison. https://t.co/cffR3dJaoQ
By Ming Tang
Social•Feb 14, 2026
AI Speeds Biology Discovery, Scientists Still Steer Ethics
AI is not taking over biology, at least not now—it's accelerating discovery, not replacing scientists; humans still ask the questions, validate results, and steer ethical choices. https://t.co/l8KCQGNd4Y
By Ming Tang
Social•Feb 14, 2026
Data Hygiene Precedes AI Success in Biotech
1/ You can't bolt AI onto chaos. In biotech, if your data is a mess, your AI won't save you. Build the data strategy first. Here's how. https://t.co/HM7qddrCsC
By Ming Tang
Social•Feb 13, 2026
CellRank Maps Trajectories From Destructive Single‑cell RNA‑seq
🧵 Single-cell RNA-seq is destructive. You sequence the cells, they're gone. So how do you reconstruct cellular trajectories? Like tracking stem cells as they differentiate? Enter CellRank. https://t.co/HqHnErHYNy
By Ming Tang
Social•Feb 13, 2026
Stay Grounded Amid Rapid Bioinformatics Advances
🧵Bioinformatics evolves fast. New tech. New data. New analysis. But here's how to stay grounded and not get overwhelmed: https://t.co/ZyxcLRExC6
By Ming Tang
Social•Feb 12, 2026
When to Merge Vs. Separate Single-Cell Datasets
🧵 Should you integrate single-cell RNA-seq datasets or not? You've got PBMCs from multiple donors. Merge them—or keep them separate? Let's break it down. https://t.co/s7PmZrvalM
By Ming Tang
Social•Feb 12, 2026
Bioinformatics Demands Matrix Thinking—Most Never Learned It
1/ If you're in bioinformatics, you're staring at matrices all day. RNA-seq? Gene x sample. scRNA-seq? Gene x cell. Everything is a matrix. But I never learned how to think in matrices. And I regret it. https://t.co/ygbY31AfMe
By Ming Tang
Social•Feb 11, 2026
ICE Enables Accurate Senescence Detection From Sparse Single‑Cell Data
ICE: robust detection of cellular senescence from weak single-cell signatures using imputation-based marker refinement https://t.co/PDp6oK5s3W https://t.co/8cPgIQEuID
By Ming Tang
Social•Feb 11, 2026
Boost R Scatter Plots with Scattermore for Million Cells
R is slow in plotting tens of thousands of points. How to speed up for a million cell scRNAseq data? check out scattermore https://t.co/tGPHObuK9S https://t.co/TfsRb5V9Xs
By Ming Tang
Social•Feb 11, 2026
Collaboration Between Bioinformaticians and Wet Biologists Unlocks Science
🧵The most underrated superpower in science: Bioinformaticians and wet biologists working together. Here’s why it matters. https://t.co/YNDqsvyu6A
By Ming Tang
Social•Feb 9, 2026
Computational Biology: Beyond Coding to Data Detective Work
🧵 So You Want to Be a Computational Biologist? 1/ Bioinformatics is more than just coding. It’s data analysis, modeling, pipelines, and detective work. Here’s what you must know. 👇 https://t.co/oJxsWkBYzP
By Ming Tang
Social•Feb 7, 2026
Inspect Your P‑value Histogram After DESeq2 Analysis
How is your p-value histogram look like? chatomics blog post: Downstream of bulk RNAseq: read in salmon output using tximport and then DESeq2 https://t.co/zGjobde9TO https://t.co/aCJaxto5tS
By Ming Tang
Social•Feb 6, 2026
Neutrophil Phenotypes and Spatial Layout Mapped in Colorectal Cancer
Single-cell integration and multi-modal profiling reveals phenotypes and spatial organization of neutrophils in colorectal cancer https://t.co/0FLusa7vuB https://t.co/ETU87KRDXd
By Ming Tang
Social•Feb 6, 2026
AI Tools Like Claude Code Transform Bioinformatics Development
Are bioinformaticians losing their jobs because of AI? After using Claude Code for a couple of months, I'm truly impressed by its ability to write and optimize bioinformatics tools. https://t.co/ojh5bv09fI
By Ming Tang
Social•Feb 6, 2026
Bioinformatics Mastery Stalls Due to Undocumented Tacit Knowledge
1/ Bioinformatics takes years to master. Not because it’s hard. But because so much of what matters… no one writes down. Let me explain https://t.co/EEboWOxB5C
By Ming Tang
Social•Feb 5, 2026
Solid Foundations Beat AI Hype in Bioinformatics
1/ AI won’t save sloppy science. Before you dive into deep learning, master your foundations. Here’s why basic bioinformatics still rules 🧵 https://t.co/nPbaEK3hGf
By Ming Tang
Social•Feb 4, 2026
MAPK‑RUNX1 Axis Controls SMARCA
Spatiotemporal control of SMARCA5 by a MAPK–RUNX1 axis distinguishes mutant KRAS-driven pancreatic malignancy from tissue regeneration https://t.co/eshLHVpD8I https://t.co/mZj4F2qeGp
By Ming Tang
Social•Feb 4, 2026
Curiosity and Mistakes, Not Brilliance, Build Expertise
After 13 years of analyzing sequencing data, I am an "expert" in doing it. I gained those experiences not because I am smarter, but simply because I am curious I made more mistakes, and encountered more problems.
By Ming Tang
Social•Feb 3, 2026
CRISPR/dCas9 Epigenome Editors Show Complex On‑off Target Relationship
Comprehensive profiling of CRISPR/dCas9 epigenome editors indicates a complex link between on and off target effects https://t.co/sEH5GH08Hh
By Ming Tang
Social•Feb 3, 2026
Suspect Mycoplasma Contamination When Human Mapping Fails
Your cell line sequencing data isn’t mapping well? Before blaming the aligner—check if you're sequencing bacteria instead of human. Let’s talk about mycoplasma contamination. https://t.co/y8Ydk0i248
By Ming Tang
Social•Feb 2, 2026
PolyAseqTrap Enables Universal Genome-Wide polyA Site Mapping
PolyAseqTrap: a universal tool for genome-wide identification and quantification of polyadenylation sites from different 3′ end sequencing data https://t.co/pwEf2GeYjb github https://t.co/gpfYhyMoX3 https://t.co/oyA5MBFutE
By Ming Tang
Social•Feb 2, 2026
Master NGS Pre‑Processing: Raw Data Understanding Is Crucial
1/ Many bioinformatics students don’t know much about NGS pre-processing. But trust me, understanding the raw data is essential. Here’s why. https://t.co/WjT1I9haD8
By Ming Tang
Social•Feb 1, 2026
Turn RNA‑seq Dimensionality Chaos Into Clarity
1/Ever feel buried under 20,000 genes and no clue where to start? That’s the curse of dimensionality in RNA‑seq. Let’s turn chaos into clarity. https://t.co/0U3wdzUsMm
By Ming Tang
Social•Feb 1, 2026
Bioinformatics Analysis Time Varies; Depends on Many Factors
“How long will the bioinformatics analysis take?” If you’ve ever been asked that… you know the answer: It depends. Here’s why: 🧵 https://t.co/PG7Xaj3oxf
By Ming Tang
Social•Jan 31, 2026
CHIP in Blood Sequencing Skews Mutation Detection
1/12 What is “CHIP” in blood sequencing, and why does it mess up mutation calls? Not every mutation is created equal. Quick explainer for oncologists, cardiologists, and genomics folks 👇 https://t.co/nBEWTsdlz3
By Ming Tang
Social•Jan 31, 2026
Academic Software's Hidden Risk: Single‑grad Maintainer Dependency
Academic software is a quiet risk in many analysis pipelines. A lot of core tools are maintained by one grad student whose incentives don't include long-term upkeep. https://t.co/ec4R4kDi84
By Ming Tang
Social•Jan 30, 2026
Bioinformatics Needs Nuance, Not Hard Cutoffs
You can not do Bioinformatics with hard cutoffs and thresholds Let me tell you something real: Biology is more about p-values. It’s messier than that. 🧵 https://t.co/iewqWMvU6D
By Ming Tang
Social•Jan 29, 2026
Understanding Login vs Interactive Shells in Bioinformatics
Bioinformatics is more about biology and code. It's shells and sessions, exports and environments. Ever been confused by “login shell” vs “interactive shell”? This thread is for you. 🧵 https://t.co/DfB8BEztiD
By Ming Tang
Social•Jan 28, 2026
Model Failures Stem From Data, Not Algorithm Power
1/ You think your ML model fails because it’s “not powerful enough”? No. It’s your data. Garbage in, garbage out. Here’s what most AI scientists miss when using public RNA-seq or single-cell data 👇 https://t.co/dVezFRLHkW
By Ming Tang
Social•Jan 27, 2026
Immune Synapse Crosstalk Directs T Cell and Dendritic Cell Functions
How crosstalk at the immune synapse shapes T cell and dendritic cell biology https://t.co/DtqWi1PWhU https://t.co/Yw3sO2UgSX
By Ming Tang
Social•Jan 27, 2026
Inherited Messy Bioinformatics Code? Learn Better Practices
You inherit someone else’s bioinformatics code. No comments. No structure. Variable names like x1, foo, temp2. And now it’s your problem. Let’s talk about that experience—and how to do better. https://t.co/RqCDNPwhMD
By Ming Tang