Ming Tommy Tang
Director of bioinformatics at AstraZeneca. subscribe to my youtube channel @chatomics. On my way to helping 1 million people learn bioinformatics. Educator, Biotech, single cell. Also talks about leadership.
tommytang.bio.link
- Scikit-bio: a fundamental Python library for biological omic data analysis www.nature.com/articles/s4...
- I was featured in The Data Wire by Pure Storage. Data challenge comes before the algorithm/AI challenge.
- 1/ AI won’t save sloppy science. Before you dive into deep learning, master your foundations. Here’s why basic bioinformatics still rules 🧵
- 2/ AI is flashy. But the core skills—UNIX, plotting, EDA—are what let you trust your data. Without them? You’re flying blind.
- 3/ UNIX isn’t sexy. But it’ll save your life when you’ve got 100 samples and need to rename, reformat, or reprocess them—fast.
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View full threadI hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics divingintogeneticsandgenomics.ck.page/profile
- Spatiotemporal control of SMARCA5 by a MAPK–RUNX1 axis distinguishes mutant KRAS-driven pancreatic malignancy from tissue regeneration www.nature.com/articles/s4...
- life-changing hack if you collabrate with wet lab scientists.
- 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.
- I feel the same about all those AI advancements (Ralph, OpenClaw, etc.). It's progressing too quickly, and I can't keep up. But stay curious. Experiment, and you'll learn the most by doing.
- I firmly believe in the law of attraction. If you are interested in something, follow people on LinkedIn or X, and you'll consistently receive related content (yes, the algorithm is that effective).
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View full threadI am sharing my hard-learned tips and tricks for bioinformatics here in my chatomics newsletter. Join the other 10K subscribers divingintogeneticsandgenomics.kit.com/profile
- Comprehensive profiling of CRISPR/dCas9 epigenome editors indicates a complex link between on and off target effects link.springer.com/article/10....
- " We demonstrate that multimerization of the catalytic domain of DNA methyltransferase 3A enhances editing potency but also induces widespread, early methylation deposition at low-to-medium methylated promoter-related regions with specific gRNAs and also with non-targeting gRNAs"
- chatomics! new blog post: Understanding prcomp() center and scale Arguments for Single-Cell RNA-seq PCA divingintogeneticsandgenomics.com/post/unders...
- I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics divingintogeneticsandgenomics.ck.page/profile
- 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.
- 1/ Mycoplasma contamination is the dirty little secret of cell culture. If your lab has it, it spreads fast. And once it’s in—it's notoriously hard to eliminate.
- 2/ You don’t always see it. No smell. There’s no cloudiness (you can see some black dots if it becomes really bad) But it messes up your experiments from the inside. Think: slow growth, altered gene expression, and compromised chromatin accessibility.
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View full threadI hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics divingintogeneticsandgenomics.ck.page/profile
- PolyAseqTrap: a universal tool for genome-wide identification and quantification of polyadenylation sites from different 3′ end sequencing data link.springer.com/article/10.... github github.com/APAexplorer...
