Ming Tommy Tang

Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus www.nature.com/articles/s4...

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

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

Understand NGS sequencing files
bioinf.comav.upv.es/courses/seq...

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

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

6. Streamlining Data-Intensive Biology With Workflow Systems dib-lab.github.io/2020-workfl...

5. two papers by Titus Brown [Ten simple rules and a template for creating workflows-as-applications](journals.plos.org/ploscompbio...)

4. discussion on biostars www.biostars.org/p/115745/

3. A review of bioinformatic pipeline frameworks academic.oup.com/bib/article...

Existing Workflow systems Repository for the CWL standards. Use https://cwl.discourse.group/ for support 😊 - common-workflow-language/common-workflow-language

2. see also from the CWL wiki github.com/common-work...

GitHub - pditommaso/awesome-pipeline: A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin - pditommaso/awesome-pipeline

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 github.com/pditommaso/...

Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment www.nature.com/articles/s4...

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

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

7. Research Software Engineering with Python
alan-turing-institute.github.io/rse-course/...

R version
rse-book.github.io/intro.html

6. Another Book on Data Science www.anotherbookondatascience.com/ compare R and python side by side

5. Feature Engineering and Selection: A Practical Approach for Predictive Models bookdown.org/max/FES/

Tidy Modeling with R The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This book provides a thorough introduction to how to use tidymodels, and an outline of good methodology and statistical practice for phases of the modeling process.

4. Tidy Modeling with R www.tmwr.org/

3. Agile Data Science with R edwinth.github.io/ADSwR/index...

2. Introduction to Data Science rafalab.dfci.harvard.edu/dsbook/ by the almighty Rafa!

7 FREE Books to learn data science 🧵 👇 (not just bookmark, do read them)

1. Data science: A first introduction datasciencebook.ca/

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

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

Has AI changed the course of Drug Development? What’s the drug development process? Has AI changed the course of Drug Development? To answer this question, we need first to understand the drug development …

AlphaFold is a remarkable achievement. It deserved the Nobel Prize.

But confusing "we can predict protein structures" with "we solved drug discovery" is a misunderstanding of where the hard problems actually are.

I wrote more about this here:
divingintogeneticsandgenomics.com/post/has-ai...

- Data silos. Pharma companies don't share failed trial data.
- Biology we don't understand yet.

None of these are solved by better structure prediction.

The real bottlenecks in drug development:

- Target validation. Is this protein actually the right one to go after?
- Clinical trials. Does the drug work in actual patients?

As Derek Lowe put it: "It is very, very rare for knowledge of a protein's structure to be any sort of rate-limiting step" in a drug project.

And here's what most people miss in the "AlphaFold solved biology" narrative:

Structure prediction was rarely the rate-limiting step in drug discovery.

There's also the induced fit problem.

When a drug binds, it can shift side chains and rearrange the binding pocket. The protein adapts to the ligand.

AlphaFold gives you the apo (unbound) structure, which may look different from the drug-bound form you actually care about.

It was never trained on protein-ligand complexes.

Cryptic pockets are some of the most interesting drug targets out there, and we can't predict them from sequence alone.

Small molecules can force a protein to open a pocket that doesn't exist in the "resting" structure.

These are called cryptic pockets.
They're absent in crystal structures and invisible to AlphaFold because AlphaFold doesn't know about ligands.

AlphaFold predicts static structures. One snapshot. One conformation.

But proteins are not static. They breathe. They flex. They shift shape depending on temperature, pH, cellular location, and what's binding to them.

A crystal structure is a photo. Biology is a movie.

Even with AlphaFold, protein structure is not a solved problem.

And protein structure was never the bottleneck for drug development anyway.

Let me explain.