Angel Castillo

BSc on genomic sciences @lcgunam | MSc Student @UNAM_mx | 🇲🇽 he/him 🏳️‍🌈 | All opinions are my own :)

[Angel Castillo @angelcaztle13]

@SkyAlertMx En Xochimilco el movimiento fue leve y corto pero completamente perceptible aprox un minuto después de recibir la notificación

[Angel Castillo @angelcaztle13]

@STECDMX Un mes “trabajando” en eso y hasta hace dos o tres días empezaron a poner las columnas. Se veía venir el retraso desde hace semanas y hasta un día antes lo confirman.

[Ming "Tommy" Tang @tangming2005]

🧵"All biology is computational biology."
That sounds bold. But it’s true. Here’s why:
https://t.co/sjibYRKxbM

[Ming "Tommy" Tang @tangming2005]

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.

[ₕₐₘₚₜₒₙ @hamptonism]

Probability Cheat Sheet:

[Itai Yanai @ItaiYanai]

Science is a process of discovering how our minds work.. of figuring out what we're drawn to.. there are things that if I force myself to read, I don't retain very well. And other things, I read the first word and I'm riveted & I just soak it up. – Martin Schwartz on the podcast

[Ming "Tommy" Tang @tangming2005]

single-cell data integration best practice https://t.co/TXYSkEtAOf

[Ming "Tommy" Tang @tangming2005]

🧵 PCA is everywhere in bioinformatics—but did you know it’s just SVD in disguise?

1/ If you've done bioinformatics, you've likely used PCA. But did you know Singular Value Decomposition (SVD) is at its core? Let’s break it down. 👇

[Ming "Tommy" Tang @tangming2005]

Why Documentation is Critical in Bioinformatics 🧵
You think you'll remember what you did 3 months ago? Think again. Poor documentation = wasted time & unreproducible results.

[Ming "Tommy" Tang @tangming2005]

Too many bioinformatics tools, too little time. Stop endlessly bookmarking—just pick one and start analyzing! 🧵👇

[Ming "Tommy" Tang @tangming2005]

🧵 "Why I ignore Nature Methods papers when choosing bioinformatics tools (sometimes) - a practical guide to tool selection..." (1/10)

[Ming "Tommy" Tang @tangming2005]

Speed Up Genomics Data Processing with xargs & parallel
1/ Bioinformatics involves handling huge datasets. But are you processing them efficiently?
Let’s talk about xargs and parallel—two Unix commands that can massively speed up your workflows.

[Ming "Tommy" Tang @tangming2005]

The Hardest Part of Bioinformatics? Naming Files
1/ What’s the most challenging thing in bioinformatics? It’s not coding. It’s naming files.
How many test.txt or foo.txt files do you have? Probably too many. Let’s talk about better file naming practices.

[Prof Lennart Nacke, PhD @acagamic]

Most papers put reviewers to sleep.

Fix your next publication:

• Drop 60% of unecessary discussion, focus on key finding
• End with "Here's why this matters to you"
• Start with conflict, not just background

Data tells.
Story sells.

Even in academia.

(Image: Katrin Wietek)

[Ming "Tommy" Tang @tangming2005]

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data https://t.co/UWZXwGziIy

[Ming "Tommy" Tang @tangming2005]

Machine learning (ML) is revolutionizing genomics, but common pitfalls can lead to misleading results. Here's a thread on how to avoid them 🧵

[Ming "Tommy" Tang @tangming2005]

Why understanding biology matters in bioinformatics
One big lesson: RNA and protein levels aren’t always correlated. If you don’t know this, you might draw the wrong conclusions. 🧵👇

[Tomer Ullman @TomerUllman]

my hopes for gen z taking over the academic review process is that it will be matched for harshness, but at least more efficient

[Itai Yanai @ItaiYanai]

Science is not really happening when the goal is a paper, a presentation, a grant, or any product. It only really happens when the goal is a process of discovery.