Faculty & Head of Data Science at Pancreatic Cancer Hale Center @DanaFarber @Harvard | PhD @eth | Genomics, immunology, deep learning |🇷🇴🇸🇪🇨🇭🇺🇸
Oct 31 • 38 tweets • 12 min read
New: the monthly roller-coaster through October’s coolest life science papers is here 🚀🧬
3-sentence summaries of papers on evolution, single cell methodologies, genetic screens & more.
And, only for October, an educational video on fighting cancer🤺 as a bonus.
Enjoy 3x10! 1. Assembly theory (Sharma et al., Nature)
The most (in)famous paper I read this month proposes a new framework (assembly theory, that is) to explain basically everything… or, more specifically, “to unify descriptions of evolutionary selection across physics and biology” 1/3
Sep 22 • 41 tweets • 11 min read
The human genome is gradually unravelling its secrets 🎁
AlphaMissense model @ScienceMagazine: one more path lit up by deep learning in exploring the code of life 🧬
We now know with high confidence if 89% of ALL missense variants are benign or pathogenic
First things first:
Missense variants = genetic variants (i.e DNA bases) that change the amino acid sequence (i.e groups of 3 bases, building blocks of proteins) in proteins.
Missense variants are more important than non-missense ones, as more likely to have functional impact.
Jul 21 • 19 tweets • 7 min read
3 amazing papers just out @Nature, the kind worth giving up sleep for🦉
The paper I am sharing today is a thoughtful philosophical perspective from @sdomcke & @JShendure proposing a new organizational framework for single cell data, as an alternative to e.g Human Cell Atlas
Compelling read for both lovers❤️ & skeptics🤔 of single cell genomics
This thread is organized as follows:
1️⃣ The need to organize Biology
2️⃣ How to organize cell types?
3️⃣ A consensus ontology
4️⃣ Structure & representation of the cell reference tree
5️⃣ Resolution of tree labels
6️⃣ Example tree
7️⃣ Human tree
Mar 24 • 25 tweets • 10 min read
Do you need to analyze Spatial Transcriptomics data, but are lost in the endless sea of methods?
Here's an explainer of the new @NatureComms paper benchmarking 18 spatial cellular deconvolution methods🧵🧵
Glioblastoma (GBM) is one of the deadliest, most aggressive cancers that exist, with a median survival of only 15 months.
In GBM, 'single cell heterogeneity' are not simply buzzwords.
Rather, this immense heterogeneity is a main reason of treatment failure
Feb 27 • 17 tweets • 6 min read
I need to raise awareness about an important point in #scRNAseq data analysis, which, in my opinion, is not acknowledged enough:
‼️In practice, most cell type assignment methods will fail on totally novel cell types. Biological/expert curation is necessary!
Here's one example👇
Last year, together with @LabPolyak@harvardmed, we published a study in which we did something totally awesome: we experimentally showed how a TGFBR1 inhibitor drug 💊 prevents breast tumor initiation in two different rat models!
Generating brand new functional proteins from scratch with large language models (e.g. #chatGPT)
Let’s understand this Transformers model used for protein design, how well it works & why this is important🧵👇
The very nice paper discussed in this thread comes from a team led by @nikhil_ai at Salesforce @SFResearch 👏