Discover and read the best of Twitter Threads about #generegulation

Most recents (6)

Part II of the $CRSP $SGMO comparison will hopefully be a bit less dry now that Part I updates are in place. This thread will focus more on valuation, outlook and why the valuation cycle should matter to investors.
Let's start with the concept of #Disruptive technologies. The originator of disruptive innovation theory, Clayton Christensen worked with HBR in 2015 to revisit the past 20 years.
#GenomicMedicine is disrupting Big Pharma who has begun to respond by spinning off old product lines and jumping into #GeneTherapy.
Read 24 tweets
Please enjoy our new #preprint, "High-throughput discovery and characterization of human transcriptional effectors"! 😀

We tested thousands of protein domains and mutants for the ability to repress/activate #GeneExpression and ID effectors in 600 proteins…
For many human #TranscriptionFactors & #chromatin regulators, we know more about *where* they bind than *how* they regulate expression

To find effector domains in an unbiased way, we tested a library of the majority of nuclear protein domains with our new method, HT-recruit Image
We find hundreds of human #repressor domains, including several domains of unknown function Image
Read 13 tweets
What's up with $SGMO valuation? This thread will try to show some history and the setup for what I still hope to be an outsized move. First worth noting that news will always trump technicals. First here is a month chart I've been using for the company 1/
This being a 10 year chart, it doesn't show how long $SGMO has been around but it does show virtually no trading volume and a 3 yr ceiling on the price as they were in early development stage while trying to get ill-chosen indications into pre/clinic 2/
The first wave $SGMO rally started in 2013 when the ceiling was broken. The company was talking up #GeneEditing #GeneRegulation #HIVcure and #diabetes None were ready but the volume picked up and the first big rally ensued going from around $9 to $20 3/
Read 13 tweets
Our new paper is on biorxiv! "Predicting master transcription factors from pan-cancer expression data"…. We set out to find master TFs in ovarian cancer, and went on quite a journey... A few highlights in the thread 1/
Prioritizing candidate MTFs without ChIP-seq data is tough, so we developed a gene expression-based approach which we loving called the Cancer Core Transcription factor Specificity (CaCTS) algorithm 🌵. (We then decorated the lab in cactuses) /2
Using CaCTS we identified candidate MTFs for 34 major tumor types and 140 molecular/histologic subtypes.

Candidate MTFs tend to be associated with super-enhancers, and many are lineage-specific essential genes - woo CaCTS works! 3/
Read 10 tweets
1/ Excited to share work led by @varshney_arushi… @GeneticsGSA, we compared gene regulatory annotations defined using diverse #epigenomic data across 4 cell types to measure cell specificities and #genetics of #geneRegulation
2/ We observed that stretch and super #enhancers are more cell type-specific whereas HOT regions and broad domains comprise more ubiquitous promoter states
3/ #eQTL in stretch enhancers have significantly smaller effect sizes compared to those in HOT regions. This suggests regulatory buffering where the expression of cell-identity genes is more tightly controlled
Read 6 tweets

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