In a pair of @biorxivpreprint preprints just posted w/ @sinabooeshaghi and @agalvezmerchan, we describe algorithms for an open data Commons Cell Atlas and demonstrate how a Human Commons Cell Atlas can be used for discovery. https://t.co/jexmXAlbMo https://t.co/snYTmjudE0 1/🧵

🧬 We’re excited to announce that we have sequenced the whole genomes of 500,000 UK Biobank volunteers! 🧬 The dataset is the world’s largest of its kind and we’re proud to make it available to approved researchers around the world 🌍 https://t.co/m2EeGLTr1d #500KGenomes

Excited to share our latest work on bioRxiv! https://t.co/IeJUyi7Cvk A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH! Thread👇

We got an exciting list of speakers for this year's RDM Career's Day. Check out the programme in the link below. Registration is also now open! @RDMOxford

#EASD2023 is off to a fantastic start ! Inspiring Presidential address seeking disease modifying therapies through collaborative academic, industry and translational research 🧬 #Diversity #Inclusion #Kindness

This work has been made possible by fantastic colleagues & mentors: Niels Tolstrup Stephanie Hatch Gareth Ettridge Julia Ast Lozarta Veizaj Carol Sun Lisandra Mendoza Daniel Ebner Alexey Epantchintsev Asmita Agrawal Vanessa Jurtz Enrique Toledo M. Barak Gilboa Carina Ämmälä

We are using #Human_biology #Functional_Genomics #CRISPR and #Machine_Learning to understand the complex relationship between body fat, tissue plasticity and insulin sensitivity !

Obesity is associated with increased risk for insulin resistance, diabetes, and cardiovascular diseases. But have you ever wondered why (and how) obesity related co-morbidities show significant inter-individual variation? To know more listen to our story at the #EASD2023 !

Human subcutaneous and visceral adipocyte atlases uncover classical and specialized adipocytes and depot-specific patterns https://t.co/FcHKlEZYHx

Chandrayaan-3 Mission: 'India🇮🇳, I reached my destination and you too!' : Chandrayaan-3 Chandrayaan-3 has successfully soft-landed on the moon 🌖!. Congratulations, India🇮🇳! #Chandrayaan_3 #Ch3

My "Friends don't like friends make bad graphs" repository reached 800 stars! Thanks everyone. https://t.co/KeSsEcs3DS

I’m excited to share some work I co-lead with @i_rodcast and spanned the labs of @PardisSabeti, @ReillyLikesIt, and @r_tewhey to design and validate synthetic cis-regulatory elements with cell type-specific activity. https://t.co/BqZYaGKwJX 1/n

This looks like 🔥 paper. Using a deep learning approach, the authors claim to have found the sequence rules that govern the transcription initiation in the human genome. Dudnyk et al. bioRxiv https://t.co/OwniRIP4Vd

Now published in Nature Protocols! Congrats @CiminiLab Here's a free read-only link to the paper:

So delighted that our paper, which uses comparative CRISPR screening to identify species differences in human and chimpanzee stem cells, is being published today!

We revealed a secret sibling to human adipocytes! SWAT cells are multipotent, secrete extra cellular matrix and share progenitors with adipocytes. Regardless of brown or white fat origin! Joint journey with @soeren1974 in @NatMetabolism https://t.co/SpwgLLc7Xd

Ever wondered if you could read 100s of metabolites and drugs in parallel using a @illumina or a @10X platform? But metabolites aren’t nucleic acids — how can that work? Check out our new preprint and read on for a thread: https://t.co/0HLfwbz7Gy.

A 🧵 on algorithms for predicting regulatory variant effects to help understand complex disease. IMO an extremely challenging problem that's at a glass mostly full / glass mostly empty point right now depending on who you ask.

New preprint on scGPT: first foundation large language model for single cell biology, pretrained on 10 million cells. Just as text is made of words, cells are characterized by genes Some thoughts on how cool this is & why it challenges the status quo of single cell analysis🧵🧵