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Prikvačeni tweet
I am excited to announce that my lab will launch at Stanford in Spring 2020! I will be an Assistant Professor in Genetics and the Children’s Heart Center. We aim to map the regulatory wiring of the genome to discover mechanisms of heart diseases. http://www.engreitzlab.org 1/pic.twitter.com/18NVMvYRqA
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Jesse Engreitz proslijedio/la je Tweet
Fantastic to see this work out in
@medrxivpreprint led by@d__vuckovic and@ParsaAkbari from@SoranzoTeam, as well as@erik_bao and@CalebLareau from our group. Wonderful collaboration with@nicolesoranzo, the doyenne of blood cell genetics! Great tweetorial from the team!
https://twitter.com/d__vuckovic/status/1224810501416374273 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Proteomics across hundreds of cell lines in CCLE — very interesting analyses of protein-RNA correlation!https://twitter.com/dnusinow/status/1220392307888754688 …
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Jesse Engreitz proslijedio/la je Tweet
1/ Four years ago, our lab set out to predict and control the loss of mesendoderm competence during ectoderm differentiation in human stem cells. Along the way, we showed that we could shape the Waddington landscape (remember intro dev bio?). Thread
https://www.biorxiv.org/content/10.1101/867010v1 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Jesse Engreitz proslijedio/la je Tweet
Ultra-high throughput scRNA-seq from Christoph Bock's lab! Combines combinatorial barcoding with droplet microfluidics to sequence >150,000 cells on a single
@10xGenomics lane:https://www.biorxiv.org/content/10.1101/2019.12.17.879304v1 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Jesse Engreitz proslijedio/la je Tweet
Just updated! Our massive database of PhD fellowships for research anywhere. We have found 117 funding opportunities for graduate students in all fields of research. We provide deadline, dollar amount, link to funder, number of pages, eligibility. https://research.jhu.edu/rdt/funding-opportunities/graduate/ …pic.twitter.com/SIWenYK7ga
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Jesse Engreitz proslijedio/la je Tweet
See theory/comp preprint on model for disordered proteins, led by our Princeton collaborator Thanos, w/ 1st authors Antonia (now fac @ UIUC) and undergrad Helena (now doing PhD @ Harvard). *Beautifully* complex & highly sequence-dependent phase behavior!https://www.biorxiv.org/content/10.1101/864942v1 …
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Jesse Engreitz proslijedio/la je Tweet
After two years on the BioRxiv and now including 2 years of progress, our paper making the case for random DNA genomics is now available! We found all sorts of cool things, but, to me, the most important is the sheer abundance of transcription factor binding sites. thread->https://twitter.com/NatureBiotech/status/1201575470887780352 …
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Fascinating connection between splicing and promoter selectionhttps://twitter.com/ChromatinHaiku/status/1202622270289481728 …
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Thank you
@TeamSciStories for this awesome cover!https://twitter.com/TeamSciStories/status/1201897291390554113?s=20 …
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Congratulations to our awesome ABC team!
@FulcoCharles Joe Nasser@eric_lander@erez@thouis@ice_berg95 @mc_monte_cristo@vidyhere and others And: We are hiring! http://www.engreitzlab.org 10/10Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
ABC also has implications for understanding the 3D genome. Estimating 3D contact as a function of genomic distance predicts CRISPR data nearly as well as using Hi-C. So, contact freq from distance (not CTCF loops, domains, etc.) appear to be key feature for most enhancers. 9/pic.twitter.com/npz0cZGm0X
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ABC maps can help to connect common disease variants to their target genes. Ex: Here is the SORT1 locus from
@kiranmusunuru@skathire. ABC maps in hepatocytes connect to SORT1 — matching eQTL and CRISPR data. (Note though that variant regulates multiple genes!) 8/pic.twitter.com/dNRUMVQlMz
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Now we have a simple way to predict enhancer-gene connections — for any gene in any cell type! At minimum, computing ABC requires ATAC-seq and H3K27ac ChIP-seq. Download the code here: https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction … 7/
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This Activity-by-Contact (ABC) model works remarkably well at predicting our CRISPR data – including in K562 and in other cell types. 6/pic.twitter.com/6bqI5M0fNm
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So, we explored other prediction methods. Our best model was surprisingly simple: Effect of enhancer = Activity (count ATAC+H3K27ac reads) x Contact (count Hi-C reads) This encodes the notion that enhancers act on promoters upon 3D contact, with different strengths. 5/pic.twitter.com/graV2C4Ey4
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With gold-standard CRISPR data in hand, we checked whether any existing models could predict enhancer-gene regulation. In fact, the answer was no! Existing methods (Hi-C loops, contact domains, nearest gene, DHS E-P correlation, and others) all performed poorly. 4/pic.twitter.com/Qn0EqZpA0m
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To answer this, we first experimentally mapped many thousands of potential enhancer-gene connections, using a new method combining CRISPRi with RNA FISH and flow cytometry (CRISPRi-FlowFISH). 3/pic.twitter.com/M8jvq8bS7F
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Enhancers tune gene expression in each cell type in the body. We know enhancers can control multiple genes over long distances, with cell-type specific effects. But, are there simple rules to explain this network of millions of enhancers x 21000 genes x 1000s of cell types? 2/pic.twitter.com/gfklBfEwCx
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I am excited to share our latest work, in which we tackle the question: How can we predict which enhancers regulate which genes in which cell types? https://www.nature.com/articles/s41588-019-0538-0 … Thread
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