Computational Biologist at @Stanford.
Stanford, CA
Joined September 2015
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Andreas Gschwind retweeted
Update: scE2G is now online at @NatureGenetics!
📄 nature.com/articles/s41588-0…
Since the preprint, we added a new benchmark on a fully held-out CRISPR dataset.
scE2G continues to show state-of-the-art performance, reinforcing that it generalizes well
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Introducing scE2G: a new model to link enhancers to target genes using single-cell data.
Excited that scE2G will enable building enhancer maps in hundreds of cell types in the human body!
Wonderful collaboration with @robin_andersson @613weilin @mayayayas and others
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Andreas Gschwind retweeted
How to link a non-coding disease GWAS variant to its target genes in the right cell type?
ENCODE-rE2G, now out in @Nature addresses this through optimal integration of chromatin activity and 3D contact data in 1,458 biosamples.
A huge collaborative effort spanning 6+ years led by @argschwind @jengreitz nature.com/articles/s41586-0…
Our paper is now out in Nature! 🎉
Expanded to 92M+ enhancer–gene links across 1,458 biosamples, with new benchmarks and analyses.
"An encyclopedia of human enhancer–gene regulatory interactions":
nature.com/articles/s41586-0…
Grateful to the whole team of this massive effort!
Excited to share our latest work on bioRxiv!
biorxiv.org/content/10.1101/…
A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH!
Thread👇
Excited to share our latest work on bioRxiv!
biorxiv.org/content/10.1101/…
A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH!
Thread👇
This has been a wonderful community effort involving countless people from the @ENCODE_NIH consortium and beyond. I would like to thank everybody I had the pleasure to work with during this long and exciting journey!
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Our paper is now out in Nature! 🎉
Expanded to 92M+ enhancer–gene links across 1,458 biosamples, with new benchmarks and analyses.
"An encyclopedia of human enhancer–gene regulatory interactions":
nature.com/articles/s41586-0…
Grateful to the whole team of this massive effort!
The predictions for 352 ENCODE biosamples can be found on encodeproject.org, and the
model is available at: github.com/karbalayghareh/EN…
All benchmarking pipelines are available on github:
- github.com/EngreitzLab/CRISP…
- github.com/EngreitzLab/eQTLE…
- github.com/EngreitzLab/ABC-M…
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