Andreas Gschwind

Computational Biologist at @Stanford.

[Andreas Gschwind @argschwind]

The correct link for the model is: https://t.co/pcRgicDk0W

[Andreas Gschwind @argschwind]

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👇

[Andreas Gschwind @argschwind]

The predictions for 352 ENCODE biosamples can be found on https://t.co/9lub3k4EFr, and the
model is available at: https://t.co/Dj87pNwiOL

All benchmarking pipelines are available on github:
- https://t.co/XFYg7EvJlx
- https://t.co/7Z1wSc1Toa
- https://t.co/DrAZs7AZLQ

15/n

[Andreas Gschwind @argschwind]

@vedranfranke Here we either use cell-type specific Hi-C (ENCODE-Hi-C) or average Hi-C contact frequency across ~35 cell types.

Would be exciting to benchmark other ways to estimate 3D contact.

[Andreas Gschwind @argschwind]

@lpachter @ENCODE_NIH Sorry about that, should be public now.

[Andreas Gschwind @argschwind]

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!

17/17

[Andreas Gschwind @argschwind]

Summary: We present a new approach to map genome-wide enhancer-gene regulatory interactions, show how to link variants to genes, and interpret the model to learn about enhancer regulation. Our framework provides a resource for building future, increasingly accurate models.

16/n