Fulai Jin

Genomics; epigenomics; 3D genome; single cell genomics; transcription regulation; CWRU associate professor; PhD from UCLA; USTC Alumni; rt!=support.

[Fulai Jin @FulaiJin]

Particularly, deep-learning methods are more versatile with “low quality” data. We also developed a software suite to help users explore optimal data representation for scHi-C data embedding and to develop novel embedding strategies.

[Fulai Jin @FulaiJin]

Happy to share a new paper from my lab online today on @NatureComms. Thanks to heroic efforts by first author Dylan Plummer and other co-authors. This is a collaboration with Drs. Jing Li and Yan Li @yan_yan_li at @cwru .

https://t.co/gdTRIDTuoi

[Fulai Jin @FulaiJin]

Indeed, the choice of data representation and preprocessing strongly impact the embedding performance. It is also possible to significantly improve an embedding tool simply by changing their data representation.

[Fulai Jin @FulaiJin]

Kudos to all the authors! It was a lot of fun seeking stories from genomic data, then testing them with experiments. Another great collaboration with the laboratory of @yan_yan_li

[Yan @yan_yan_li]

It has been more than 4 years since we started this project. Another great collaboration with the laboratory of @FulaiJin . All three first authors (@chenweng1991 @axg826 , and Shanshan Zhang) are proudly graduated with PhD now.

https://t.co/gggrEaD3u7

[Fulai Jin @FulaiJin]

@hao_wu_7 @ebfabyanic @PengHu_xqq @QiQiu8788 Congratulations!🍾

[Fulai Jin @FulaiJin]

@JianXuLab @UTSWNews @StJude Congratulations Jian!

[Fulai Jin @FulaiJin]

@surbhisona321 @Angela_Ting_PhD @drmjcameron @koyuturkm Congrats！

[Fulai Jin @FulaiJin]

@Jesse_R_Dixon @ZXuSalk Congratulations!

[Fulai Jin @FulaiJin]

@Lovin_Chromatin I choose 1 and the following is a self-promotion.
https://t.co/Bks46S133L

[Fulai Jin @FulaiJin]

@GenomeOrgAus Thank you for the wonderful opportunity to present our work!

[Fulai Jin @FulaiJin]

@wheatwill2 I think so. In the paper we used the same model on both human and mouse data.

[Fulai Jin @FulaiJin]

Our paper is finally online at Nature Genetics. Our goal is to democratize the kb-resolution Hi-C analyses. DeepLoop works very well with sparse 6-cutter, 4-cutter, single cell, or allele-resolved Hi-C data. Let others know if it works for you and let us know if not.

[Nature Genetics @NatureGenet]

✨ PUBLISHED TODAY @NatureGenet
📰 DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution
🧑🏿‍🤝‍🧑🏻 Yan Li, Jing Li, Fulai Jin and team
👇🏼
https://t.co/duRxyb30NJ

[Genome Organisation Australia @GenomeOrgAus]

🚨🚨🚨We are excited to announce the August edition of the GOA Seminar series will be presented by @FulaiJin please register here to join in 👇👇 https://t.co/nOUjI6MyiS

[Fulai Jin @FulaiJin]

@mikelove @uncbiostat congratulations

[Anders Sejr Hansen @Anders_S_Hansen]

(1/n) Excited to share preprint by @ViraatGoel @mileshuseyin

We develop Region-Capture Micro-C (RCMC)

By focusing on specific regions, we generate the deepest 3D genome maps thus far

We see coalescence of enhancer and promoters into “microcompartments”

https://t.co/1Fsd6ixnK9

[Fulai Jin @FulaiJin]

We started this project in early 2018 collaborating with the labs of Drs. Jing Li and Yan Li @yan_yan_li at CWRU. Thank all the contributing authors during this long journey. For a read-only full text, please check https://t.co/ISRAeCKN7y

[Fulai Jin @FulaiJin]

We identified many allele-specific chromatin interactions with DeepLoop and connected them to X-inactivation, imprinting, and heterozygous SVs and SNPs. Many AS-loops rewire promoter enhancers with transcriptional consequences.