The relationship between genome structure and function DOI
A. Marieke Oudelaar, Douglas R. Higgs

Nature Reviews Genetics, Journal Year: 2020, Volume and Issue: 22(3), P. 154 - 168

Published: Nov. 24, 2020

Language: Английский

Transcriptional Addiction in Cancer DOI Creative Commons

James E. Bradner,

Denes Hnisz,

Richard A. Young

et al.

Cell, Journal Year: 2017, Volume and Issue: 168(4), P. 629 - 643

Published: Feb. 1, 2017

Language: Английский

Citations

1028
Long-range enhancer–promoter contacts in gene expression control DOI
Stefan Schoenfelder, Peter Fraser

Nature Reviews Genetics, Journal Year: 2019, Volume and Issue: 20(8), P. 437 - 455

Published: May 13, 2019

Language: Английский

Citations

944
Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation DOI Creative Commons
Jason D. Buenrostro, M. Ryan Corces, Caleb A. Lareau

et al.

Cell, Journal Year: 2018, Volume and Issue: 173(6), P. 1535 - 1548.e16

Published: April 26, 2018

Language: Английский

Citations

637
Structural variation in the 3D genome DOI
Malte Spielmann, Darío G. Lupiáñez, Stefan Mundlos

et al.

Nature Reviews Genetics, Journal Year: 2018, Volume and Issue: 19(7), P. 453 - 467

Published: April 24, 2018

Language: Английский

Citations

633
Enhancer redundancy provides phenotypic robustness in mammalian development DOI
Marco Osterwalder, Iros Barozzi, Virginie Tissières

et al.

Nature, Journal Year: 2018, Volume and Issue: 554(7691), P. 239 - 243

Published: Jan. 31, 2018

Language: Английский

Citations

630
Activity-Regulated Transcription: Bridging the Gap between Neural Activity and Behavior DOI Creative Commons
Ee-Lynn Yap, Michael E. Greenberg

Neuron, Journal Year: 2018, Volume and Issue: 100(2), P. 330 - 348

Published: Oct. 1, 2018

Language: Английский

Citations

556
Sequential regulatory activity prediction across chromosomes with convolutional neural networks DOI Creative Commons
David R. Kelley, Yakir Reshef, Maxwell L. Bileschi

et al.

Genome Research, Journal Year: 2018, Volume and Issue: 28(5), P. 739 - 750

Published: March 27, 2018

Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system predict cell-type–specific epigenetic transcriptional profiles in large mammalian genomes DNA sequence alone. By use of convolutional neural networks, this identifies promoters distal regulatory elements synthesizes their content make effective gene expression predictions. We show that model predictions the influence variants on align well causal underlying eQTLs populations can be useful generating mechanistic hypotheses enable fine mapping disease loci.

Language: Английский

Citations

444
Dynamic interplay between enhancer–promoter topology and gene activity DOI
Hongtao Chen, Michal Levo, Lev Barinov

et al.

Nature Genetics, Journal Year: 2018, Volume and Issue: 50(9), P. 1296 - 1303

Published: July 23, 2018

Language: Английский

Citations

429
Enhancer Reprogramming Promotes Pancreatic Cancer Metastasis DOI Creative Commons
Jae‐Seok Roe, Chang‐Il Hwang, Tim D.D. Somerville

et al.

Cell, Journal Year: 2017, Volume and Issue: 170(5), P. 875 - 888.e20

Published: July 27, 2017

Language: Английский

Citations

417
Visualizing DNA folding and RNA in embryos at single-cell resolution DOI

Leslie J. Mateo,

Sedona E. Murphy, Antonina Hafner

et al.

Nature, Journal Year: 2019, Volume and Issue: 568(7750), P. 49 - 54

Published: March 18, 2019

Language: Английский

Citations

396