Nils Krietenstein

This study shows that FACT is essential for chromatin replication and transcription by disrupting and reassembling nucleosomes, preventing genome aggregation.

The paper introduces CLASTER, a deep learning model that predicts nascent transcription levels from chromatin landscape and 3D structure data, achieving high correlation with experimental measurements.

The paper integrates 4D Nucleome data to analyze 3D genome structure and function in human cells, revealing chromatin domains, loops, and interactions, and showing how genetic variants affect genome folding.

The paper introduces Micro-C-ChIP, a method for high-resolution chromatin organization analysis, and applies it to mouse and human cells to reveal promoter-promoter networks and bivalent promoter architecture.

This study creates a single-cell RNA-seq atlas of the mouse seminal vesicle, identifying secretory and immune cell populations and their transcriptomes.

This study investigates enhancer-promoter contact architecture and communication, finding that RNA polymerase II and PARP1 activity stabilize these contacts.

This study evaluates 3C-based assays to understand how experimental parameters affect chromatin interaction detection and quantification, leading to the development of Hi-C 3.0.

This study improves chromosome architecture mapping by using Micro-C to achieve nucleosome-resolution insights in human cells, revealing new looping interactions and structural patterns.