Juliane Weller

This paper develops a pooled paired prime editing screen to generate large-scale deletions across the genome, identifying non-coding regions with fitness effects and demonstrating the feasibility of genome-wide deletion perturbation.

This study uses high-throughput screening to identify small molecules that enhance antibiotic efficacy against multidrug-resistant Gram-negative bacteria.

This paper develops a CRISPR-based method to generate large-scale structural genomic rearrangements in human cells, revealing selection pressures and gene expression impacts of these changes.

The paper uses enhancer scrambling to study a super-enhancer of the OTX2 gene, finding that a single DNase I hypersensitive site predominantly controls its expression.

This paper designs viral diagnostics using a model-based optimization approach, achieving higher sensitivity and specificity across viral genomic variation.

This paper identifies position-specific sequence determinants of base editing efficiency and develops a machine learning model to predict editing outcomes for adenine and cytosine editors.

The paper predicts insertion efficiency of short sequences into the genome using prime editing by analyzing sequence features and developing a machine learning model.

AutoRELACS automates the RELACS ChIP-seq protocol, enabling high-throughput data generation and analysis with quality comparable to manual methods.