Monika Heinzl

The paper introduces Ledidi, a computational method for designing cis-regulatory elements by optimizing continuous sequences guided by deep learning models, validated experimentally for controlling transcriptional outcomes.

The paper detects ErbB2 mutations in male germline using error-corrected sequencing, finding age-related accumulation and early mutational enrichment in young donors.

The paper uses duplex sequencing to analyze de novo mutations in sperm DNA from older and younger donors, finding higher mutation frequencies and recurrence in older donors.

The paper develops quality control tools to improve duplex sequencing data yields by reducing data loss and enhancing variant calling accuracy.

The paper corrects errors in duplex sequencing data to reunite reads and improve the efficiency of variant detection.