Sebastian M. Waszak

This study identifies PP2A-A as a target of CRL3KBTBD4, showing that KBTBD4 mutations disrupt PP2A quality control and contribute to medulloblastoma by altering phospho-dependent signaling.

This paper develops an automated microinjection robot for zebrafish xenograft models, achieving 60% success rate and 70% survival rate, with twice the speed of manual injections.

The paper identifies gene dosage effects on telomere maintenance and disease pathways in neuroblastoma using genomic and transcriptomic data from 115 tumors.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

The paper analyzes somatic retrotransposition events in 2,954 cancer genomes, identifying L1 insertions as significant contributors to structural variations and oncogenesis.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

This paper identifies driver mutations across 2,500 tumors from 37 cancer types, revealing a whole-genome panorama of genomic events that underlie tumor development.

This study analyzes whole genome and RNA-Seq data from 1,188 cancer patients to map the effects of somatic and germline genetic variation on gene expression in cancer.

Butler enables faster cloud-based analysis of thousands of human genomes by managing infrastructure, workflows, and execution with 43% increased throughput.