Natalie Sauerwald

Otari predicts tissue-specific isoform abundance changes caused by genetic variants using a graph-based attention model trained on genomic and long-read transcriptome data.

Mixture modeling of 239 phenotype features in 5,392 SPARK autistic children defines four replicable classes carrying distinct common and rare variant signatures and developmental gene-timing patterns.

This paper develops an algorithmic framework to optimize TAD definitions and biological properties, showing that TADs vary by cell type and are better represented as subpopulations rather than a single set.

This paper introduces a statistical nonparametric method to identify consistently important features across samples and demonstrates its application to RNA sequencing data.

This study quantifies structural variability of TADs across 137 Hi-C samples, showing significant variation between samples and protocol-specific changes but robustness to lab differences.

The paper uses topological data analysis to study chromosome structure changes during cellular differentiation across three cell lines, identifying global and local topological differences.

The paper uses a Gaussian Network Model (GNM) to predict chromosomal dynamics from Hi-C data, showing correlations with accessibility and gene coexpression.