Kimberly Glass

This paper introduces MONSTER, a method to identify transcription factor drivers of cell state transitions using gene regulatory network models, applied to COPD studies to find persistent regulatory signals.

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

This study analyzes gene regulation across 38 tissues to show that transcription factor-target connections are more tissue-specific than genes, and that tissue-specific genes are not highly targeted in their networks but occupy bottleneck positions.

The paper compares gene expression and regulatory patterns between cell lines and their source tissues, finding reduced expression levels and altered transcription factor regulation in cell lines.

The paper uses a network-based approach to analyze eQTLs across 13 tissues, identifying cis- and trans-eQTLs and linking SNPs to biological processes and disease associations.

The paper identifies sexual dimorphism in gene expression and regulatory networks across 31 human tissues using GTEx data, showing that many genes and networks are sex-specific.