Ian C. McDowell

This paper develops novel base editors by diversifying TadA orthologs and using generative models to optimize specificity and efficiency.

The paper introduces BETS, a statistical framework for inferring gene regulatory networks from transcriptional time series data, and validates it using glucocorticoid response data and external sources.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper introduces a nonparametric model, DPGP, to cluster gene expression time series data and demonstrates its effectiveness using simulated and real datasets.

The paper constructs co-expression networks that integrate transcription and splicing regulation across tissues, identifying shared and tissue-specific regulatory hubs and genetic variants impacting gene expression.

The paper uses Mendelian randomization to study lincRNA regulation of mRNA expression across four tissues, finding linc-eQTLs similar to protein-coding eQTLs and identifying lincRNAs associated with adipose-related traits.