16 May 2025
Emily MIraldi
Divisions of Immunobiology and Biomedical Informatics
Cincinatti Children's Hospital
Single-cell (sc) RNA sequencing provides a quantitative, genome-scale approximation of cell behavior within complex tissue environments. Gene regulatory networks (GRNs) describe the control of gene expression by transcription factors (TFs) and are thus an essential engineering tool linking cellular behaviors to targetable, molecular regulatory mechanisms. The majority of disease-associated genetic risk variants are noncoding, enriched in enhancers and TF binding sites. Thus, GRNs are also essential to understanding the impact of noncoding genetic variants on cell behavior, as they connect TFs, the bona fide readers of DNA sequence (genetics) and chromatin state (interplay between environment and genetics), to downstream gene expression. Despite the strong rationale and exponential increase in sc-genomics datasets for GRN inference, accurate construction of context- and cell-type-specific GRNs form sc-genomics data is an outstanding challenge. In this theory lunch, I will discuss challenges, contradictions, best practices and future opportunities for GRN inference from sc-genomics data.