Methods to Improve Insight from (sc)ATAC-seq for Regulatory Networks and Genetics
Principle Investigator: Emily Miraldi, PhD
Most human disease-associated genetic polymorphisms fall outside protein-coding sequences, overlap significantly with enhancers, promoters, and other locus-control regions, and are thought to affect cellular behavior and disease phenotypes by altering gene transcription. Thus, reconstruction of cell-specific gene regulatory networks (GRNs) in humans is essential for uncovering causal links between the genetic variation in non-coding DNA and complex diseases. GRNs describe the control of gene expression by transcription factors (TFs); they provide mechanistic insight into the complex regulation of cellular behavior and human diseases.
The Assay for Transposase Accessible Chromatin (ATAC-seq) opens new opportunities for GRN inference and genetics. This easy-to-use, popular technique provides high-resolution chromatin accessibility with low sample input requirements, even to single-cell resolution. Our group has demonstrated that ATAC-seq improves GRN inference in mammalian settings. ATAC-seq can provide mechanistic insight into gene regulation and genetics through prediction of TF binding profiles and 3D- chromatin connectivity, linking TF binding sites to gene loci of action. We are building maxATAC, a suite of top-performing models for TF-binding and histone-state prediction and a novel algorithm to infer 3D-chromatin- interactions from ATAC-seq or scATAC-seq (collectively “(sc)ATAC-seq”).
