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Tissue-specific transcriptional networks

Beyond the studies in the lab focused on germline gene regulatory mechanisms, we have long been key members of multiple consortia (modENCODE, modERN) dedicated to systematic identification of functional elements in the worm genome. In particular, we have determined the whole-animal binding profiles of ~400 transcription factors (TFs) using ChIP-seq. These large scale datasets are available to the research community, facilitating investigation of many different aspects of gene regulation in C. elegans. They set the stage to ask the next critical questions about gene regulation, including: Which properties of TF binding consistently distinguish between productive and non-productive effects on gene expression? Can TF functional redundancy or compensation be reliably predicted? To what extent does cellular context affect functional relationships between TFs?To address these questions, the lab is embarking on a new project to systematically generate paired, cell type-specific, in vivo binding and gene expression profiles for ~75 TFs that have human orthologs and represent a range of TF families. We intend to develop machine learning models using these data to discover the key characteristics of TF binding that result in transcriptional regulation of target genes and predict functional TF binding independent of experimental system.