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With the recent advances in sequencing technologies, thousands of functional genomics data have been released, which provides unprecedented opportunities to annotate the human genome and interpret the genetic variant impact. Here, we develop such a deep genome annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on several data-rich ENCODE cell types. Specifically, we developed computational methods to integrate these multi-omics data to uniformly construct regulatory elements and perform deep integration over many advanced assays to connect many regulators and non-coding elements into multi-modal networks. We further organize this resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression, and GWAS. Targeted validations of the prioritized regulators, elements, and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of our annotation resource.

Dr. Zhang is an Assistant Professor at UCI. Her research interests are in the areas of bioinformatics and computational biology. She was previously a postdoc at Yale University. During her postdoc, she was one of the coordinating trainees in international consortia, such as ENCODE and psychENCODE, where she leveraged various machine learning technologies and novel high-throughput sequencing assays to decipher the gene regulation “grammar”. She led the current release of the ENCODEC deep annotation resource for cancer in ENCODE3. Her current research focus is on developing computational methods to understand how genetic variations can result in phenotypic changes.