Abstract
In this talk I will present two methods for understanding gene regulation. The first is Stitchit, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. Stitchit segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods on different datasets, and higher enrichment in experimentally determined chromatin interactions and GWAS mutations. We validated newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of Stitchit.
Second, I will present a novel approach for predicting gene expression in single cell RNA data using cis-regulatory motifs or epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. Our proposed method allows us to identify distinct TFs that show cell-type specific regulation and opens new paths for integration of single cell RNA and chromatin data.
Zoom connection information
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Meeting ID: 636 5439 5783; Passcode: 422473
Junior talk
Ieva Rauluseviciute, PhD student at the Centre for Molecular Medicine Norway (NCMM), UiO, will present her work about "Identification of transcription factor co-binding events."