Deciphering the sequence determinants of regulatory dynamics

Transcription factors (TFs) bind to regulatory elements and control cellular processes such as lineage specification and differentiation. Understanding the complex cis-regulatory grammar that underlies cell fate choice is of great importance for stem cell research and regenerative medicine. A large variety of functional signals can be measured genome-wide by techniques based on high-throughput sequencing, even at single cell resolution. This includes binding of TFs, the chromatin environment, DNA accessibility and DNA methylation. Here I will describe our work on deciphering the role of the DNA sequence in regulatory dynamics, based on analysis of genome-wide patterns. This includes the sequence determinants of the Polycomb-deposited histone modification H3K27me3 and the use of our motif analysis framework GimmeMotifs to identify relevant TF motifs from epigenomic and transcriptomic data.

Workshop: integrative analysis of epigenomic dynamics at regulatory elements

To control cell identity and behavior, transcription factors (TFs) bind to specific sequence motifs in cis-regulatory elements to integrate complex and interconnected cellular signals. Enhancers and promoters are are characterized by specific patterns of histone modifications and DNA methylation. For instance, active promoters are associated with histones where lysine 4 of histone H3 is tri-methylated (H3K4me3), while enhancer activity is associated with H3K27ac. This epigenomic state of regulatory elements is cell type-specific and shows dynamic changes during development or cellular differentiation. The epigenome can be assayed genome-wide through application of Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-seq). In this workshop, you will learn how to perform integrative analysis of epigenomic data. We will cover visualization, clustering, annotation and regulatory motif analysis. While no specific knowledge or experience is required, some familiarity with the command line will be beneficial.

Simon van Heeringen

Associate Professor Simon van Heeringen

Group Leader, Molecular Developmental Biology Department, Radboud University

Simon van Heeringen completed his PhD in 2012 at the Radboud University, the Netherlands, where he worked on computational analysis of transcription regulation. During his post-doc he worked with Gert Jan Veenstra to decipher the sequence determinants of binding by the Polycomb Repressive Complex 2 (PRC2), which is recruited to DNA and represses genes by long-term silencing through trimethylation of lysine 27 of histone H3 (H3K27me3). Using machine learning in combination with evolutionary approaches Simon uncovered PRC2-associated sequence patterns conserved between vertebrates. In a recent collaborative follow-up to this work he found that MTF2 is required for DNA-driven PRC2 recruitment to chromatin. In 2013 Simon started as a group leader in the Molecular Developmental Biology department at the Radboud University. Through his research he aims to understand the complex cis-regulatory grammar that underlies cell fate choice by application of computational tools to high-throughput genomic measurements. This includes analysis and integration of transcriptomic and epigenomic data, regulatory motif analysis, machine learning and predictive modeling. In addition, he develops computational methods and tools for genomic annotation and regulatory network inference, which are necessary to gain insight into the regulatory rules underlying development and differentiation.

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