Abstract

We present the software Condition-specific Regulatory Units Prediction (CRUP) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell types and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.

Highlights

  • Gene expression is to a large degree regulated by distal genomic elements referred to as enhancers [1], which recruit a combination of different factors to activate transcription from a targeted core promoter

  • Short summary of CRUP In this work, we describe the three-step framework Condition-specific Regulatory Units Prediction (CRUP) to predict active enhancer regions, assign them to conditions, and correlate each dynamically changing enhancer to putative target genes

  • CRUP-EP is designed such that it takes into account the basic genomic structure of an enhancer, which is in essence an open chromatin region flanked by nucleosomes

Read more

Summary

Introduction

Gene expression is to a large degree regulated by distal genomic elements referred to as enhancers [1], which recruit a combination of different factors to activate transcription from a targeted core promoter. The activity state of enhancers may change dynamically across conditions, e.g., across varying time points or disease states. Their activity patterns are central in the context of phenotypic diversity [2, 3], and altered activity can be the source of pathogenic gene-enhancer disruptions and subsequent misregulation [4]. By analyzing epigenetic profiles of experimentally determined enhancers, e.g., histone modifications (HMs) or binding sites of co-activators like p300 [6] based on ChIP-seq measurements [7], dynamic changes

Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.