Bioinformatics: Predicting enhancer-promoter links from cancer transcriptomics data with network science

Background

Gene expression is mainly regulated at the transcriptional level by the binding of transcription factors at cis-regulatory regions such as promoters and enhancers. Promoters are regulatory regions located upstream of a gene transcription start site (TSS) where TFs attract RNA polymerases to initiate transcription. Enhancers are DNA regions that have the capacity to enhance transcription of target TSSs irrespective of orientation or genomic distance. While enhancers can be located far away in the genome from the promoters of the genes they are regulating, they are found in close 3D proximity in the nucleus. Despite recent technological advances to link enhancers to genes, it is still a challenge to identify (or predict) which enhancer is acting upon which promoter in a given condition.

Aim of the Master project

In this project, we aim to develop a computational model and software to predict enhancer-promoter links that are active in cancer patients. The candidate will adapt current methods used to predict gene regulatory networks to predict enhancer-promoter regulatory links from cancer transcriptomics data (RNA-seq from The Cancer Genome Atlas). Specifically, the model will rely on priors linking enhancers to promoters that are available from several resources such as EpiRegio. The regulatory network will then be updated based on co-activity captured by RNA-seq for both (from TCGA) using a message-passing algorithm. Finally, the algorithm will be expanded to reconstruct cancer patient- or subtype-specific networks.

Host environment

The candidate will be co-supervised by Drs. Marieke Kuijjer and Anthony Mathelier along with Drs. Tatiana Belova and Roza Berhan Lemma, postdoctoral fellows in their groups. Drs. Kuijjer and Mathelier have shared group meetings at the Centre for Molecular Medicine Norway (NCMM), UiO. Dr. Kuijjer is a group leader at the NCMM. Dr. Kuijjer's research focuses on developing computational tools to model gene regulatory networks in cancer and complex diseases. Her work has led to the implementation of several computational models to construct and study regulatory networks in cancers. Dr. Mathelier is a group leader at NCMM, UiO, and an adjunct researcher at the Department of Medical Genetics, Oslo University Hospital. His research focuses on understanding the regulation of gene expression by developing cutting-edge bioinformatics tools with immediate application to real-life biological problems. His work has led to the development of several computational tools and resources to decipher gene expression regulation.

Prerequisites

We seek a highly motivated individual with programming skills and interest in the development of computational tools dedicated to the analysis of high throughput sequencing data. Knowledge in statistical methods is a plus. The selected candidate will be excited about combining life sciences and computation to analyze gene expression regulation. The successful candidate will be collaborative, independent, with strong enthusiasm for research, and should be fluent in at least one of the following programming languages: Python, R, or bash. Being familiar with gene expression regulation in general is an advantage.

Publisert 1. okt. 2021 15:04 - Sist endret 1. okt. 2021 15:04

Omfang (studiepoeng)

60