Mikhail Spivakov and lab (MRC LMS; Imperial College London)
DNA regulatory elements such as enhancers are often located large genomic distances away from the genes they control (up to millions of base pairs). It is generally accepted however that these elements typically exert their effects by coming into physical proximity with their target genes through 3D chromosomal contacts.
The recently proposed ABC model (Fulco et al., Nat Genet 2019) predicts gene expression using data on the activity of enhancers and their contacts with gene promoters in 3D.
As a source of 3D chromosomal contacts, the ABC model uses data from Hi-C (Lieberman-Aiden et al., Nature 2009), a high-throughput biochemical technique that is based on proximity ligation of DNA fragments that co-localise in 3D followed by detection by sequencing. This technique can theoretically profile the contacts between all pairs of DNA fragments in a cell’s nucleus, but it suffers from limited sensitivity due to the fact that enormous amounts of sequencing are required to detect these contacts robustly. Various techniques have been developed to enrich the material for contacts of interest, including Capture Hi-C (Schoenfelder et al., Genome Res 2015; Mifsud et al. Nat Genet 2015) and HiChIP (Mumbach et al., Nat Meth 2016).
The aim of this project is to test whether using Capture Hi-C data processed in different ways instead of low-resolution Hi-C improves the predictive power of the ABC model against gold-standard CRISPR-mediated repression datasets.
Desired skill level
This project requires an intermediate level of R or another scripting language, experience of working with large datasets and epigenomic analysis tools. Basic understanding of gene regulation and genomic assays is desirable.