Enrico Ferrero
Computational Biology, GSK, Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK
The identification of therapeutic targets is a critical issue in drug discovery, with several programmes failing because of a weak linkage between target and disease.
Genome-wide association studies and large gene expression experiments are providing insights into the biology of several common and complex diseases, but the complexity of transcriptional regulation mechanisms often limit our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacersacross different tissues and cell types.
In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.
The workflow is published on F1000Research as 'Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies'.
- The .Rmd source file can be used to reproduce the analysis.
- The .md file is best for visualisation on GitHub.
- The .html file is for visualisation in web browsers.
- the .docx file is for use with Microsoft Word.
- The .pdf file was created for submission to F1000Research through Overleaf.
- All other files are needed for knitting.