Computational Resources

Here you can find some of the tools and other resources developed in the group.
Personalised ASE Caller (PAC)

Saukkonen et al. 2022 Genome Research

Allele-specific expression (ASE) is the imbalanced expression of the two alleles of a gene. While most genes are expressed equally from both alleles, gene regulatory differences driven by genetic changes (i.e. regulatory variants) frequently cause the two alleles to be expressed at different levels, resulting in allele-specific expression patterns. The power of ASE analysis lies in its applicability to individual samples. We have developed a personalised ASE calling pipeline for RNA-seq data that combines diploid genome mapping with other features that improve the accuracy of ASE calls in single-samples analyses. This work was a collaboration with Dr. Alan Hodgkinson (King’s College London, UK).


OOB: A toolbox for analysing omics data 'out-of-bounds'

The oob package provides a collection of tools for analysing and visualising different types of 'omics' data, including analyses specific to functional enrichment and bulk/single-cell RNA-sequencing. It was designed with the philosophy of avoiding the use of specialised R objects that may become opaque due to their complex structures. The package contains functions for reading/writing text files and other data formatting utilities. It can be used for computing various statistics such as mode, geometric mean, coefficient of variation, standard error of the mean, and correlation distances. It also includes a wide range of normalisation techniques for RNA/scRNA-seq and implementation of diverse analyses (e.g. PCA, UMAP, TRIMAP, Leiden clustering). Easy-to-use functional enrichment can be performed via several families of algorithm: overrepresentation, functional class scoring, or with a method developed with this package, Gene Set Differential Activation (GSDA). A comprehensive set of plotting functions is also available at each step of the analyses.