New Bioinformatics Tools for Enhanced Analysis of Single-Cell and Spatial Transcriptomics Data

Our laboratory is internationally recognised for developing cutting-edge computational tools for the advanced analysis of next-generation sequencing data, particularly RNA sequencing (RNA-seq). We have developed software that is widely adopted by the research community. For example, the featureCounts program is extensively used for RNA-seq quantification and has received over 20,000 citations. With rapid advances in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies, we are currently developing novel algorithms and software tools to improve the processing and analysis of data generated by these platforms. scRNA-seq and ST technologies are transforming the study of gene regulation and cellular function. scRNA-seq enables transcriptome profiling at single-cell resolution, revealing cellular heterogeneity that is masked in bulk tissue analyses. ST complements this by preserving tissue architecture, allowing gene expression to be mapped to specific spatial locations and microenvironments. Together, these approaches provide critical context for understanding cell-cell interactions and the influence of spatial organisation on cellular behaviour. Our methodological research in scRNA-seq and ST focuses on several key areas, including the discovery of alternative splicing events, identification of genomic variants, quantification and segmentation of spatial transcriptomics data, and the application of AI to improve single-cell and spatial data analysis. In addition, we are developing new algorithms for mapping long-read sequencing data and detecting genomic mutations from long-read platforms. We are seeking students with a strong computational background to join these research projects. Students will receive training in algorithm design, high-performance computing, and large-scale transcriptomics data analysis, working at the forefront of computational genomics. These projects also offer opportunities to collaborate closely with experimental researchers generating single-cell and spatial data, ensuring that the developed methods are broadly applicable and highly impactful. Prospective students should have a strong computational background (e.g. software engineering, computer science, bioinformatics, statistics, or related fields). Prior experience with biological data is not required; however, a strong interest in applying computational innovation to genomics is essential.