Dr Justin P. Whalley
Senior Postdoctoral Bioinformatician
I am a senior postdoctoral bioinformatician in the Knight group studying the the varying responses of the innate immune system. I teach the Introduction to Statistics module to first year DPhil in Genomic Medicine and Statistics students. I am also a Research Member of Common Room at Kellogg College.
We can produce, large, detailed datasets detailing genes’ expression in the innate immune system cells under various treatments. I enjoy the technical and mathematical challenge of reducing these multi-dimensional datasets to a set of quintessential features that can help us further understand the innate immune system. I look to use partial least squares and similar methods to provide us with powerful techniques to find which samples have an extreme response to treatment and which genes are differentially expressed in these cases. Another useful tool is tensor decomposition, allowing us to pinpoint interesting gene expression patterns; dependent on which sample and under which treatment the genes are acting under. This will hopefully lead to a better understand our innate immune system and the associated important diseases from auto-inflammatory states to sepsis.
My background is mathematics, learnt at the University of Bristol. My introduction to biology was at the Université Evry Val d’Essonne, where I undertook a PhD in plant genetics with a special interest in how biological networks are perturbed by gene duplications. Following Paris, I headed to Barcelona and the Centro Nacional de Análisis Genómico to work in medical genomics, with concerns in rare, hereditary diseases and cancer. I concentrated on next generation sequencing data and the question of how to trust results when combining data from various sources, as done in meta-projects like the Pan-Cancer Analysis of Whole Genomes and RD-Connect. Currently my research interests have drifted to working with large, transcriptomic and genomic datasets and using various tools developed for data mining to extract interesting information, with applications in understanding our innate immune system. Though I am also happy to work with other big datasets; from other fields of research to the more quirky, like the literary clock.
Framework for quality assessment of whole genome cancer sequences
Whalley JP. et al, (2020), Nature Communications, 11
Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples
Bailey MH. et al, (2020), Nature Communications, 11
Sex differences in oncogenic mutational processes
Li CH. et al, (2020), Nature Communications, 11
Pan-cancer analysis of whole genomes
(2020), Nature, 578, 82 - 93
Recurrent somatic mutations reveal new insights into consequences of mutagenic processes in cancer.
Stobbe MD. et al, (2019), PLoS computational biology, 15