Skip to Main Content
Justin P. Whalley, PhD

Justin P. Whalley, PhD

Justin P. Whalley, PhD
Assistant Professor

Dr. Justin P. Whalley was educated in the UK (M.Sci. Mathematics, University of Bristol) and France (Ph.D. Bioinformatics, University of Évry).

He moved to Spain to work as a postdoc at the National Center for Genomic Analysis (CNAG). During his time there, he ran the Quality Control working group for the Pan-Cancer Analysis of Whole Genomes project to assess the data coming in and reduce batch effects. This involved collaboration with researchers from the Broad Institute of MIT and Harvard, the German Cancer Research Center and the Wellcome Sanger Institute in the UK.

He returned to the UK to work as a Senior Bioinformatician at the University of Oxford. His time there coincided with the global pandemic and he was deeply involved in the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium as the lead for the Integration (Tensor) working group.

Dr. Whalley became a member of the faculty of the Chicago Medical School at Rosalind Franklin University of Medicine and Science in January 2023.


We can produce large, detailed datasets detailing genes’ expression, protein abundance, epigenetic profiles and cell populations. I work on the computational and mathematical challenge of reducing these multidimensional datasets to a set of quintessential features. Using tensor decomposition, we can integrate this multilevel data and uncover the importance of interaction between these different layers in understanding biological systems. The work is in support of a long term research goal to understand clinical symptoms or biological phenomena at the molecular level. This has the translational potential to help predict outcomes for patients, especially those presenting with an infection, and hence guide treatment.

Another interesting avenue of research is looking to work with already published data. By combining independent datasets, we increase our power to find interesting results, that would otherwise remain hidden in smaller studies. This work can also provide a useful guide to future research, or act as a validation for our experimental work.

This research is done independently, as well as in collaboration with faculty from within Rosalind Franklin University of Medicine, and external researchers.


  • Torrance HD, Zhang P, Longbottom ER, Mi Y, Whalley JP, Allcock A, Kwok AJ, et al. (2023). A transcriptomic approach to understand patient susceptibility to pneumonia after abdominal surgery. Under review at Annals of Surgery, preprint can be found at medRxiv
  • COVID-19 Lung Single Cell Mass Cytometry Imaging Consortium (COSMIC) (2022). Unbiased single cell spatial analysis localises inflammatory clusters of immature neutrophils-CD8 T cells to alveolar progenitor cells in fatal COVID-19 lungs. Under review at Nature Communications, preprint can be found at medRxiv
  • Mi Y, Burnham KL, Charles PD, Heilig R, Vendrell I, Whalley JP, Torrance HD, et al. (2022). High-throughput mass spectrometry maps the sepsis plasma proteome and differences in response. Under review at Science Translational Medicine, preprint can be found at medRxiv
  • Fish M, Rynne J, Jennings A, Lam C, Lamikanra AA, Ratcliff J, Cellone-Trevelin S, et al. Coronavirus disease 2019 subphenotypes and differential treatment response to convalescent plasma in critically ill adults: secondary analyses of a randomized clinical trial. (2022). Intensive care medicine. doi:10.1007/s00134-022-06869-w PMID:36102943
  • Kotanidis CP, Xie C, Alexander D, Rodrigues JCL, Burnham K, Mentzer A, O’Connor D, et al. Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19. (2022). The Lancet. Digital health. doi:10.1016/S2589-7500(22)00132-7 PMID:36038496
  • Gilchrist JJ, Makino S, Naranbhai V, Sharma PK, Koturan S, Tong O, Taylor CA, et al. Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity. (2022). Nature communications. doi:10.1038/s41467-022-31626-4 PMID:35835762
  • Zhang P, Amarasinghe HE, Whalley JP, Tay C, Fang H, Migliorini G, Brown AC, et al. Epigenomic analysis reveals a dynamic and context-specific macrophage enhancer landscape associated with innate immune activation and tolerance. (2022). Genome biology. doi:10.1186/s13059-022-02702-1 PMID:35751107
  • COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. (2022). Cell. doi:10.1016/j.cell.2022.01.012 PMID:35216673
  • Zhang JY, Roberts H, Flores DSC, Cutler AJ, Brown AC, Whalley JP, Mielczarek O, et al. Using de novo assembly to identify structural variation of eight complex immune system gene regions. (2021). PLoS computational biology. doi:10.1371/journal.pcbi.1009254 PMID:34343164
  • Navarro-Guerrero E, Tay C, Whalley JP, Cowley SA, Davies B, Knight JC, Ebner D. Genome-wide CRISPR/Cas9-knockout in human induced Pluripotent Stem Cell (iPSC)-derived macrophages. (2021). Scientific reports. doi:10.1038/s41598-021-82137-z PMID:33608581
  • Whalley JP, Buchhalter I, Rheinbay E, Raine KM, Stobbe MD, Kleinheinz K, Werner J, et al. Framework for quality assessment of whole genome cancer sequences. (2020). Nature communications. doi:10.1038/s41467-020-18688-y PMID:33028839
  • Bailey MH, Meyerson WU, Dursi LJ, Wang LB, Dong G, Liang WW, Weerasinghe A, et al. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples. (2020). Nature communications. doi:10.1038/s41467-020-18151-y PMID:32958763
  • Li CH, Prokopec SD, Sun RX, Yousif F, Schmitz N, PCAWG Tumour Subtypes and Clinical Translation, Boutros PC, PCAWG Consortium. Sex differences in oncogenic mutational processes. (2020). Nature communications. doi:10.1038/s41467-020-17359-2 PMID:32859912
  • Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, et al. The evolutionary history of 2,658 cancers. (2020). Nature. doi:10.1038/s41586-019-1907-7 PMID:32025013
  • ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. (2020). Nature. doi:10.1038/s41586-020-1969-6 PMID:32025007
  • PCAWG Transcriptome Core Group, Calabrese C, Davidson NR, Demircio─člu D, Fonseca NA, He Y, Kahles A, et al. Genomic basis for RNA alterations in cancer. (2020). Nature. doi:10.1038/s41586-020-1970-0 PMID:32025019
  • Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, Boot A, et al. The repertoire of mutational signatures in human cancer. (2020). Nature. doi:10.1038/s41586-020-1943-3 PMID:32025018
  • Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshøj H, et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. (2020). Nature. doi:10.1038/s41586-020-1965-x PMID:32025015
  • Li Y, Roberts ND, Wala JA, Shapira O, Schumacher SE, Kumar K, Khurana E, et al. Patterns of somatic structural variation in human cancer genomes. (2020). Nature. doi:10.1038/s41586-019-1913-9 PMID:32025012
  • Carlevaro-Fita J, Lanzós A, Feuerbach L, Hong C, Mas-Ponte D, Pedersen JS, PCAWG Drivers and Functional Interpretation Group, Johnson R, PCAWG Consortium. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis. (2020). Communications biology. doi:10.1038/s42003-019-0741-7 PMID:32024996
  • Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, et al. Pathway and network analysis of more than 2500 whole cancer genomes. (2020). Nature communications. doi:10.1038/s41467-020-14367-0 PMID:32024854
  • Jiao W, Atwal G, Polak P, Karlic R, Cuppen E, PCAWG Tumor Subtypes and Clinical Translation Working Group, Danyi A, et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. (2020). Nature communications. doi:10.1038/s41467-019-13825-8 PMID:32024849
  • Paczkowska M, Barenboim J, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, et al. Integrative pathway enrichment analysis of multivariate omics data. (2020). Nature communications. doi:10.1038/s41467-019-13983-9 PMID:32024846
  • Cmero M, Yuan K, Ong CS, Schröder J, PCAWG Evolution and Heterogeneity Working Group, Corcoran NM, Papenfuss T, et al. Inferring structural variant cancer cell fraction. (2020). Nature communications. doi:10.1038/s41467-020-14351-8 PMID:32024845
  • Rubanova Y, Shi R, Harrigan CF, Li R, Wintersinger J, Sahin N, Deshwar AG, et al. Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. (2020). Nature communications. doi:10.1038/s41467-020-14352-7 PMID:32024834
  • Zhang Y, Chen F, Fonseca NA, He Y, Fujita M, Nakagawa H, Zhang Z, et al. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations. (2020). Nature communications. doi:10.1038/s41467-019-13885-w PMID:32024823
  • Bhandari V, Li CH, Bristow RG, Boutros PC, PCAWG Consortium. Divergent mutational processes distinguish hypoxic and normoxic tumours. (2020). Nature communications. doi:10.1038/s41467-019-14052-x PMID:32024819
  • Shuai S, PCAWG Drivers and Functional Interpretation Working Group, Gallinger S, Stein LD, PCAWG Consortium. Combined burden and functional impact tests for cancer driver discovery using DriverPower. (2020). Nature communications. doi:10.1038/s41467-019-13929-1 PMID:32024818
  • Sieverling L, Hong C, Koser SD, Ginsbach P, Kleinheinz K, Hutter B, Braun DM, et al. Genomic footprints of activated telomere maintenance mechanisms in cancer. (2020). Nature communications. doi:10.1038/s41467-019-13824-9 PMID:32024817
  • Stobbe MD, Thun GA, Diéguez-Docampo A, Oliva M, Whalley JP, Raineri E, Gut IG. Recurrent somatic mutations reveal new insights into consequences of mutagenic processes in cancer. (2019). PLoS computational biology. doi:10.1371/journal.pcbi.1007496 PMID:31765368