EDUCATION

University of Toronto [Jan. 2019 – To Date]

Doctorate of Science, Computational Biology, Supervisor: Dr. Hannes Röst

Research areas: Proteomics, Multi-Omics, Personalized Medicine, Algorithmic and Software Development, Machine Learning

McMaster University [Sept. 2012 – June 2017]

Bachelor of Technology, Specialization in Biotechnology

RESEARCH EXPERIENCE

University of Toronto, Toronto, Canada [Jan. 2019 – To Date]

Ph.D. Researcher in Dr. Hannes Röst’s Lab, Ph.D., Department of Molecular Genetics

• Implement methods for consistent quantification of post-translational modifications in large-scale mass spectrometry, resulting in a 21% increase in quantified phosphopeptides and maintaining a 97% consistent identification rate.

• Developed data analysis and prediction pipelines for multi-omic personalized medicine collaborations, with a focus on investigating Chronic Fatigue Syndrome (CFS) and Glioblastoma tumor recurrence.

Bruker Daltonics, Toronto, Canada [July 2024 – Dec. 2024]

Research and Software Engineer Intern

• Designed and implemented a Rust library for deep learning-based peptide property prediction and semi-supervised rescoring of peptide-spectrum matches (PSMs).

• Integrated transfer learning to enhance peptide property predictions, increasing identification rates for both tryptic and non-tryptic peptides in Sage’s proteomics database searching.

The Hospital for Sick Children,Toronto, Canada [May 2017 – Aug. 2018]

Junior Researcher, Dr. Ran Kafri’s Lab, Department of Molecular Genetics

• Contributed to the development of an experimental assay aimed at studying signaling pathways involved in coordinating cellular growth rate and cell size.

• Collaborated with Daniel Snyder, to develop a computational framework for image-analysis segmentation.

SELECTED PUBLICATIONS

Equal contributions and co-authorship is denoted by asterisk (*)

1. Sing, J.C.*, et. al. (2025) pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas. Journal of Proteome Research. doi.org/10.1021/acs.jproteome.4c00873

2. Jahanbani, F., Sing, J. C.*, et. al. (2024) Longitudinal Cytokine and Multi-Modal Health Data of an Extremely Severe ME/CFS Patient with HSD. Frontiers in Immunology. doi.org/10.3389/fimmu.2024.1369295

3. Sing, J.C.*, et. al. (2024) MassDash: A web-based dashboard for targeted mass spectrometry visualization. Journal of Proteome Research. doi.org/10.1021/acs.jproteome.4c00026

4. Cosenza-Contreras, M., Schäfer, A., Sing, J., et al. (2024) Proteometabolomics of initial and recurrent GBM highlights an increased immune cell signature with altered lipid metabolism, Neuro-Oncology. doi.org/10.1093/neuonc/noad208

5. Gupta, S., Sing, J.C.* and Röst, H.L. (2023) Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment, Communications Biology, 6(1). doi:10.1038/s42003-023-05437-2

6. Sing, J.*, et. al. (2023). Diagnostic potential of microbiome metagenomics sequencing for cardiovascular disease risk stratification. BioRxiv. doi.org/10.1101/2023.10.02.560614.

7. Srinivasan, A., Sing, J.*, Gingras, AC., and Röst, H. (2022). Improving phosphoproteomics profiling using data-independent mass spectrometry. Journal of Proteome Research. doi.org/10.1021/acs.jproteome.2c00172

TEACHING EXPERIENCE

SELECTED PRESENTATIONS

HUPO, Enhancing Consistent Quantification of Site-Localized PTMs in Large-Scale DIA-MS Experiments [Sept. 2023]

Research To The People Symposium, Proteomics for Personalized Cancer Care [June. 2023]

SELECTED HONOURS AND AWARDS