Ignatius Pang

Dr. Ignatius Pang is a Senior Research Officer at CMRI experienced in collaborating with multiple research groups on diverse projects that required his expertise in bioinformatics, data analytics and results interpretation. The aims of the research collaborations often involve the analysis of data generated from ‘-omics’ experiments to identify biomarkers for personalized medicine, disease classification or predicting treatment outcome and to understanding the molecular basis of diseases. Achieving the above aims reqires his expertise in proteomics, phosphoproteomics, multivariate statistical analysis of ‘multi-omics’ datasets and the analysis and visualization of biological networks.

Research Interests

Quantitaitive Proteomics and Phosphoproteomics and Proteogenomics

Dr Pang is currently developing the novel ProteomeRiver pipeline which facilitates the reproducible analysis of differential abundance of proteins and their phosphorylation events. The pipeline enables the batch analysis of many pairwise treatment versus control comparisons and subsequent pathways overrepresentation analysis. To enable differential abundance analysis of mono- and multi-phosphorylation events, ProteomeRiver incorporates missing values imputation (PhosR*), removal of unwanted variation (ruv*), linear models (limma*), kinase-substrate enrichment (KinSwingR*), and pathways analysis (clusterProfiler*). The ProteomeRiver pipeline is currently applied on a variety of medical research projects.

Figure 1. KinSwingR tool prediction of upstream kinases.

In addition, he has also co-authored the PG Nexus pipeline which facilitates the proteogenomic data integration of proteomics, transcriptomics, and genomics datasets. The pipeline has been applied to the proteomic validation of alternatively spliced isoforms of mRNA transcripts in human mesenchymal stem cells. (*) denotes R or Bioconductor packages.

Integrative Multi-omics Analysis

Dr. Pang has experience in integrative ‘multi-omics’ data analysis, including transcriptomics, proteomics, phosphoproteomics, and metabolomics data, which are collected from the same samples or subjects. He has previously applied multi-omics analysis for the development of a low-alcohol wine yeast strain. In addition, Dr. Pang was involved in a large collaborative study which used multi-omics analysis for the identification of a biological pathway, common across 25 bacterial strains from four species, that allows in bacteria to survive in blood and cause sepsis. Sepsis has a mortality rate of 20-40% even with optimal treatment in high income studies, is estimated to have caused ~11 million deaths in 2017 and with antibiotic resistance mortality rate is rising.

Figure 2. Visualization of the abundance of transcripts, proteins and metabolites in the central carbon metabolism pathway using the Cytoscape network visualization tool (Varela et al. 2018 Metabolic engineering 49, 178-191).

Biological Network Analysis and Visualisation

Dr. Pang is applying biological networks analysis and network visualisation to identify how changes in the abundance of metabolites, transcripts, proteins, and post-translational modifications are associated with each other within the network. He is the co-developer of the Cytoscape App: PTMOracle, which is to be used for visualising post-translational modifications in protein interaction networks. He has also used transcriptomics and regulatory networks to understand the mechanism of antifungal drug synergy. Dr Pang was also worked on identifying the interactions between bacterial small RNA and their target genes and understanding their functions in regulating vancomycin resistance in methicillin/oxacillin-resistant Staphylococcus aureus (MRSA).

Figure 3. Cytoscape visualization of signalling network with phosphorylation within phosphodegron motif (blue) and cell cycle regulated proteins (red).

Top 5 Publications

Mediati, D. G., Wong, J. L., Gao, W., McKellar, S., Pang, C., Wu, S., Wu, W., Sy, B., Monk, I. R., Biazik, J. M., Wilkins, M. R., Howden, B. P., Stinear, T. P., Granneman, S., & Tree, J. J. (2022). RNase III-CLASH of multi-drug resistant Staphylococcus aureus reveals a regulatory mRNA 3'UTR required for intermediate vancomycin resistance. Nature communications, 13(1), 3558. link

Tay, A. P., Pang, C., Winter, D. L., & Wilkins, M. R. (2017). PTMOracle: A Cytoscape App for Covisualizing and Coanalyzing Post-Translational Modifications in Protein Interaction Networks. Journal of proteome research, 16(5), 1988–2003. link

Pang, C., Ballouz, S., Weissberger, D., Thibaut, L. M., Hamey, J. J., Gillis, J., Wilkins, M. R., & Hart-Smith, G. (2020). Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes. Molecular & cellular proteomics : MCP, 19(11), 1876–1895. link

Pang, C. N., Lai, Y. W., Campbell, L. T., Chen, S. C., Carter, D. A., & Wilkins, M. R. (2017). Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response. Scientific reports, 7, 40232. link

Tay, A. P., Pang, C. N., Twine, N. A., Hart-Smith, G., Harkness, L., Kassem, M., & Wilkins, M. R. (2015). Proteomic Validation of Transcript Isoforms, Including Those Assembled from RNA-Seq Data. Journal of proteome research, 14(9), 3541–3554. link