Main Research Papers by the CSSB lab in the past 5 years

1. H. Koh, A. Pilbrow, S. Tan, Q. Zhao, P. Benke, B. Burla, F. Torta, J. Pickering, R. Troughton, C. Pemberton, W. Soo, L.H. Ling, R. Doughty, H. Choi, M. Wenk, A.M. Richards, M. Chan. An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes. Frontiers in Cardiovascular Medicine 2023. Paper featuring the upgraded iOmicsPASSplus software.

2. M. Wenk and H. Choi. Abundant circulating lipids - a new opportunity for NSCLC detection? News and Views article. Nat. Rev. Clin. Oncol. 2022; 19(6):361-362. 

3. Y. Tan, Y. Gao, G. Teo, H. Koh, E.S. Tai, C.M. Khoo, K.P. Choi, L. Zhou, H. Choi. Plasma metabolome and lipidome associations with type 2 diabetes and diabetic nephropathy. Metabolites 2021; 11(4):228. 

4. S. Ko, G.X.L. Li, H. Choi*, J. Won*. Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx. Brief. Bioinfo. 2021; 22(6):bbab256. 

5. S. Ghosh, A. Datta, H. Choi. multiSLIDE is a web server for exploring connected elements of biological pathways in multi-omics data. Nat. Comms 2021; 12(1): 1-11. 

6. G. Teo, W.S. Chew, B. Burla, D. Herr, E.S. Tai, M.R. Wenk, F.T. Torta, H. Choi. MRMkit: automated data processing for large-scale targeted metabolomics analysis. Analytical Chemistry 2021; 92(20): 13677-13682.  

7. P. Narayanaswamy#, G. Teo#,  J.R. Ow, A. Lau, P. Kaldis, S. Tate, and H. Choi. MetaboKit: a comprehensive data extraction tool for untargeted metabolomics. Mol. Omics 2020; 16(5): 436-447. 

8. G.X.L. Li, D. Munro, D. Fermin, C. Vogel, H. Choi. A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes. Hum. Mutation. 2020; 41(5): 934-945. 

9. H.W.L. Koh, D. Fermin, C. Vogel, K.P. Choi, R. Ewing, H. Choi. iOmicsPASS: network-based integration of multi-omics data for predictive subnetwork discovery. npj Systems Biology and Applications 2019, 5:22. 

10. B. Vitrinel, H.W.L. Koh, F. Mujgan Kar, S. Maity, J. Rendleman, H. Choi, C. Vogel. Exploiting interdata relationships in next-generation proteomics analysis. Mol. Cell. Proteomics 2019, 18:S5-S14. 

11. H. Choi, H.W.L. Koh, L. Zhou, H. Cheng, T.P. Loh, E.P. Rizi, S.A. Toh, G.V. Ronnett, B.E. Huang, C.M. Khoo. Plasma protein and microRNA biomarkers of insulin resistance: a network-based integrative -omics analysis. Frontiers in Physiol. 2019; 10: 379. 

12. H.W.L. Koh, Y. Zhang, C. Vogel, H. Choi. EBprotV2: a Perseus plugin for statistical analysis of labeling-based quantitative proteomics data. J. Proteome Res. 2018, 18 (2), 748-752. 

13. S. Ghosh, A. Datta, K. Tan, H. Choi, SLIDE - a web-based tool for interactive visualization of large-scale -omics data. Bioinformatics 2018, 35 (2), 346-348. 

14. G.X. Li, C. Vogel, H. Choi. PTMscape: an open source tool to predict generic post-translational modifications and map hotspots of modification crosstalk. Molecular Omics 2018, 14:197-209. 

15. G.S. Teo, Y. Zhang, C. Vogel*, H. Choi*. PECAplus: a comprehensive package of software tools for time-specific, biological function-level detection of dynamic regulatory changes. npj Systems Biology and Applications 2017, 4 (1), 3.

16. H. Choi, D. Ghosh, Z. Qin. Computationally tractable multivariate HMM in genome-wide mapping studies. Hidden Markov Models: Methods and Protocols 2017, 135-148.

17. G. Chen, S. Walmsley, G. Cheung, L. Chen, C. Cheng, R. Beuerman, T. Wong, L. Zhou*, H. Choi*. Customized consensus spectral library building for untargeted quantitative metabolomics analysis using data independent acquisition mass spectrometry and MetaboDIA workflow, Analytical Chemistry. 2017, 89 (9), 4897-4906.

Papers from collaboration

1. J. Chang et al., Q-RAI data-independent acquisition for lipidomic quantitative profiling. Sci. Rep. 2023; 13(1): 19281.

2. M. Hoppe et al., Patterns of oncogene coexpression at single-cell resolution influence survival in lymphoma. Cancer Discovery. 2023; 13(5): 1144-1163.   

3. K. Allgoewer et al., Re-mining serum proteomics data reveals extensive post-translational modifications upon Zika and dengue infection. Mol. Omics 2023; 19(4): 308-320. 

4. S. Wu et al., Proteomic signatures of the serological response to influenza vaccination in a large human cohort study. Viruses 2022; 14(11): 2479. 

5. S. Wu et al., Evaluation of determinants of the serological response to the quadrivalent split-inactivated influenza vaccine. Mol. Syst. Biol. 2022; 18(5):e10724. 

6. T. Tumkaya et al., Most primary olfactory neurons have individually neutral effects on behavior. Elife 2022; 11:e71238. 

7. S. Tan et al., Variability of the plasma lipidome and subclinical coronary atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 2022; 42(1):100-112. 

8. A. Chan et al., Novel autoantibodies in idiopathic small fiber neuropathy. Ann. Neurol. 2022;91(1):66-77. 

9. A. Targa el al., Non-genetic and genetic rewiring underlie adaptation to hypomorphic alleles of an essential gene. EMBO J. 2021;40(21):e107839. 

10. J.W. Dear et al., A metabolomic analysis of thiol response for standard and modified N-acetyl cysteine treatment regimens in patients with acetaminophen overdose. Clin. Transl. Sci. 2021;14(4):1476-1489. 

11. M. Hoppe et al., Quantitative imaging of RAD51 expression as a marker of platinum resistance in ovarian cancer. EMBO Mol. Med. 2021;13(5):e13366. 

12. K. Allgoewer et al., New proteomics signatures to distinguish between Zika and Dengue infections. Mol. Cell. Proteomics. 2021;20:100052. 

13. J. Ow et al., Remodeling of whole-body lipid metabolism and a diabetic-like phenotype caused by loss of CDK1 and hepatocyte division. Elife 2020, 9, e63835. 

14. M. Dewhurst et al., Loss of hepatocyte cell division leads to liver inflammation and fibrosis. PLoS Genetics 2020, 16 (11), e1009084. 

15. S. Ahn et al., Self-semi-supervised clustering of large-scale data with a massive null group. JKSS 2020, 1-16.

16. S. Manohar et al., Polyubiquitin chains linked by lysine residue 48 selectively target oxidized proteins in vivo. Antioxidants & Redox Signaling 2019, 31(15), 1133-1149. 

17. J. Niska-Blakie et al., Knockout of the non-essential gene SUGCT creates diet-linked, age-related microbiome disbalance with a diabetes-like metabolic syndrome phenotype. Cellular and Molecular Life Sciences, 2019, In press. 

18. J. Ong et al., Insights into early recovery from influenza pneumonia by spatial and temporal quantification of putative lung regenerating cells and by lung proteomics. Cells 2019;8(9):975. 

19. Ho et al., Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16(7):565-566. 

20. Chew et al., Large-scale lipidomics identifies associations between plasma sphingolipids and T2DM incidence. JCI Insight 2019, 4: 126925. 

21. J. Lim et al., Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. Statist. App. Genet. Mol. Biol. 2019;18(4):/j/sagmb.2019.18.issue-4. . 

22. Rendleman et al., New insights into the cellular temporal response to proteostatic stress. Elife 2018, 7, e39054. 

23. Rendleman et al., Integration of large-scale multi-omic datasets: a protein-centric view. Curr. Opin. Syst. Biol. 2018, In press. 

24. Lee et al., ABRF Proteome Informatics Research Group (iPRG) 2016 Study: inferring proteoforms from bottom-up proteomics data. J. Biomol. Tech. 2018, 29 (2), 39. 

25. Baltazar et al., Concentration-dependent protein loading of extracellular vesicles released by Histoplasma capsulatum after antibody treatment and its modulatory action upon macrophages. Sci. Rep. 2018, 8 (1), 8065.  

26. Caldez et al., Metabolic remodeling during liver regeneration. Devel. Cell 2018, 47 (4), 425-438. 

27. Hahn et al., A least squares method for detecting multiple change points in a univariate time series. Pacific Rim Statistical Conference for Production Engineering 2018, 125-143.