Papers in the works at CSSB lab

  1. MetaboKIT: an open-source software suite for untargeted metabolomics data processing with various modes of mass spectrometry data acquisition
  2. MultiSLIDE: user-guided simultaneous visualization of multiple -omics data
  3. Gene-to-Protein-to-Disease (GPD): a protein-centric approach to test genotype-disease associations using exome sequencing data (with Christine Vogel)
  4. iOmicsPASS: network-based integration of multi-omics data for predictive subnetwork discovery (with K.P. Choi, Rob Ewing)
  5. Cross covariance estimation for integration of multiple molecular data sets (with Johan Lim)
  6. Truncated rank correlation (TRC) as a robust of test-retest reliability in mass spectrometry data (with Johan Lim)
  7. A review article: Next generation proteomics data integration needs to exploit relationships between data types (with Christine Vogel)

Papers by the CSSB lab in the past 5 years

  1. 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. Accepted.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. H. Choi, D. Ghosh, Z. Qin. Computationally tractable multivariate HMM in genome-wide mapping studies. Hidden Markov Models: Methods and Protocols 2017, 135-148.
  7. 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.
  8. G.C. Teo, H.W.L. Koh, D. Fermin, J.P. Lambert, J.D. Knight, A.C. Gingras, H. Choi. SAINTq: scoring protein-protein interactions in affinity purification-mass spectrometry experiments with fragment or peptide intensity data. Proteomics 2016, 16 (15-16):2238-45.
  9. Z. Cheng#, G.S. Teo#, S. Krueger, T.M. Rock, H.W.L. Koh, H. Choi*, C. Vogel*. Differential dynamics of the mammalian mRNA and protein expressionresponse to misfolding stress. Mol. Syst. Biol. 2016, 12 (1):855.
  10. G.S. Teo, S. Kim, Ben Collins, A.C. Gingras, A.I. Nesvizhskii, H. Choi. mapDIA: a new platform for data normalization and analysis for label-free quantitative proteomics data. J. Proteomics 2015, 129:108-20.
  11. G. Chen, C. Liang, D. Fermin, C.N. Ong, C.S. Tan, H. Choi, MetTailor: Dynamic block summary and data normalization for robust analysis of mass spectrometry data in metabolomics. Bioinformatics. 2015, 31 (22):3645-52.
  12. H. Choi, S. Kim, D. Fermin, C.C. Tsou, A.I. Nesvizhskii, QPROT: statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics. J. Proteomics 2015, 129:121-6.
  13. H.W.L. Koh, H. Swa, D. Fermin, S.G. Ler, J. Gunaratne, H. Choi, EBprot: differential expression analysis in labeling-based quantitative proteomics experiments. Proteomics. 2015, 15(15):2580-91.

Papers from collaboration

  1. Rendleman et al., New insights into the cellular temporal response to proteostatic stress. Elife 2018, 7, e39054.
  2. Rendleman et al., Integration of large-scale multi-omic datasets: a protein-centric view. Curr. Opin. Syst. Biol. 2018, In press.
  3. 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.
  4. 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.
  5. Caldez et al., Metabolic remodeling during liver regeneration. Devel. Cell 2018, 47 (4), 425-438.
  6. 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.
  7. Tan et al., In vitro model of fully differentiated human nasal epithelial cells infected with rhinovirus reveals epithelium-initiated immune responses, Journal of Infectious Diseases, 2017, jix640.
  8. Windpassinger et al., CDK10 mutations in humans and mice cause severe growth retardation, spine malformations, and developmental delays. AJHG 2017, 101 (3), 391-403.
  9. Shen et al., IKK2 regulates cytokinesis during vertebrate development. Sci. Rep. 2017, 7 (1), 8094.
  10. Liu et al., Profiling of plasma metabolites suggests altered mitochondrial fuel usage and remodeling of sphingolipid metabolism in individuals with Type 2 diabetes and kidney disease. Kidney International Reports 2017, 2 (3), 470-480.
  11. Knight et al., ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nature Methods 2017, 14 (7), 645-646.
  12. Sontag et al., Identification of novel host interactors of effectors secreted by Salmonella and Citrobacter. mSystems. 2017, 1 (4): pii:e00032-15.
  13. Lu et al., Metabolic signatures and risk of type 2 diabetes in a Chinese population. 2016, 59 (11): 2349-2359.
  14. Liu et al., Data independent acquisition analysis in ProHits 4.0. J. Proteomics. 2016, 149:64-68.
  15. Sun et al., Plasma fatty acids, oxylipins, and risk of myocardial infarction: the Singapore Chinese Health Study. J. Lipid Res. 2016, 57(7):1300-1307.
  16. Baltazar et al., Antibody binding alters the characteristics and contents of extracellular vesicles release by Histoplasma capsulatum. mSphere 2016, 1(2): e00085-15.
  17. Shaik et al., Phosphoprotein network analysis of white adipose tissues unveils deregulated pathways in response to high-fat diet. Sci. Rep. 2016, 6:25844.
  18. Liu et al., Data independent acquisition analysis in ProHits 4.0. J. Proteomics 2016, S1874-3919(16):30174-9.
  19. Oh et al., Adjuvant-induced human monocyte secretome profiles reveal adjuvant- and age-specific protein signatures. Mol. Cell. Proteomics, 2016, 15(6):1877-1894.
  20. Chen et al., Plasma metabonomic profiling of diabetic retinopathy, Diabetes, 2016, 65(4):1099-1108.
  21. Liu et al., Gene essentiality is a quantitative property linked to cellular evolvability. Cell 2015, 163(6):1388-99.
  22. Sun et al., Plasma a-linolenic and long-chain w-3 fatty acids are associated with a lower risk of acute myocardial infarction in Singapore Chinese adults. J. Nutr. 2015, 146(2):275-282.
  23. Majumdar et al., Environmental effects of nanoceria on seed production of common bean (Phaseolus vulgaris): a proteomic analysis. Environ. Sci. Technol. 2015, 49(22):13283-93.
  24. Lee et al., Mammographic breast density and common genetic variants in breast cancer risk prediction. PLoS One, 2015, 10(9):e0136650.
  25. Tsou et al., DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics, Nat. Methods 2015, 12(3):258-64.