Main papers by the CSSB lab in the past 5 years

  1. H.W.L. Koh, Y. Zhang, C. Vogel, H. Choi. EBprotV2: a Perseus plugin for statistical analysis of labeling-based quantitative proteomics data. Submitted.
  2. H.W.L. Koh, D. Fermin, K.P. Choi, R. Ewing, H. Choi. iOmicsPASS: a network-based predictive analysis of clinical multi-omics data. Submitted.
  3. S. Ghosh, A. Datta, K. Tan, H. Choi, SLIDE - a web-based tool for interactive visualization of large-scale -omics data. Bioinformatics. In press.
  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 14:197-209 (2018).
  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 4(3) (2017).
  6. H. Choi, D. Ghosh, Z. Qin. Computationally tractable multivariate HMM in genome-wide mapping studies. Hidden Markov Models: Methods and Protocols, 135-148 (2017).
  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. 89(9), 4897-4906 (2017).
  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. 16(15-16):2238-45 (2016).
  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. 12(1):855 (2016).
  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. 129:108-20 (2015).
  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. 31(22):3645-52 (2015).
  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. 129:121-6 (2015).
  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. 15(15):2580-91 (2015).
  14. D. Fermin, D. Avtonomov, H. Choi*, A. Nesvizhskii*. LuciPHOr2: site localization of generic post-translational modifications from tandem mass spectrometry data. Bioinformatics. 31(7):1141-43. (2014)
  15. G.C. Teo, G. Liu, J. Zhang, A. Nesvizhskii, A-.C. Gingras, H. Choi, SAINTexpress: improvements and additional features in SAINT software. J. Proteomics. 100:37-43. (2014)
  16. G.S. Teo, C. Vogel, D. Ghosh, S. Kim, H. Choi, PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation. J. Proteome Res. 13(1):29-37. (2014)
  17. D. Fermin, S. Walmsley, A-.C. Gingras, H. Choi*, A. Nesvizhskii*, Luciphor: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach, Mol. Cell. Proteomics 12: 3409-3419 (2013).
  18. H. Choi, D. Fermin, A. Nesvizhskii, D. Ghosh, Z.S. Qin, Sparsely correlated hiddenMarkov models with application to genome-wide location studies. Bioinformatics 29(5):533-41 (2013).

Papers with our contribution (data analysis projects)

  1. Lee et al., ABRF Proteome Informatics Research Group (iPRG) 2016 Study: inferring proteoforms from bottom-up proteomics data. J. Biomol. Tech. 2018, jbt. 18-2902-003.
  2. J. Rendleman et al., Quantifying multi-layered expression regulation in response to stress of the endoplasmic reticulum, bioRxiv 308379, 2018.
  3. K.S. 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.
  4. K.S. 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.
  5. C. Windpassinger et al., CDK10 mutations in humans and mice cause severe growth retardation, spine malformations, and developmental delays. AJHG 2017, 101(3), 391-403.
  6. H. Shen et al., IKK2 regulates cytokinesis during vertebrate development. Sci. Rep. 2017, 7(1), 8094.
  7. J.J. 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.
  8. J.D.R. Knight et al., ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nature Methods 2017, 14(7), 645-646.
  9. R.L. Sontag et al., Identification of novel host interactors of effectors secreted by Salmonella and Citrobacter. mSystems. 2017, 1(4): pii:e00032-15.
  10. Y. Lu et al., Metabolic signatures and risk of type 2 diabetes in a Chinese population. 2016, 59(11): 2349-2359.
  11. G. Liu et al., Data independent acquisition analysis in ProHits 4.0. J. Proteomics. 2016, 149:64-68.
  12. Y. 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.
  13. L. Baltazar et al., Antibody binding alters the characteristics and contents of extracellular vesicles release by Histoplasma capsulatum. mSphere 2016, 1(2): e00085-15.
  14. A. Alli Shaik et al., Phosphoprotein network analysis of white adipose tissues unveils deregulated pathways in response to high-fat diet. Sci. Rep. 2016, 6:25844.
  15. G. Liu et al., Data independent acquisition analysis in ProHits 4.0. J. Proteomics 2016, S1874-3919(16):30174-9.
  16. D.Y. Oh et al., Adjuvant-induced human monocyte secretome profiles reveal adjuvant- and age-specific protein signatures. Mol. Cell. Proteomics, 2016, 15(6):1877-1894.
  17. L. Chen et al., Plasma metabonomic profiling of diabetic retinopathy, Diabetes, 2016, 65(4):1099-1108.
  18. G. Liu et al., Gene essentiality is a quantitative property linked to cellular evolvability. Cell 2015, 163(6):1388-99.
  19. Y. 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.
  20. S. 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.
  21. C.P. Lee et al., Mammographic breast density and common genetic variants in breast cancer risk prediction. PLoS One, 2015, 10(9):e0136650.
  22. C. Tsou et al., DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics, Nat. Methods 2015, 12(3):258-64.
  23. J. Knight et al., A web-tool for visualizing quantitative protein-protein interaction data. Proteomics, Proteomics 2014, 15(8):1432-6.
  24. M. Taipale et al., A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways. Cell, 2014, 158(2):434-448.
  25. K. Tan et al., microRNAs in a regenerating lung: an integrative systems biology analysis in a mouse model of influenza pneumonia. BMC Genomics, 2014, 15:587.
  26. T. Narasaraju et al., Combination therapy with hepatocyte growth factor and oseltamivir confers enhanced protection against influenza viral pneumonia. Curr. Mol. Med. 2014, 14(5):690-702.
  27. K. Kim et al., Reinvestigation of aminoacyl-TRNA synthetase core complex by affinity purification-mass spectrometry reveals TARSL2 as a potential member of the complex. PLoS One, 2013, 8(12):e81734.
  28. A. Couzens et al., Protein interaction network of the mammalian HIPPO pathway reveals mechanisms of kinase-phosphatase interactions, Sci. Signal. 2013, 19;6(302).
  29. D. Mellacheruvu et al., The CRAPome: a contaminant repository for affinity purification-mass spectrometry data, Nat. Methods, 2013, 10, 730-736.
  30. E. Bayer-Santos et al., Proteomic analysis of Trypanosoma cruzi secretome: characterization of two populations of extracellular vesicles and soluble protein, J. Proteome Res., 2013, 12(2), 883-97.