Publications

Short list: Quantitative mass spectrometry-based proteomics

  1. T. Huang et al. “Statistical detection of differentially abundant proteins in experiments with repeated measures designs and isobaric labeling”. Journal of Proteome Research, 22:2641, 2023. [LINK]
  2. D. Kohler et al. “MSstats version 4.0: statistical analyses of quantitative mass spectrometry-based proteomic experiments with chromatography-based quantification at scale”. Journal of Proteome Research, 22:1466, 2023. [LINK]
  3. L. Malinovska et al. “Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications”, Nature Protocols, 18:659, 2023. [LINK]
  4. D. Kohler et al. “MSstatsShiny: a GUI for versatile, scalable, and reproducible statistical analyses of quantitative proteomic experiments”, Journal of Proteome Research, 22:551, 2023. [LINK]
  5. D. Kohler et al. “MSstatsPTM: Statistical relative quantification of posttranslational modifications in bottom-up mass spectrometry-based proteomics”, Molecular & Cellular Proteomics, 22:100477, 2022. [LINK]
  6. M. Choi et al. “MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets”, Nature Methods, 17:981, 2020. [LINK]
  7. T. Huang et al. “MSstatsTMT: Statistical detection of differentially abundant proteins in experiments with isobaric labeling and multiple mixtures”, Molecular & Cellular Proteomics, mcp.RA120.002105, 2020. [LINK]
  8. T.-H. Tsai et al. “Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments”. Molecular & Cellular Proteomics, mcp.RA119.001792, 2020. [LINK]
  9. E. Dogu et al. “MSstatsQC 2.0: R/Bioconductor package for statistical quality control of mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 18:678, 2019. [LINK]
  10. C. Galitzine et al. “Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays”, Molecular & Cellular Proteomics, RA117.000322, 2018. [LINK]
  11. T.-H. Tsai et al. “Statistical characterization of therapeutic protein modifications”. Scientific Reports, 7, 7896, 2017. [LINK]
  12. A. L. Oberg, O. Vitek. “Statistical design of quantitative mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 8:2144, 2009 [LINK].

Short list: Mass spectrometry-based imaging

  1. K. A. Bemis et al. “Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis”, Nature Methods, in press, 2023. [LINK]
  2. D. Guo et al. “A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images”. In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 39:btad067, 2023. [LINK]
  3. M. C. Föll et al. “Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework”, Clinical Proteomics, 19:8, 2022. [LINK]
  4. D. Guo et al. “Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), 36:i300, 2020. [LINK]
  5. D. Guo et al. “Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), 35:i208, 2019. [LINK]
  6. K. A. Bemis et al. “Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs”, International Journal of Mass Spectrometry, 437:49, 2019. [LINK]
  7. K. A. Bemis, O. Vitek. “Matter: an R package for rapid prototyping with larger-than-memory datasets on disk”. Bioinformatics, 33:3142, 2017. [LINK]
  8. K. Bemis et al. “Probabilistic segmentation of mass spectrometry images helps select important ions and characterize confidence in the resulting segments”. Molecular & Cellular Proteomics, MCP.O115.053918, 2016. [LINK]
  9. K. D. Bemis et al. “Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments”. Bioinformatics, 31:2418, 2015. [LINK]

Short list: Causal inference in biomolecular systems

  1. . Mohammad-Taheri et al. “Optimal adjustment sets for causal query estimation in partially observed biomolecular networks”. In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 39:i494, 2023. [LINK]
  2. S. Mohammad-Taheri et al. “Do-calculus enables estimation of causal effects in partially observed biomolecular pathways”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 38:i350 2022. [LINK]
  3. J. Zucker et al. “Leveraging structured biological knowledge for counterfactual inference: a case study of viral pathogenesis”. IEEE Transactions on Big Data, 7:25, 2021. [LINK]
  4. R. O. Ness et al. “Integrating mechanistic and structural causal models enables counterfactual inference in complex systems” In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 14211, 2019. [LINK]
  5. C. Galitzine et al. “Statistical inference of peroxisome dynamics”. In: Proceedings of Research in Computational Molecular Biology (RECOMB). Lecture Notes in Computer Science, vol 10812:54. Springer, 2018. [LINK]
  6. R. O. Ness et al. “A Bayesian active learning experimental design for inferring signaling networks”. In: Proceedings of Research in Computational Molecular Biology (RECOMB). Lecture Notes in Computer Science, 10229:134, 2017. [LINK]
  7. R. Ness et al. “From correlation to causality: statistical approaches to learning regulatory relationships in large-scale biomolecular investigations”. Journal of Proteome Research, 15:683, 2016. [LINK]

 

 

All publications:

  1. K. A. Bemis, M. C. Föll, D. Guo, S. S. Lakkimsetty, O. Vitek. “Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis”, Nature Methods, in press, 2023. [LINK]
  2. M. Buljan, A. Banaei-Esfahani, P. Blattmann, F. Meier-Abt, W. Shao, O. Vitek, H. Tang, R. Aebersold. “A computational framework for the inference of protein complex remodeling from whole-proteome measurements”, Nature Methods, in press, 2023. [LINK]
  3. T. Huang, M. Staniak, F. Veiga Leprevost, A. Figueroa-Navedo, A. Ivanov, A. Nesvizhskii, M. Choi, O. Vitek. “Statistical detection of differentially abundant proteins in experiments with repeated measures designs and isobaric labeling”. Journal of Proteome Research, 22:2641, 2023. [LINK]
  4. S. Mohammad-Taheri, V. Tewari, R. Kapre, E. Rahiminasab, K. Sachs, C. T. Hoyt, J. Zucker, O. Vitek. “Optimal adjustment sets for causal query estimation in partially observed biomolecular networks”. In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 39:i494, 2023. [LINK]
  5. D. Kohler, M. Staniak, T.-H. Tsai, T. Huang, N. Shulman, O. M. Bernhardt, B. X. MacLean, A. I. Nesvizhskii, L. Reiter, E. Sabidó, M. Choi, O. Vitek. “MSstats version 4.0: statistical analyses of quantitative mass spectrometry-based proteomic experiments with chromatography-based quantification at scale”. Journal of Proteome Research, 22:1466, 2023. [LINK]
  6. L. Malinovska, V. Cappelletti, D. Kohler, I. Piazza, T.-H. Tsai, M. Pepelnjak, P. Stalder, C. Dörig, F. Sesterhenn, F. Elsässer, L. Kralickova, N. Beaton, L. Reiter, N. de Souza, O. Vitek, P. Picotti. “Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications”, Nature Protocols, 18:659, 2023. [LINK]
  7. D. Guo, M. C. Föll, K. A. Bemis, O. Vitek. “A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images”. In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 39:btad067, 2023. [LINK]
  8. D. Kohler, M. Kaza, C. Pasi, T. Huang, M. Staniak, D. Mohandas, E. Sabidó, M. Choi, O. Vitek. “MSstatsShiny: a GUI for versatile, scalable, and reproducible statistical analyses of quantitative proteomic experiments”, Journal of Proteome Research, 22:551, 2023. [LINK]
  9. D. Kohler, T.-H. Tsai, E. Verschueren, T. Huang, T. Hinkle, L. Phu, M. Choi, O. Vitek. “MSstatsPTM: Statistical relative quantification of posttranslational modifications in bottom-up mass spectrometry-based proteomics”, Molecular & Cellular Proteomics, 22:100477, 2022. [LINK]
  10. S. Mohammad-Taheri, J. Zucker, C. Tapley Hoyt, K. Sachs, V. Tewari, R. Ness, O. Vitek. “Do-calculus enables estimation of causal effects in partially observed biomolecular pathways”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 38:i350 2022. [LINK]
  11. M. C. Föll, V. Volkmann, K. Enderle-Ammour, S. Timme, K. Wilhelm, D. Guo, O. Vitek, P. Bronsert, O. Schilling. “Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework”, Clinical Proteomics, 19:8, 2022. [LINK]
  12. L. South, D. Saffo, O. Vitek , C. Dunne, M. A. Borkin. “Effective use of Likert scales in visualization evaluations”, In proceedings of Eurographics Conference on Visualization (EuroVis), Computer Graphics, 41:3, 2022. [LINK]
  13. A. N. Marsh, V. Sharma, S. K. Mani, O. Vitek, M. J. MacCoss, B. X. MacLean. “Skyline Batch: An intuitive user interface for batch processing with Skyline”, Journal of Proteome Research, 21:289, 2021. [LINK]
  14. J. Zucker, K. Paneri, S. Mohammad-Taheri, S. Bhargava, P. Kolambkar, C. Bakker, J. Teuton, C. T. Hoyt, K. Oxford, R. Ness, O. Vitek. “Leveraging structured biological knowledge for counterfactual inference: a case study of viral pathogenesis”. IEEE Transactions on Big Data, 7:25, 2021. [LINK]
  15. T. Maculins, E. Verschueren, T. Hinkle, M. Choi, P. Chang, C. Chalouni, S. Rao, Y. Kwon, J. Lim, A. K. Katakam, R. C. Kunz, B. K. Erickson, T. Huang, T.-H. Tsai, O. Vitek, M. Reichelt, Y. Senbabaoglu, B. Mckenzie, J. R. Rohde, I. Dikic, D. S. Kirkpatrick, A. Murthy. “Multiplexed proteomics of autophagy-deficient murine macrophages reveals enhanced antimicrobial immunity via the oxidative stress response”. Elife, 10:e62320, 2021. [LINK]
  16. M. Choi, J. Carver, C. Chiva, M. Tzouros, T. Huang, T.-H. Tsai, B. Pullman, O. M. Bernhardt, R. Hüttenhain, G. C. Teo, Y. Perez-Riverol, J. Muntel, M. Müller, S. Goetze, M. Pavlou, E. Verschueren, B. Wollscheid, A. I. Nesvizhskii, L. Reiter, T. Dunkley, E. Sabidó, N. Bandeira, O. Vitek. “MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets”, Nature Methods, 17:981, 2020. [LINK]
  17. T. Huang, M. Choi, M. Tzouros, S. Golling, N. J. Pandya, B. Banfai, T. Dunkley, O. Vitek. “MSstatsTMT: Statistical detection of differentially abundant proteins in experiments with isobaric labeling and multiple mixtures”, Molecular & Cellular Proteomics, mcp.RA120.002105, 2020. [LINK]
  18. Y. Peng, S. Jain, Y. Li, M. Gregus, A. Ivanov, O. Vitek, P. Radivojac. “New mixture models for decoy-free false discovery rate estimation in mass-spectrometry proteomics”, In: Proceedings of European Conference on Computational Biology (ECCB), Bioinformatics, Vol. 36, Suppl 2:i745, 2020. [LINK]
  19. F. Cerciello, M. Choi, S. L. Sinicropi-Yao, K. Lomeo, J. M. Amann, E. Felley-Bosco, R. A. Stahel, B. W. S. Robinson, J. Creaney, H. I. Pass, O. Vitek, D. P Carbone. “Verification of a blood based targeted proteomics signature for malignant pleural mesothelioma”, Cancer Epidemiology, Biomarkers & Prevention, 29:1973, 2020.  [LINK]
  20. D. Guo, M. Föll, V. Volkmann, K. Enderle-Ammour, P. Bronsert, O. Schilling, O. Vitek. “Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), 36:i300, 2020. [LINK]
  21. T.-H. Tsai, M. Choi, B. Banfai, Y. Liu, B. X. MacLean, T. Dunkley, O. Vitek. “Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments”. Molecular & Cellular Proteomics, mcp.RA119.001792, 2020. [LINK]
  22. T. Huang, R. Bruderer, J. Muntel, Y. Xuan, O. Vitek, L. Reiter “Combining precursor and fragment information for improved detection of differential abundance in data independent acquisition”, Molecular & Cellular Proteomics, mcp.RA119.001705, 2020. [LINK]
  23. R. O. Ness, K. Paneri, O. Vitek. “Integrating mechanistic and structural causal models enables counterfactual inference in complex systems” In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 14211, 2019. [LINK]
  24. R. Hüttenhain, M. Choi, L. M. de la Fuente, K. Oehl, C.-Y. Chang, A.-K. Zimmermann, S. Malander, H. Olsson, S. Surinova, T. Clough, V. Heinzelmann-Schwarz, P. J. Wild, D. Dinulescu, E. Niméus, O. Vitek and R. Aebersold. “A targeted mass spectrometry strategy for developing proteomic biomarkers: a case study of epithelial ovarian cancer”. Molecular & Cellular Proteomics, mcp.RA118.001221, 2019. [LINK]
  25. D. Guo, K. Bemis, C. Rawlins, J. Agar, O. Vitek. “Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues”, In: Proceedings of Intelligent Systems for Molecular Biology (ISMB), 35:i208, 2019. [LINK]
  26. E. D. Berger, C. Hollenbeck, P. Maj, O. Vitek, J. Vitek. “On the impact of programming languages on code quality”, ACM Tansactions on Programming Languages (TOPLAS), 41:21, 2019. [LINK]
  27. M. Schwab, S. Hao, O. Vitek, J. Tompkin, J. Huang, M. A. Borkin. “Evaluating pan and zoom timelines and sliders”, Human-Computer Interaction (CHI), Paper No. 556, 2019. [LINK]
  28. D. Amodei, J. Egertson, B. MacLean, R. Johnson, G. E. Merrihew, A. Keller, D. Marsh, O. Vitek, P. Mallick, M. J. MacCoss. “Improving precursor selectivity in data independent acquisition using overlapping windows”. Journal of The American Society for Mass Spectrometry, 30:669, 2019. [LINK]
  29. J. Muntel, J. Kirkpatrick, R. Bruderer, T. Huang, O. Vitek, A. Ori, L. Reiter. “Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time”, Journal of Proteome Research, 18:1340, 2019. [LINK]
  30. E. Dogu, S. Mohammad-Taheri, R. Olivella, F. Marty, I. Lienert, L. Reiter, E. Sabidó, O. Vitek. “MSstatsQC 2.0: R/Bioconductor package for statistical quality control of mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 18:678, 2019. [LINK]
  31. K. A. Bemis, D. Guo, A. J. Harry, M. Thomas, I. Lanekoff, M. P. Stenzel-Poore, S. L. Stevens, J. Laskin, O. Vitek. “Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs”, International Journal of Mass Spectrometry, 437:49, 2019. [LINK]
  32. P. M. J. Beltran, K. C. Cook, Y. Hashimoto, C. Galitzine, L. A. Murray, O. Vitek, I. M. Cristea. “Infection-induced peroxisome biogenesis is a metabolic strategy for herpesvirus replication”, Cell Host & Microbe, 24:526, 2018. [LINK]
  33. N. Atallah, O. Vitek, F. Gaiti, M. Tanurdzič, J. A. Banks. “Sex determination in Ceratopteris richardii is accompanied by transcriptome changes that drive epigenetic reprogramming of the young gametophyte”, G3: Genes, Genomes, Genetics,  8:2205, 2018. [LINK]
  34. C. Galitzine, P. J. Beltran, I. M. Cristea, O. Vitek. “Statistical inference of peroxisome dynamics”. In: Proceedings of Research in Computational Molecular Biology (RECOMB). Lecture Notes in Computer Science, vol 10812:54. Springer, 2018. [LINK]
  35. C. Galitzine, J. D. Egertson, S. Abbatiello, C. M. Henderson, A. N. Hoofnagle, M. MacCoss, O. Vitek. “Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays”, Molecular & Cellular Proteomics, RA117.000322, 2018. [LINK]
  36. T.-H. Tsai, Z. Hao, Q. Hong, B. Moore, C. Stella, J. H. Zhang, Y. C, M. Kim, T. Koulis, G. A. Ryslik, E. Verschueren, F. Jacobson, W. E. Haskins, O. Vitek. “Statistical characterization of therapeutic protein modifications”. Scientific Reports, 7, 7896, 2017. [LINK]
  37. K. A. Bemis, O. Vitek. “Matter: an R package for rapid prototyping with larger-than-memory datasets on disk”. Bioinformatics, 33:3142, 2017. [LINK]
  38. E. Dogu, S. Mohammad-Taheri, S. E. Abbatiello, M. S. Bereman, B. MacLean, B. Schilling, O. Vitek. “MSstatsQC: Longitudinal system suitability monitoring and quality control for targeted proteomic experiments”. Molecular & Cellular Proteomics, M116.064774, 2017. [LINK]
  39. R. O. Ness, K. Sachs, P. Mallick, O. Vitek. “A Bayesian active learning experimental design for inferring signaling networks”. In: Proceedings of Research in Computational Molecular Biology (RECOMB). Lecture Notes in Computer Science, 10229:134, 2017. [LINK]
  40. M. Choi, Z. F. Eren-Dogu, C. M. Colangelo, J. S. Cottrell, M. R. Hoopmann, E. A. Kapp, S. Kim, H. Lam, T. A. Neubert, M. Palmblad, B. S. Phinney, S. T. Weintraub, B. MacLean, O. Vitek. “ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of differentially abundant proteins in label-free quantitative LC-MS/MS experiments”. Journal of Proteome Research, 16:945, 2017. [LINK]
  41. C. Terfve, E. Sabidó, Y. Wu, E. Gonçalves, M. Choi, S. Vaga, O. Vitek, J. Saez-Rodriguez, R. Aebersold. “System-wide quantitative proteomics of the metabolic syndrome in mice: genotypic and dietary effects”. Journal of Proteome Research, 16:831, 2017. [LINK]
  42. S. van de Ven, K. Bemis, K. Lau, R. Adusumilli, U. Kota, M. Stolowitz, O. Vitek, P. Mallick, S. Gambhir. “Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection”. Proteomics, 16:1660, 2016.[LINK]
  43. K. Bemis, A. Harry, L. S. Eberlin, C. R. Ferreira, S. M. van de Ven, P. Mallick, M. Stolowitz, O. Vitek. “Probabilistic segmentation of mass spectrometry images helps select important ions and characterize confidence in the resulting segments”. Molecular & Cellular Proteomics, MCP.O115.053918, 2016. [LINK]
  44. R. Ness, K. Sachs, O. Vitek. “From correlation to causality: statistical approaches to learning regulatory relationships in large-scale biomolecular investigations”. Journal of Proteome Research, 15:683, 2016. [LINK]
  45. E. Borràs, E. Cantó, M. Choi, L. M. Villar, J. C. Álvarez-Cermeño, C. Chiva, X. Montalban, O. Vitek, M. Comabella, E. Sabidó. “Protein-based classifier to predict conversion from clinically isolated syndrome to multiple sclerosis”. Molecular & Cellular Proteomics, M115.053256, 2016. [LINK]
  46. S. Surinova, M. Choi, S. Tao, P. J. Schüffler, C.-Y. Chang, T. Clough, K. Vysloužil, M. Khoylou, J. Srovnal, Y. Liu, M. Matondo, R. Hüttenhain, H. Weisser, J. M. Buhmann, M. Hajdúch, H. Brenner, O. Vitek, R. Aebersold. “Prediction of colorectal cancer diagnosis based on circulating plasma proteins”. EMBO Molecular Medicine, 8:1361, 2015. [LINK]
  47. S. Surinova, L. Radová, M. Choi, J. Srovnal, H. Brenner, O. Vitek, M. Hajdúch, R. Aebersold. “Non-invasive prognostic protein biomarker signatures associated with colorectal cancer”. EMBO Molecular Medicine, 7:1153, 2015. [LINK]
  48. M. J. Rardin, B. Schilling, Lin.-Y. Cheng, B. X. MacLean, D. J. Sorenson, A. K. Sahu, M. J. MacCoss, O. Vitek and B. W. Gibson. “MS1 peptide ion intensity chromatograms in MS2 (SWATH) data independent acquisitions: Improving post acquisition analysis of proteomic experiments”. Molecular & Cellular Proteomics, O115.048181, 2015. [LINK]
  49. A. Palmer, E. Ovchinnikova, M. Thune, R. Lavigne, B. Guevel, A. Dyatlov, O. Vitek, C. Pineau, M. Boren, T. Alexandrov. “Using collective expert judgements to evaluate quality measures of mass spectrometry images”. Bioinformatics 31:i375, 2015. [LINK]
  50. K. D. Bemis, A. Harry, L. S. Eberlin, C. Ferreira, S. M. van de Ven, P. Mallick, M. Stolowitz, O. Vitek. “Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments”. Bioinformatics, 31:2418, 2015. [LINK]
  51. R. Bruderer, O. M. Bernhardt, T. Gandhi, S. M. Miladinovic, L.-Y. Cheng, S. Messner, T. Ehrenberger, V. Zanotelli, Y. Butscheid, C. Escher, O. Vitek, O. Rinner, L. Reiter. “Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen treated 3D liver microtissues”. Molecular & Cellular Proteomics, M114.044305, 2015. [LINK]
  52. Y. Liu, A. Buil, B. C. Collins, L. C. J. Gillet, L. C. Blum, L.‐Y. Cheng, O. Vitek, J. Mouritsen, G. Lachance, T. D. Spector, E. T. Dermitzakis, R. Aebersold. “Quantitative variability of 342 plasma proteins in a human twin population”. Molecular Systems Biology, 11:786, 2015. [LINK]
  53. N. Selevsek, C.-Y. Chang, L. C. Gillet, P. Navarro, O. M. Bernhardt, L. Reiter, L.-Y. Cheng, O. Vitek, R. Aebersold. “Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-MS”. Molecular & Cellular Proteomics, M113.035550, 2015. [LINK]
  54. M. Choi, C.-Y. Chang, T. Clough, D. Broudy, T. Killeen, B. MacLean, O. Vitek. “MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments”. Bioinformatics, 30:2524, 2014. [LINK]
  55. D. Broudy, T. Killeen, M. Choi, N. Shulman, D. R. Mani, S. E. Abbatiello, D. Mani, R. Ahmad, A. K. Sahu, B. Schilling, K. Tamura, Y. Boss, V. Sharma, B. W. Gibson, S. A. Carr, O. Vitek, M. J. MacCoss, B. MacLean. “A framework for installable external tools in Skyline”. Bioinformatics, 30:2521, 2014. [LINK]
  56. C.-Y. Chang, E. Sabidó,R. Aebersold, O. Vitek. “Targeted protein quantification using sparse reference labeling”. Nature Methods, 11:301, 2014. [LINK]
  57. S. Carr, S. E. Abbatiello. B. L. Ackermann, C. Borchers, B. Domon, E. W. Deutsch, R. P. Grant, A. N. Hoofnagle, R. Hüttenhain, J. M. Koomen, D. C. Liebler, T. Liu, B. MacLean, D. R. Mani, E. Mansfield, H. Neubert, A. G. Paulovich, L. Reiter, O. Vitek, R. Aebersold, L. Anderson, R. Bethem, J. Blonder, E. Boja, J. Botelho, M. Boyne, R. A. Bradshaw, A. L. Burlingame, D. Chan, H. Keshishian, E. Kuhn, C. Kinsinger, J. Lee, S.-W. Lee, R. Moritz, J. Oses-Prieto, N. Rifai, J. Ritchie, H. Rodriguez, P. R. Srinivas, R.R. Townsend, J. Van Eyk, G. Whiteley, A. Wiita, S. Weintraub. “Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach”. Molecular & Cellular Proteomics, M113.036095, 2014. [LINK]
  58. F. Cerciello, M. Choi, A. Nicastri, D. Bausch-Fluck, A. Ziegler, O. Vitek, E. Felley-Bosco, R. Stahel, R. Aebersold, B. Wollscheid. “Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring”. Clinical Proteomics, 10:16, 2013. [LINK]
  59. S. Surinova, R. Hüttenhain, C.-Y. Chang, L. Espona, O. Vitek, R. Aebersold. “Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies”. Nature Protocols, 8:1602, 2013. [LINK]
  60. E. Sabidó, Y. Wu, L. Bautista, T. Porstmann, C.-Y. Chang, O. Vitek, M. Stoffel, R. Aebersold. “Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet”. Molecular Systems Biology, 9:681, 2013. [LINK]
  61. D. Yu, W. Huber, O. Vitek. “Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size”. Bioinformatics, 29:1275, 2013. [LINK]
  62. D. Yu, J. M. C. Danku, I. Baxter, S. Kim, O. K. Vatamaniuk, O. Vitek, D. E. Salt. “High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome”. BMC Genomics, 13:623, 2012. [LINK]
  63. T. Clough, S. Thaminy, S. Ragg, R. Aebersold, O. Vitek. “Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs”. BMC Bioinformatics, 13:S16, 2012. [LINK]
  64. K. Ma, O. Vitek, A. I. Nesvizhskii. “A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet”.BMC Bioinformatics, 13:S16, 2012. [LINK]
  65. J. K. Muhlemann, H. Maeda, C.-Y. Chang, P. San Miguel, I. Baxter, B. Cooper, M. A. Perera, B. J. Nikolau, O. Vitek, J. A. Morgan, N. Dudareva. “Developmental changes in the metabolic network of snapdragon flowers”. PLoS One, 7(7):e40381, 2012. [LINK]
  66. E. Sabidó, O. Quehenberger, Q. Shen, C.-Y. Chang, I. Shah, A. M. Armando, A. Andreyev, O. Vitek, E. A. Dennis, R. Aebersold. “Targeted proteomics of the Eicosanoid biosynthetic pathway completes an integrated genomics-proteomics-metabolomics picture of cellular metabolism”. Molecular & Cellular Proteomics, 11:M111.014746, 2012. [LINK]
  67. C.-Y. Chang, P. Picotti, R. Hüttenhain, V. Heinzelmann-Schwarz, M. Jovanovic, R. Aebersold, O. Vitek. “Protein significance analysis in selected reaction monitoring (SRM) measurements”. Molecular & Cellular Proteomics, 11:M111.014662, 2012. [LINK]
  68. T. Ye, C. Zheng, S. Zhang, G. A. N. Gowda, O. Vitek, D. Raftery. “Add to Subtract: A simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures”. Analytical Chemistry, 84:994, 2012. [LINK,NEWS]
  69. L. Käll, O. Vitek. “Computational mass spectrometry-based proteomics”. PLoS Computational Biology, 7:e1002277, 2011. [LINK]
  70. L. S. Riter, P. K. Jensen, J. M. Ballam, E. Urbanczyk-Wochniak, T. Clough, O. Vitek, J. Sutton, M. Athanas, M. F. Lopez, S. MacIsaac. “Evaluation of label-free quantitative proteomics in a plant matrix: A case study of the night-to-day transition in corn leaf”.Analytical Methods, 3:2733, 2011. [LINK]
  71. D. Yu, J. Danku, I. Baxter, S. Kim, O. K. Vatamaniuk, D. E. Salt, O. Vitek. “Noise reduction in genome-wide perturbation screens using linear mixed-effect models”. Bioinformatics, 27:2173, 2011. [LINK]
  72. T. Clough, S. Braun, V. Fokin, I. Ott, S. Ragg, G. Schadow, O. Vitek. “Statistical design and analysis of label-free LC-MS proteomic experiments: A case study of coronary artery disease”. Methods in Molecular Biology, 728:293, 2011. [LINK]
  73. C. Zheng, S. Zhang, S. Ragg, D. Raftery, O. Vitek. “Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection”. Bioinformatics, 27:1637, 2011. [LINK]
  74. A. L. Dill, L. S. Eberlin, A. B. Costa, C. Zheng, D. R. Ifa, L. Cheng, T. A. Masterson, M. O. Koch, O. Vitek, R. G. Cooks. “Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry”. Chemistry: A European Journal, 11:2897, 2011. [LINK]
  75. B. Bodenmiller, S. Wanka, C. Kraft, J. Urban, D. Campbell, P. Pedrioli, B. Gerrits,  P. Picotti, H. Lam, O. Vitek, M.-Y. Brusniak, B. Roschitzki, C. Zhang, R. Schlapbach, K. Shokat, A. Colman-Lerner, A. Nesvizhskii, M. Peter, R. Loewith, C. von Mering and R. Aebersold.”Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast”. Science Signaling, 3:rs4, 2010. [LINK]
  76. I. Baxter, J. N. Brazelton, D. Yu, Y. S. Huang, B. Lahner, E. Yakubova, Y. Li, J. Bergelson, J. O. Borevitz, M. Nordborg, O. Vitek, D. E. Salt. “A coastal cline in sodium accumulation in Arabidopsis thaliana is driven by natural variation of the sodium transporter AtHKT1;1″. PLoS Genetics, 6:e1001193, 2010. [LINK,NEWS]
  77. A. L. Dill, L. S. Eberlin, C. Zheng, A. B. Costa, D. R. Ifa, L. Cheng, T. A. Masterson, M. O. Koch, O. Vitek and R. G. Cooks. “Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry”.Analytical and Bioanalytical Chemistry, 398:2969, 2010. [LINK]
  78. T. Clough, M. Key, I. Ott, S. Ragg, G. Schadow, O. Vitek. “Protein quantification in label-free LC-MS experiments”. Journal of Proteome Research, 8:5275, 2009. [LINK]
  79. C. Sherwood, A. Eastham, L. W. Lee, J. Risler, O. Vitek, D. B. Martin. “Correlation between y-type ions observed in ion trap and triple quadrupole mass spectrometers”. Journal of Proteome Research, 8:4243, 2009. [LINK]
  80. S. Zhang, C. Zheng, I. R. Lanza, K. S. Nair, D. Raftery, O. Vitek. “Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling”. Analytical Chemistry, 81:6080, 2009. [LINK]
  81. O. Vitek. “Getting started in computational mass spectrometry-based proteomics”. PLoS Computational Biology, 5:e1000366, 2009. [LINK]
  82. A. L. Oberg, O. Vitek. “Statistical design of quantitative mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 8:2144, 2009 [LINK].
  83. M.-Y. Brusniak, B. Bodenmiller, D. Campbell, K. Cooke, J. Eddes, A. Garbutt, H. Lau, S. Letarte, L. N. Mueller, V. Sharma, O. Vitek, N. Zhang, R. Aebersold, J. D. Watts. “Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics”. BMC Bioinformatics, 9:542, 2008 [LINK]
  84. S. Letarte, M.-Y. Brusniak, D. Campbell, J. Eddes, C. J. Kemp, H. Lau, L. Mueller, A. Schmidt, P. Shannon, K. S. Kelly-Spratt, O. Vitek, H. Zhang, R. Aebersold and J. D. Watts. “Differential plasma glycoproteome of p19ARF skin cancer mouse model using the Corra label-free LC-MS proteomics platform”. Proteomics: Clinical Applications, 4:105, 2008 [LINK].
  85. I. R. Baxter, O. Vitek, B. Lahner, B. Muthukumar, M. Borghi, J. Morrissey, M. L. Guerinot, D. E. Salt. “The leaf ionome as a multivariable system to detect a plant’s physiological status”. Proceedings of the National Academy of Sciences, 105:12081, 2008 [LINK].
  86. L. Hohmann, J. Eng, A. Gemmill, J. Klimek, O. Vitek, G. Reid, D. Martin. “Quantification of the compositional information provided by immonium ions on a quadrupole-TOF mass spectrometer”. Analytical Chemistry, 80:5596, 2008. [LINK]
  87. A. Nesvizhskii, O. Vitek, R. Aebersold. “Analysis and validation of proteomic data generated by tandem mass spectrometry”. Nature Methods, 4:787, 2007. [LINK]
  88. L. N. Mueller, O. Rinner, A. Schmidt, S. Letarte, B. Bodenmiller, M.-Y. Brusniak, O. Vitek, R. Aebersold, M. Muller. “SuperHirn – a novel tool for high resolution LC-MS based peptide/protein profiling”. Proteomics, 7:3470, 2007. [LINK]
  89. O. Vitek, C. Bailey-Kellogg, B. A. Craig, J. Vitek. “Inferential backbone assignment for sparse data”. Journal of Biomolecular NMR, 31:187, 2006. [LINK]
  90. Z. Yi, O. Vitek, M. A. Qasim, S. M. Lu, W. Lu, M. Ranjbar, J. Li, M. C. Laskowski, C. Bailey-Kellogg, M. Laskowski. “Functional evolution within a protein superfamily”. Proteins, 63:697, 2006. [LINK]
  91. O. Vitek, C. Bailey-Kellogg, B. A. Craig, P. Kuliniewicz, J. Vitek. “Reconsidering complete search algorithms for protein backbone NMR assignment”. Bioinformatics, 21:ii230, 2005. [LINK]
  92. L. S. Riter, O. Vitek, K. M. Gooding, B. D. Hodge, R. K. Julian, Jr. “Statistical design of experiments as a tool in mass spectrometry”.Journal of Mass Spectrometry, 40:565, 2005. [LINK]
  93. O. Vitek, J. Vitek, B. A. Craig, C. Bailey-Kellogg. “Model-based assignment and inference of protein backbone nuclear magnetic resonances”. Statistical Applications in Genetics and Molecular Biology, 3:Article 6, 2004. [LINK]
  94. J. C. Fleet, L. Wang, O. Vitek, B. A. Craig, H. J. Edenberg. “Gene expression profiling of Caco-2 BBe cells suggests a role for specific signaling pathways during intestinal differentiation”. Physiological Genomics, 13:57, 2003. [LINK]
  95. T. V. Perneger, A.-C. Rae, J.-M. Gaspoz, F. Borst, O. Vitek, C. Heliot. “Screening for pressure ulcer risk in an acute care hospital: Development of a brief bedside scale”. Journal of Clinical Epidemiology, 55:498, 2002. [LINK]
  96. B. A. Craig, O. Vitek, M. A. Black, M. Tanurdzic, R. W. Doerge. “Designing microarrays”. InProceedings of Applied Statistics in Agriculture, Kansas State University, edited by George Milliken, p.159, 2001. [LINK]
  97. S. Beer-Borst, A. Morabia, S. Hercberg, O. Vitek, M. S. Bernstein, P. Galan, R. Galasso, S. Giampaoli, S. Houterman, E. McCrum, S. Panico, F. Pannozzo, P. Preziosi, L. Ribas, L. Serra-Majem, W. M. M. Verschuren, J. Yarnell, M. E. Northridge. “Obesity and other health determinants across Europe: The Euralim Project”. Journal of Epidemiology and Community Health, 54:424, 2000. [LINK]
  98. S. Beer-Borst, S. Hercberg, A. Morabia, M. S. Bernstein, P. Galan, R. Galasso, S. Giampaoli, E. McCrum, S. Panico, P. Preziosi, L. Ribas, L. Serra-Majem, M. F. Vescio, O. Vitek, J. Yarnell, M. E. Northridge. “Dietary patterns in six European populations: results from Euralim, a collaborative European data harmonization and information campaign”. European Journal of Clinical Nutrition, 54:253, 2000. [LINK]