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Showing 1 to 12 of 43 entries
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Paediatrics and proteomics: back to the beginning.

Proteomics. Clinical applications

Everett A, Ignjatovic V.
PMID: 25461013
Proteomics Clin Appl. 2014 Dec;8(11):805-6. doi: 10.1002/prca.201470064.

No abstract available.

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Everett A, Ignjatovic V. Paediatrics and proteomics: back to the beginning. Proteomics Clin Appl. 2014;8(11):805-6doi: 10.1002/prca.201470064.
Everett, A., & Ignjatovic, V. (2014). Paediatrics and proteomics: back to the beginning. Proteomics. Clinical applications, 8(11), 805-6. https://doi.org/10.1002/prca.201470064
Everett, Allen, and Ignjatovic, Vera. "Paediatrics and proteomics: back to the beginning." Proteomics. Clinical applications vol. 8,11 (2014): 805-6. doi: https://doi.org/10.1002/prca.201470064
Everett A, Ignjatovic V. Paediatrics and proteomics: back to the beginning. Proteomics Clin Appl. 2014 Dec;8(11):805-6. doi: 10.1002/prca.201470064. PMID: 25461013.
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When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens.

Lab on a chip

Kristina G, Radomir P, Eva B, Lenka D, Radek L, Rostislav V, Borivoj V, Dalibor V.
PMID: 19294317
Lab Chip. 2009 Apr 07;9(7):1014-7. doi: 10.1039/b815503h. Epub 2009 Jan 15.

To improve recovery, selectivity and reproducibility of SELDI-TOF analyses, we found it necessary to modify manufacturer's recommended protocols on sample and chip preparation. To yield reproducible denaturing conditions we verified concentrations of denaturing, reducing and lipid-solubilizing agents. We improved...

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Kristina G, Radomir P, Eva B, et al. When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens. Lab Chip. 2009;9(7):1014-7doi: 10.1039/b815503h.
Kristina, G., Radomir, P., Eva, B., Lenka, D., Radek, L., Rostislav, V., Borivoj, V., & Dalibor, V. (2009). When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens. Lab on a chip, 9(7), 1014-7. https://doi.org/10.1039/b815503h
Kristina, Greplova, et al. "When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens." Lab on a chip vol. 9,7 (2009): 1014-7. doi: https://doi.org/10.1039/b815503h
Kristina G, Radomir P, Eva B, Lenka D, Radek L, Rostislav V, Borivoj V, Dalibor V. When one chip is not enough: augmenting the validity of SELDI-TOF proteomic profiles of clinical specimens. Lab Chip. 2009 Apr 07;9(7):1014-7. doi: 10.1039/b815503h. Epub 2009 Jan 15. PMID: 19294317.
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Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS).

BMC bioinformatics

Staab JM, O'Connell TM, Gomez SM.
PMID: 20214818
BMC Bioinformatics. 2010 Mar 09;11:123. doi: 10.1186/1471-2105-11-123.

BACKGROUND: Nuclear magnetic resonance spectroscopy is one of the primary tools in metabolomics analyses, where it is used to track and quantify changes in metabolite concentrations or profiles in response to perturbation through disease, toxicants or drugs. The spectra...

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Staab JM, O'Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics. 2010;11:123doi: 10.1186/1471-2105-11-123.
Staab, J. M., O'Connell, T. M., & Gomez, S. M. (2010). Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC bioinformatics, 11123. https://doi.org/10.1186/1471-2105-11-123
Staab, Jennifer M, et al. "Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS)." BMC bioinformatics vol. 11 (2010): 123. doi: https://doi.org/10.1186/1471-2105-11-123
Staab JM, O'Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics. 2010 Mar 09;11:123. doi: 10.1186/1471-2105-11-123. PMID: 20214818; PMCID: PMC2851603.
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Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation.

Proteomics

Yin X, Liu X, Zhang Y, Yan G, Wang F, Lu H, Shen H, Yang P.
PMID: 25044409
Proteomics. 2014 Sep;14(17):2008-16. doi: 10.1002/pmic.201300510. Epub 2014 Aug 13.

In this paper, we demonstrate a rapid and reproducible 1D LC-MS/MS workflow for fast quantitative proteomic research. We have optimized the LC-MS/MS conditions, including digestion and gradient conditions, sample loading amount, and MS parameter settings. As a result, we...

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Yin X, Liu X, Zhang Y, et al. Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics. 2014;14(17):2008-16doi: 10.1002/pmic.201300510.
Yin, X., Liu, X., Zhang, Y., Yan, G., Wang, F., Lu, H., Shen, H., & Yang, P. (2014). Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics, 14(17), 2008-16. https://doi.org/10.1002/pmic.201300510
Yin, Xuefei, et al. "Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation." Proteomics vol. 14,17 (2014): 2008-16. doi: https://doi.org/10.1002/pmic.201300510
Yin X, Liu X, Zhang Y, Yan G, Wang F, Lu H, Shen H, Yang P. Rapid and sensitive profiling and quantification of the human cell line proteome by LC-MS/MS without prefractionation. Proteomics. 2014 Sep;14(17):2008-16. doi: 10.1002/pmic.201300510. Epub 2014 Aug 13. PMID: 25044409.
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Construction and completion of flux balance models from pathway databases.

Bioinformatics (Oxford, England)

Latendresse M, Krummenacker M, Trupp M, Karp PD.
PMID: 22262672
Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18.

MOTIVATION: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models...

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Latendresse M, Krummenacker M, Trupp M, et al. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012;28(3):388-96doi: 10.1093/bioinformatics/btr681.
Latendresse, M., Krummenacker, M., Trupp, M., & Karp, P. D. (2012). Construction and completion of flux balance models from pathway databases. Bioinformatics (Oxford, England), 28(3), 388-96. https://doi.org/10.1093/bioinformatics/btr681
Latendresse, Mario, et al. "Construction and completion of flux balance models from pathway databases." Bioinformatics (Oxford, England) vol. 28,3 (2012): 388-96. doi: https://doi.org/10.1093/bioinformatics/btr681
Latendresse M, Krummenacker M, Trupp M, Karp PD. Construction and completion of flux balance models from pathway databases. Bioinformatics. 2012 Feb 01;28(3):388-96. doi: 10.1093/bioinformatics/btr681. Epub 2012 Jan 18. PMID: 22262672; PMCID: PMC3268246.
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Knowledge Translation: Moving Proteomics Science to Innovation in Society.

Omics : a journal of integrative biology

Holmes C, McDonald F, Jones M, Graham J.
PMID: 27223900
OMICS. 2016 Jun;20(6):352-61. doi: 10.1089/omi.2016.0032. Epub 2016 May 25.

Proteomics is one of the pivotal next-generation biotechnologies in the current "postgenomics" era. Little is known about the ways in which innovative proteomics science is navigating the complex socio-political space between laboratory and society. It cannot be assumed that...

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Holmes C, McDonald F, Jones M, et al. Knowledge Translation: Moving Proteomics Science to Innovation in Society. OMICS. 2016;20(6):352-61doi: 10.1089/omi.2016.0032.
Holmes, C., McDonald, F., Jones, M., & Graham, J. (2016). Knowledge Translation: Moving Proteomics Science to Innovation in Society. Omics : a journal of integrative biology, 20(6), 352-61. https://doi.org/10.1089/omi.2016.0032
Holmes, Christina, et al. "Knowledge Translation: Moving Proteomics Science to Innovation in Society." Omics : a journal of integrative biology vol. 20,6 (2016): 352-61. doi: https://doi.org/10.1089/omi.2016.0032
Holmes C, McDonald F, Jones M, Graham J. Knowledge Translation: Moving Proteomics Science to Innovation in Society. OMICS. 2016 Jun;20(6):352-61. doi: 10.1089/omi.2016.0032. Epub 2016 May 25. PMID: 27223900.
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A Glimpse into the Future of Sensing.

ACS sensors

Gooding JJ.
PMID: 29566493
ACS Sens. 2018 Mar 23;3(3):519. doi: 10.1021/acssensors.8b00177.

No abstract available.

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Gooding JJ. A Glimpse into the Future of Sensing. ACS Sens. 2018;3(3):519doi: 10.1021/acssensors.8b00177.
Gooding, J. J. (2018). A Glimpse into the Future of Sensing. ACS sensors, 3(3), 519. https://doi.org/10.1021/acssensors.8b00177
Gooding, J Justin. "A Glimpse into the Future of Sensing." ACS sensors vol. 3,3 (2018): 519. doi: https://doi.org/10.1021/acssensors.8b00177
Gooding JJ. A Glimpse into the Future of Sensing. ACS Sens. 2018 Mar 23;3(3):519. doi: 10.1021/acssensors.8b00177. PMID: 29566493.
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Structural proteomics: methods in deriving protein structural information and issues in data management.

BioTechniques

Mylvaganam SE, Prabhakaran M, Tudor SS, Moezzi S, Ramnarayan K.
PMID: 11906007
Biotechniques. 2002 Mar;42-6.

Structural proteomics is an emerging paradigm that is gaining importance in the post-genomic era as a valuable discipline to process the protein target information being deciphered. The field plays a crucial role in assigning function to sequenced proteins, defining...

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Mylvaganam SE, Prabhakaran M, Tudor SS, et al. Structural proteomics: methods in deriving protein structural information and issues in data management. Biotechniques. 2002;42-6
Mylvaganam, S. E., Prabhakaran, M., Tudor, S. S., Moezzi, S., & Ramnarayan, K. (2002). Structural proteomics: methods in deriving protein structural information and issues in data management. BioTechniques, 42-6.
Mylvaganam, Shankari E, et al. "Structural proteomics: methods in deriving protein structural information and issues in data management." BioTechniques vol. (2002): 42-6.
Mylvaganam SE, Prabhakaran M, Tudor SS, Moezzi S, Ramnarayan K. Structural proteomics: methods in deriving protein structural information and issues in data management. Biotechniques. 2002 Mar;42-6. PMID: 11906007.
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A dream of single-cell proteomics.

Nature methods

Marx V.
PMID: 31406385
Nat Methods. 2019 Sep;16(9):809-812. doi: 10.1038/s41592-019-0540-6.

No abstract available.

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Marx V. A dream of single-cell proteomics. Nat Methods. 2019;16(9):809-812doi: 10.1038/s41592-019-0540-6.
Marx, V. (2019). A dream of single-cell proteomics. Nature methods, 16(9), 809-812. https://doi.org/10.1038/s41592-019-0540-6
Marx, Vivien. "A dream of single-cell proteomics." Nature methods vol. 16,9 (2019): 809-812. doi: https://doi.org/10.1038/s41592-019-0540-6
Marx V. A dream of single-cell proteomics. Nat Methods. 2019 Sep;16(9):809-812. doi: 10.1038/s41592-019-0540-6. PMID: 31406385.
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The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium.

Molecular & cellular proteomics : MCP

Côté RG, Griss J, Dianes JA, Wang R, Wright JC, van den Toorn HW, van Breukelen B, Heck AJ, Hulstaert N, Martens L, Reisinger F, Csordas A, Ovelleiro D, Perez-Rivevol Y, Barsnes H, Hermjakob H, Vizcaíno JA.
PMID: 22949509
Mol Cell Proteomics. 2012 Dec;11(12):1682-9. doi: 10.1074/mcp.O112.021543. Epub 2012 Sep 04.

The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several...

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Côté RG, Griss J, Dianes JA, et al. The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Mol Cell Proteomics. 2012;11(12):1682-9doi: 10.1074/mcp.O112.021543.
Côté, R. G., Griss, J., Dianes, J. A., Wang, R., Wright, J. C., van den Toorn, H. W., van Breukelen, B., Heck, A. J., Hulstaert, N., Martens, L., Reisinger, F., Csordas, A., Ovelleiro, D., Perez-Rivevol, Y., Barsnes, H., Hermjakob, H., & Vizcaíno, J. A. (2012). The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Molecular & cellular proteomics : MCP, 11(12), 1682-9. https://doi.org/10.1074/mcp.O112.021543
Côté, Richard G, et al. "The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium." Molecular & cellular proteomics : MCP vol. 11,12 (2012): 1682-9. doi: https://doi.org/10.1074/mcp.O112.021543
Côté RG, Griss J, Dianes JA, Wang R, Wright JC, van den Toorn HW, van Breukelen B, Heck AJ, Hulstaert N, Martens L, Reisinger F, Csordas A, Ovelleiro D, Perez-Rivevol Y, Barsnes H, Hermjakob H, Vizcaíno JA. The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Mol Cell Proteomics. 2012 Dec;11(12):1682-9. doi: 10.1074/mcp.O112.021543. Epub 2012 Sep 04. PMID: 22949509; PMCID: PMC3518121.
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Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

BMC bioinformatics

Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD.
PMID: 19087345
BMC Bioinformatics. 2008 Dec 16;9:542. doi: 10.1186/1471-2105-9-542.

BACKGROUND: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry...

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Brusniak MY, Bodenmiller B, Campbell D, et al. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008;9:542doi: 10.1186/1471-2105-9-542.
Brusniak, M. Y., Bodenmiller, B., Campbell, D., Cooke, K., Eddes, J., Garbutt, A., Lau, H., Letarte, S., Mueller, L. N., Sharma, V., Vitek, O., Zhang, N., Aebersold, R., & Watts, J. D. (2008). Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC bioinformatics, 9542. https://doi.org/10.1186/1471-2105-9-542
Brusniak, Mi-Youn, et al. "Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics." BMC bioinformatics vol. 9 (2008): 542. doi: https://doi.org/10.1186/1471-2105-9-542
Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008 Dec 16;9:542. doi: 10.1186/1471-2105-9-542. PMID: 19087345; PMCID: PMC2651178.
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Analysis of environmental stress response on the proteome level.

Mass spectrometry reviews

Nesatyy VJ, Suter MJ.
PMID: 18553564
Mass Spectrom Rev. 2008 Nov-Dec;27(6):556-74. doi: 10.1002/mas.20177.

Thousands of man-made chemicals are annually released into the environment by agriculture, transport, industries, and other human activities. In general, chemical analysis of environmental samples used to assess the pollution status of a specific ecosystem is complicated by the...

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Nesatyy VJ, Suter MJ. Analysis of environmental stress response on the proteome level. Mass Spectrom Rev. 2008;27(6):556-74doi: 10.1002/mas.20177.
Nesatyy, V. J., & Suter, M. J. (2008). Analysis of environmental stress response on the proteome level. Mass spectrometry reviews, 27(6), 556-74. https://doi.org/10.1002/mas.20177
Nesatyy, Victor J, and Suter, Marc J-F. "Analysis of environmental stress response on the proteome level." Mass spectrometry reviews vol. 27,6 (2008): 556-74. doi: https://doi.org/10.1002/mas.20177
Nesatyy VJ, Suter MJ. Analysis of environmental stress response on the proteome level. Mass Spectrom Rev. 2008 Nov-Dec;27(6):556-74. doi: 10.1002/mas.20177. PMID: 18553564.
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Showing 1 to 12 of 43 entries