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Items: 1 to 20 of 98

1.

A flexible statistical model for alignment of label-free proteomics data--incorporating ion mobility and product ion information.

Benjamin AM, Thompson JW, Soderblom EJ, Geromanos SJ, Henao R, Kraus VB, Moseley MA, Lucas JE.

BMC Bioinformatics. 2013 Dec 16;14:364. doi: 10.1186/1471-2105-14-364.

2.

MassUntangler: a novel alignment tool for label-free liquid chromatography-mass spectrometry proteomic data.

Ballardini R, Benevento M, Arrigoni G, Pattini L, Roda A.

J Chromatogr A. 2011 Dec 9;1218(49):8859-68. doi: 10.1016/j.chroma.2011.06.062. Epub 2011 Jun 22.

PMID:
21783198
3.

An automated pipeline for high-throughput label-free quantitative proteomics.

Weisser H, Nahnsen S, Grossmann J, Nilse L, Quandt A, Brauer H, Sturm M, Kenar E, Kohlbacher O, Aebersold R, Malmström L.

J Proteome Res. 2013 Apr 5;12(4):1628-44. doi: 10.1021/pr300992u. Epub 2013 Feb 22.

PMID:
23391308
4.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction.

Palmblad M, Mills DJ, Bindschedler LV, Cramer R.

J Am Soc Mass Spectrom. 2007 Oct;18(10):1835-43. Epub 2007 Jul 26.

5.

Optimal precursor ion selection for LC-MALDI MS/MS.

Zerck A, Nordhoff E, Lehrach H, Reinert K.

BMC Bioinformatics. 2013 Feb 18;14:56. doi: 10.1186/1471-2105-14-56.

6.
7.

Comparative LC-MS: a landscape of peaks and valleys.

America AH, Cordewener JH.

Proteomics. 2008 Feb;8(4):731-49. doi: 10.1002/pmic.200700694. Review.

PMID:
18297651
8.

Identification of Yersinia pestis and Escherichia coli strains by whole cell and outer membrane protein extracts with mass spectrometry-based proteomics.

Jabbour RE, Wade MM, Deshpande SV, Stanford MF, Wick CH, Zulich AW, Snyder AP.

J Proteome Res. 2010 Jul 2;9(7):3647-55. doi: 10.1021/pr100402y.

PMID:
20486690
9.

Metaprotein expression modeling for label-free quantitative proteomics.

Lucas JE, Thompson JW, Dubois LG, McCarthy J, Tillmann H, Thompson A, Shire N, Hendrickson R, Dieguez F, Goldman P, Schwarz K, Patel K, McHutchison J, Moseley MA.

BMC Bioinformatics. 2012 May 4;13:74. doi: 10.1186/1471-2105-13-74.

10.

Semi-supervised LC/MS alignment for differential proteomics.

Fischer B, Grossmann J, Roth V, Gruissem W, Baginsky S, Buhmann JM.

Bioinformatics. 2006 Jul 15;22(14):e132-40.

11.

Effects of traveling wave ion mobility separation on data independent acquisition in proteomics studies.

Shliaha PV, Bond NJ, Gatto L, Lilley KS.

J Proteome Res. 2013 Jun 7;12(6):2323-39. doi: 10.1021/pr300775k. Epub 2013 May 2.

PMID:
23514362
12.

ETISEQ--an algorithm for automated elution time ion sequencing of concurrently fragmented peptides for mass spectrometry-based proteomics.

Wong JW, Schwahn AB, Downard KM.

BMC Bioinformatics. 2009 Aug 10;10:244. doi: 10.1186/1471-2105-10-244.

13.

A novel mass spectrometry cluster for high-throughput quantitative proteomics.

Palmblad M, van der Burgt YE, Mostovenko E, Dalebout H, Deelder AM.

J Am Soc Mass Spectrom. 2010 Jun;21(6):1002-11. doi: 10.1016/j.jasms.2010.02.001. Epub 2010 Feb 8.

14.

Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics.

Distler U, Kuharev J, Navarro P, Levin Y, Schild H, Tenzer S.

Nat Methods. 2014 Feb;11(2):167-70. doi: 10.1038/nmeth.2767. Epub 2013 Dec 15.

PMID:
24336358
15.

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features.

Wang B, Zhang J, Chen P, Ji Z, Deng S, Li C.

BMC Bioinformatics. 2013;14 Suppl 8:S9. doi: 10.1186/1471-2105-14-S8-S9. Epub 2013 May 9.

16.
17.

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS.

Shin B, Jung HJ, Hyung SW, Kim H, Lee D, Lee C, Yu MH, Lee SW.

Mol Cell Proteomics. 2008 Jun;7(6):1124-34. doi: 10.1074/mcp.M700419-MCP200. Epub 2008 Feb 25.

18.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

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.

BMC Bioinformatics. 2008 Dec 16;9:542. doi: 10.1186/1471-2105-9-542.

19.
20.

Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry.

Wang B, Valentine S, Plasencia M, Raghuraman S, Zhang X.

BMC Bioinformatics. 2010 Apr 11;11:182. doi: 10.1186/1471-2105-11-182.

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