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Items: 1 to 20 of 97

1.

Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins.

Lee HY, Kim EG, Jung HR, Jung JW, Kim HB, Cho JW, Kim KM, Yi EC.

Sci Rep. 2019 Sep 20;9(1):13653. doi: 10.1038/s41598-019-49665-1.

2.

Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics.

Langley SR, Mayr M.

J Proteomics. 2015 Nov 3;129:83-92. doi: 10.1016/j.jprot.2015.07.012. Epub 2015 Jul 18.

PMID:
26193490
3.

The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results.

Braisted JC, Kuntumalla S, Vogel C, Marcotte EM, Rodrigues AR, Wang R, Huang ST, Ferlanti ES, Saeed AI, Fleischmann RD, Peterson SN, Pieper R.

BMC Bioinformatics. 2008 Dec 9;9:529. doi: 10.1186/1471-2105-9-529.

4.

Improved LC-MS/MS spectral counting statistics by recovering low-scoring spectra matched to confidently identified peptide sequences.

Zhou JY, Schepmoes AA, Zhang X, Moore RJ, Monroe ME, Lee JH, Camp DG, Smith RD, Qian WJ.

J Proteome Res. 2010 Nov 5;9(11):5698-704. doi: 10.1021/pr100508p. Epub 2010 Oct 4.

5.

A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification.

Tu C, Shen S, Sheng Q, Shyr Y, Qu J.

J Proteomics. 2017 Jan 30;152:276-282. doi: 10.1016/j.jprot.2016.11.020. Epub 2016 Nov 27.

PMID:
27903464
6.

Cross-correlation of spectral count ranking to validate quantitative proteome measurements.

Kannaste O, Suomi T, Salmi J, Uusipaikka E, Nevalainen O, Corthals GL.

J Proteome Res. 2014 Apr 4;13(4):1957-68. doi: 10.1021/pr401096z. Epub 2014 Mar 26.

PMID:
24611565
7.

Enhanced peptide quantification using spectral count clustering and cluster abundance.

Lee S, Kwon MS, Lee HJ, Paik YK, Tang H, Lee JK, Park T.

BMC Bioinformatics. 2011 Oct 28;12:423. doi: 10.1186/1471-2105-12-423.

8.

Detecting differential and correlated protein expression in label-free shotgun proteomics.

Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF.

J Proteome Res. 2006 Nov;5(11):2909-18.

PMID:
17081042
9.

Critical assessment of proteome-wide label-free absolute abundance estimation strategies.

Ahrné E, Molzahn L, Glatter T, Schmidt A.

Proteomics. 2013 Sep;13(17):2567-78. doi: 10.1002/pmic.201300135. Epub 2013 Jul 30.

PMID:
23794183
10.

NSI and NSMT: usages of MS/MS fragment ion intensity for sensitive differential proteome detection and accurate protein fold change calculation in relative label-free proteome quantification.

Wu Q, Zhao Q, Liang Z, Qu Y, Zhang L, Zhang Y.

Analyst. 2012 Jul 7;137(13):3146-53. doi: 10.1039/c2an35173k. Epub 2012 May 14.

PMID:
22582177
11.

Spectral counting robust on high mass accuracy mass spectrometers.

Hoehenwarter W, Wienkoop S.

Rapid Commun Mass Spectrom. 2010 Dec 30;24(24):3609-14. doi: 10.1002/rcm.4818.

PMID:
21108307
12.

freeQuant: A Mass Spectrometry Label-Free Quantification Software Tool for Complex Proteome Analysis.

Deng N, Li Z, Pan C, Duan H.

ScientificWorldJournal. 2015;2015:137076. doi: 10.1155/2015/137076. Epub 2015 Nov 8.

13.

Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies.

Milac TI, Randolph TW, Wang P.

Stat Interface. 2012;5(1):75-87.

14.

Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.

Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianférani S, Ferro M, Van Dorssaeler A, Burlet-Schiltz O, Schaeffer C, Couté Y, Gonzalez de Peredo A.

J Proteomics. 2016 Jan 30;132:51-62. doi: 10.1016/j.jprot.2015.11.011. Epub 2015 Nov 14.

PMID:
26585461
15.

Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.

Wolahan SM, Hirt D, Glenn TC.

In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25.

16.

Multiparameter Optimization of Two Common Proteomics Quantification Methods for Quantifying Low-Abundance Proteins.

Zhang C, Shi Z, Han Y, Ren Y, Hao P.

J Proteome Res. 2019 Jan 4;18(1):461-468. doi: 10.1021/acs.jproteome.8b00769. Epub 2018 Nov 26.

PMID:
30394099
17.

ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments.

Choi M, Eren-Dogu ZF, Colangelo C, Cottrell J, Hoopmann MR, Kapp EA, Kim S, Lam H, Neubert TA, Palmblad M, Phinney BS, Weintraub ST, MacLean B, Vitek O.

J Proteome Res. 2017 Feb 3;16(2):945-957. doi: 10.1021/acs.jproteome.6b00881. Epub 2017 Jan 3.

PMID:
27990823
18.

A multi-model statistical approach for proteomic spectral count quantitation.

Branson OE, Freitas MA.

J Proteomics. 2016 Jul 20;144:23-32. doi: 10.1016/j.jprot.2016.05.032. Epub 2016 May 31.

19.

Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes.

Dicker L, Lin X, Ivanov AR.

Mol Cell Proteomics. 2010 Dec;9(12):2704-18. doi: 10.1074/mcp.M110.002774. Epub 2010 Sep 7.

20.

The proteomic advantage: label-free quantification of proteins expressed in bovine milk during experimentally induced coliform mastitis.

Boehmer JL, DeGrasse JA, McFarland MA, Tall EA, Shefcheck KJ, Ward JL, Bannerman DD.

Vet Immunol Immunopathol. 2010 Dec 15;138(4):252-66. doi: 10.1016/j.vetimm.2010.10.004. Epub 2010 Oct 14. Review.

PMID:
21067814

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