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Items: 21

1.

An efficient solution for resolving iTRAQ and TMT channel cross-talk.

Searle BC, Yergey AL.

J Mass Spectrom. 2019 Mar 18. doi: 10.1002/jms.4354. [Epub ahead of print]

PMID:
30882954
2.

Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry.

Searle BC, Pino LK, Egertson JD, Ting YS, Lawrence RT, MacLean BX, Villén J, MacCoss MJ.

Nat Commun. 2018 Dec 3;9(1):5128. doi: 10.1038/s41467-018-07454-w.

3.

Data-Independent Acquisition Mass Spectrometry To Quantify Protein Levels in FFPE Tumor Biopsies for Molecular Diagnostics.

Kim YJ, Sweet SMM, Egertson JD, Sedgewick AJ, Woo S, Liao WL, Merrihew GE, Searle BC, Vaske C, Heaton R, MacCoss MJ, Hembrough T.

J Proteome Res. 2019 Jan 4;18(1):426-435. doi: 10.1021/acs.jproteome.8b00699. Epub 2018 Dec 12.

PMID:
30481034
4.

Calibration Using a Single-Point External Reference Material Harmonizes Quantitative Mass Spectrometry Proteomics Data between Platforms and Laboratories.

Pino LK, Searle BC, Huang EL, Noble WS, Hoofnagle AN, MacCoss MJ.

Anal Chem. 2018 Nov 6;90(21):13112-13117. doi: 10.1021/acs.analchem.8b04581. Epub 2018 Oct 23.

PMID:
30350613
5.

Incorporating In-Source Fragment Information Improves Metabolite Identification Accuracy in Untargeted LC-MS Data Sets.

Seitzer PM, Searle BC.

J Proteome Res. 2019 Feb 1;18(2):791-796. doi: 10.1021/acs.jproteome.8b00601. Epub 2018 Oct 18.

PMID:
30295490
6.

PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.

Ting YS, Egertson JD, Bollinger JG, Searle BC, Payne SH, Noble WS, MacCoss MJ.

Nat Methods. 2017 Sep;14(9):903-908. doi: 10.1038/nmeth.4390. Epub 2017 Aug 7.

7.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ.

Mass Spectrom Rev. 2017 Jul 9. doi: 10.1002/mas.21540. [Epub ahead of print] Review.

8.

Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets.

Searle BC, Gittelman RM, Manor O, Akey JM.

Genetics. 2016 Dec;204(4):1391-1396. Epub 2016 Oct 11.

9.

ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

Epstein JA, Blank PS, Searle BC, Catlin AD, Cologna SM, Olson MT, Backlund PS, Coorssen JR, Yergey AL.

Proteomics. 2016 Sep;16(18):2480-90. doi: 10.1002/pmic.201600137.

10.

Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry.

Lawrence RT, Searle BC, Llovet A, Villén J.

Nat Methods. 2016 May;13(5):431-4. doi: 10.1038/nmeth.3811. Epub 2016 Mar 28.

11.

An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies Using Isobaric Tags.

Cologna SM, Crutchfield CA, Searle BC, Blank PS, Toth CL, Ely AM, Picache JA, Backlund PS, Wassif CA, Porter FD, Yergey AL.

J Proteome Res. 2015 Oct 2;14(10):4169-78. doi: 10.1021/acs.jproteome.5b00254. Epub 2015 Sep 3.

12.

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments.

Searle BC, Egertson JD, Bollinger JG, Stergachis AB, MacCoss MJ.

Mol Cell Proteomics. 2015 Sep;14(9):2331-40. doi: 10.1074/mcp.M115.051300. Epub 2015 Jun 22.

13.

A standardized framing for reporting protein identifications in mzIdentML 1.2.

Seymour SL, Farrah T, Binz PA, Chalkley RJ, Cottrell JS, Searle BC, Tabb DL, Vizcaíno JA, Prieto G, Uszkoreit J, Eisenacher M, Martínez-Bartolomé S, Ghali F, Jones AR.

Proteomics. 2014 Nov;14(21-22):2389-99. doi: 10.1002/pmic.201400080. Epub 2014 Sep 23.

14.

Interlaboratory studies and initiatives developing standards for proteomics.

Ivanov AR, Colangelo CM, Dufresne CP, Friedman DB, Lilley KS, Mechtler K, Phinney BS, Rose KL, Rudnick PA, Searle BC, Shaffer SA, Weintraub ST.

Proteomics. 2013 Mar;13(6):904-9. doi: 10.1002/pmic.201200532. Epub 2013 Feb 19.

15.

The mzIdentML data standard for mass spectrometry-based proteomics results.

Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL, Julian R, Binz PA, Deutsch EW, Hermjakob H, Reisinger F, Griss J, Vizcaíno JA, Chambers M, Pizarro A, Creasy D.

Mol Cell Proteomics. 2012 Jul;11(7):M111.014381. doi: 10.1074/mcp.M111.014381. Epub 2012 Feb 27.

16.

A face in the crowd: recognizing peptides through database search.

Eng JK, Searle BC, Clauser KR, Tabb DL.

Mol Cell Proteomics. 2011 Nov;10(11):R111.009522. doi: 10.1074/mcp.R111.009522. Epub 2011 Aug 29. Review.

17.

Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies.

Searle BC.

Proteomics. 2010 Mar;10(6):1265-9. doi: 10.1002/pmic.200900437.

PMID:
20077414
18.

Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies.

Searle BC, Turner M, Nesvizhskii AI.

J Proteome Res. 2008 Jan;7(1):245-53. doi: 10.1021/pr070540w.

PMID:
18173222
19.

Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm.

Searle BC, Dasari S, Wilmarth PA, Turner M, Reddy AP, David LL, Nagalla SR.

J Proteome Res. 2005 Mar-Apr;4(2):546-54.

PMID:
15822933
20.

High-throughput identification of proteins and unanticipated sequence modifications using a mass-based alignment algorithm for MS/MS de novo sequencing results.

Searle BC, Dasari S, Turner M, Reddy AP, Choi D, Wilmarth PA, McCormack AL, David LL, Nagalla SR.

Anal Chem. 2004 Apr 15;76(8):2220-30.

PMID:
15080731
21.

Identification of respiratory vagal feedback in awake normal subjects using pseudorandom unloading.

BuSha BF, Judd BG, Manning HL, Simon PM, Searle BC, Daubenspeck JA, Leiter JC.

J Appl Physiol (1985). 2001 Jun;90(6):2330-40.

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