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  • P-ISSN2233-4203
  • E-ISSN2093-8950

Proteome Analysis of Mouse Adipose Tissue and Colon Tissue using a Novel Integrated Data Processing Pipeline

Mass Spectrometry Letters, (P)2233-4203; (E)2093-8950
2014, v.5 no.1, pp.16-23
https://doi.org/10.5478/MSL.2014.5.1.16
Jong-Moon Park (Gachon University)
Na-Young Han (Gachon University)
Kim Hokeun (Korea University)
Injae Hwang (Seoul National University)
Jae Bum Kim (Seoul National University)
Ki-Baik Hahm (CHA University Bundang Medical Center)
Lee Sang-Won (Korea University)
Lee Hookeun (Gachon University)
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Abstract

Liquid chromatography based mass spectrometry (LC-MS) is a key technology for analyzing highly complex anddynamic proteome samples. With highly accurate and sensitive LC-MS analysis of complex proteome samples, efficient dataprocessing is another critical issue to obtain more information from LC-MS data. A typical proteomic data processing starts withprotein database search engine which assigns peptide sequences to MS/MS spectra and finds proteins. Although several searchengines, such as SEQUEST and MASCOT, have been widely used, there is no unique standard way to interpret MS/MS spectraof peptides. Each search engine has pros and cons depending on types of mass spectrometers and physicochemical properties ofpeptides. In this study, we describe a novel data process pipeline which identifies more peptides and proteins by correcting precursorion mass numbers and unifying multi search engines results. The pipeline utilizes two open-source software, iPE-MMRfor mass number correction, and iProphet to combine several search results. The integrated pipeline identified 25% more proteinsin mouse epididymal adipose tissue compared with the conventional method. Also the pipeline was validated using controland colitis induced colon tissue. The results of the present study shows that the integrated pipeline can efficiently identifyincreased number of proteins compared to the conventional method which can be a breakthrough in identification of a potentialbiomarker candidate.

keywords
iPE-MMR iProphet Q-TOF TPP
Submission Date
2014-02-14
Revised Date
2014-03-07
Accepted Date
2014-03-11

Reference

1

Shin, B.. (2008). . Mol. Cell. Proteomics, 7, 1124-.

2

Jung, H. -J.. (2010). . Anal. Chem, 82, 8510-.

3

Mayampurath, A. M.. (2008). . Bioinforma. Oxf. Engl, 24, 1021-.

4

Petyuk, V. A.. (2010). . Mol. Cell. Proteomics, 9, 486-.

5

Shteynberg, D.. (2011). . Mol. Cell. Proteomics, 10, -.

6

Eng, J. K.. (1994). . J. Am. Soc. Mass Spectrom, 5, 976-.

7

Pappin, D. J.. (1993). . J. Curr. Biol, 3, 327-.

8

Bjornson, R. D.. (2008). . Proteome Res, 7, 293-.

9

Keller, A.. (2011). . Methods Mol. Biol, 694, 169-.

10

Pedrioli, P. G. A.. (2004). . Aebersold, R. Nat. Biotechnol, 22, 1459-.

11

Keller, A.. (2002). . Anal. Chem, 74, 5383-.

12

Bao, B.. (2013). . PLoS One, 8, e84075-.

13

Yeo, M.. (2006). . Proteomics, 6, 1158-.

14

Jaitly, N.. (2009). . BMC Bioinformatics, 10, 87-.

15

Angst, B. D.. (2001). . J. Cell Sci, 114, 629-.

16

Bosco, D.. (2007). . J. Endocrinol, 194, 21-.

17

Jiang, H.. (2009). . Diabetes Metab. Res. Rev, 25, 232-.

Mass Spectrometry Letters