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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
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  • Research Article   
  • J Proteomics Bioinform,
  • DOI: 10.4172/0974-276X.1000496

Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical Considerations

Sudhir Srivastava1,2, Michael Merchant3,4, Anil Rai1 and Shesh N. Rai2,5*
1Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
2Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, United States of America
3Department of Medicine, University of Louisville, Louisville, Kentucky, United States of America
4Department of Pharmacology &Toxicology, University of Louisville, Louisville, Kentucky, United States of America
5Biostatistics and Bioinformatics Facility, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, United States of America
*Corresponding Author : Shesh N. Rai, Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, United States of America, Tel: + 502-852-4030, Fax: + 502-852-7979, Email: [email protected]

Received Date: Mar 01, 2019 / Accepted Date: Mar 27, 2019 / Published Date: Apr 04, 2019

Abstract

Introduction: The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS.

Methods: We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability.

Results and Conclusion: We found that the FFPE TSM is better than the FR TSM. We also found that the onestep TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.

Keywords: ANOVA; Imputation; Proteins; Tissue storage; Tissue extraction; Technical variability

Citation: Srivastava S, Merchant M, Rai A, Rai SN (2019) Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical Considerations. J Proteomics Bioinform 12: 048-055. Doi: 10.4172/0974-276X.1000496

Copyright: © 2019 Srivastava S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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