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Proteomic Profiling: Data Mining and Analyses

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Application of Clinical Bioinformatics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 11))

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Abstract

Proteomics, the large scale study of proteins, provides a complementary approach to genomics in exploring biological phenomena. With modern development of Mass Spectrometry-based technologies, proteomics has evolved into a powerful analytical platform for life science researchers, and has advanced our understanding of the complex and dynamic nature of proteins. In the clinical field, proteomics studies have been widely applied in identifying biomarkers, monitoring disease status, and assessing treatment effect. In this chapter, an overview of current proteomics profiling is introduced from four perspectives: collecting protein samples with appropriate experimental approaches, characterizing protein features with advanced mass spectrometry-based technologies, annotating protein information with publicly available databases, and interpreting protein functions with bioinformatics analyses. We also give an example of how proteomics research workflow is applied in breast cancer studies.

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Abbreviations

2-DE:

Two-dimensional gel electrophoresis

AP-MS:

Affinity purification coupled with mass spectrometry

BLAST:

Basic local alignment search tool

CDS:

Coding sequences

DE:

Differentially expressed

ES:

Enrichment score

ESI:

Electrospray ionization

FTICR:

Fourier-transform ion cyclotron resonance

GC-MS:

Gas chromatography-mass spectrometry

GO:

Gene ontology

GPMDB:

Global proteome machine database

GSEA:

Gene set enrichment analysis

HGNC:

Hugo gene nomenclature committee

HUPO-PSI:

Hupo proteomics standards initiative

KNN:

K-nearest neighbors

LC-MS:

Liquid chromatography-mass spectrometry

LOWESS:

Locally weighted scatterplot smoothing

LSA:

Least-squares adaptive

MALDI:

Matrix-assisted laser desorption/ionization

MALDI-TOF:

Matrix assisted laser desorption ionization time-of-flight

MIAPE:

Minimum information about a proteomics experiment

MOPED:

Model organism protein expression database

MOWSE:

Molecular weight search

MS:

Mass spectrometry

MSE:

Mass spectrometry with elevated energy

MudPIT:

Multidimensional protein identification technology

PLGEM:

Power law global error model

PLGS:

Proteinlynx global server

PPIN:

Protein-protein interaction network

PPIs:

Protein–protein interactions

PSEA:

Protein set enrichment analysis

PTMs:

Post translational modifications

RPLC:

Reversed-phase liquid chromatography

SAM:

Significance analysis of microarray

SDS-PAGE:

Sodium dodecyl sulfate polyacrylamide gel electrophoresis

SILAC:

Stable isotope labeling by amino acids in cell culture

Spl:

Spectral index

TOF:

Time-of-flight

XML:

Extensible markup language

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Authors and Affiliations

  1. Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA

    Lan Zhang, Wei Zhu, Yong Zeng & Jigang Zhang

  2. College of Life Sciences, Hunan Normal University, Changsha, Hunan, 410081, People’s Republic of China

    Wei Zhu

  3. College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, 100044, China

    Yong Zeng

  4. Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA

    Hong-Wen Deng

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  3. Yong Zeng
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  4. Jigang Zhang
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  1. Shanghai Inst.of Clinical Bioinformatics, Zhongshan Hospital, Fudan University, Shang Hai, China

    Xiangdong Wang

  2. Institute of Health Care Engineering, Graz University of Technology, Graz, Austria

    Christian Baumgartner

  3. School Of Medicine & Medical Science, University College Dublin, Dublin 4, Ireland

    Denis C. Shields

  4. Health and Tropical Medicine, Tulane University School of Public Healt, New Orleans, Louisiana, USA

    Hong-Wen Deng

  5. Section of Clinical Bioinformatics, Swiss Institute of Bioinformatics, Switzerland

    Jacques S Beckmann

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Zhang, L., Zhu, W., Zeng, Y., Zhang, J., Deng, HW. (2016). Proteomic Profiling: Data Mining and Analyses. In: Wang, X., Baumgartner, C., Shields, D., Deng, HW., Beckmann, J. (eds) Application of Clinical Bioinformatics. Translational Bioinformatics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7543-4_6

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