DATA ANALYSIS WORKFLOW FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS STUDIES

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Ni, Y. (2014). DATA ANALYSIS WORKFLOW FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS STUDIES. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Metabolomics has emerged as an integral part of systems biology research that attempts to comprehensively study low molecular weight organic and inorganic metabolites under certain conditions within a biological system. Technological advances in the past decade have made it possible to carry out metabolomics studies in a high-throughput fashion using gas chromatography coupled with mass spectrometry. As a result, large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze the data in a high-throughput fashion as well. To address this need, we have developed computational algorithms and the associated software tool named an Automated Data Analysis Pipeline (ADAP). ADAP allows data to flow seamlessly through the data processing steps that include de-nosing, peak detection, deconvolution, alignment, compound identification and quantitation. The development of ADAP started in 2009 and the past four years have witnessed continuous improvements in its performance from ADAP-GC 1.0, to ADAP-GC 2.0, and to the current ADAP-GC 3.0. As part of the performance assessment of ADAP-GC, we have compared it with three other software tools. In this dissertation, I will present the computational details about these three versions of ADAP-GC, the capabilities of the software tool, and the results from software comparison.

Details
Author

Ni, Yan

Title

DATA ANALYSIS WORKFLOW FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS STUDIES

Physical Description

1 online resource (119 pages) : PDF

Date

2014

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Metabolomics has emerged as an integral part of systems biology research that attempts to comprehensively study low molecular weight organic and inorganic metabolites under certain conditions within a biological system. Technological advances in the past decade have made it possible to carry out metabolomics studies in a high-throughput fashion using gas chromatography coupled with mass spectrometry. As a result, large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze the data in a high-throughput fashion as well. To address this need, we have developed computational algorithms and the associated software tool named an Automated Data Analysis Pipeline (ADAP). ADAP allows data to flow seamlessly through the data processing steps that include de-nosing, peak detection, deconvolution, alignment, compound identification and quantitation. The development of ADAP started in 2009 and the past four years have witnessed continuous improvements in its performance from ADAP-GC 1.0, to ADAP-GC 2.0, and to the current ADAP-GC 3.0. As part of the performance assessment of ADAP-GC, we have compared it with three other software tools. In this dissertation, I will present the computational details about these three versions of ADAP-GC, the capabilities of the software tool, and the results from software comparison.

Genre

doctoral dissertations

Subjects--Topics

Bioinformatics

Degree

Ph.D.

Keywords

Compound Identification and Quantitation
Data Processing
Deconvolution
Gas Chromatography
Mass Spectrometry
Metabolomics

Subject Area

Information Technology

Advisor(s)

Du, Xiuxia

Committee Members

Fodor, Anthony
Cooper, Brian
Gibas, Cynthia
Mukherjee, Pinku

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2014.

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Identifier

Ni_uncc_0694D_10589

Permalink

http://hdl.handle.net/20.500.13093/etd:1356