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Data analysis workflow for gas chromatography mass spectrometry-based metabolomics studies.

机译：用于基于气相色谱质谱的代谢组学研究的数据分析工作流程。

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摘要

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.
机译：代谢组学已成为系统生物学研究不可或缺的一部分，该研究试图全面研究生物系统中某些条件下的低分子量有机和无机代谢物。过去十年中的技术进步使得使用气相色谱与质谱联用以高通量的方式进行代谢组学研究成为可能。结果，从这些研究中产生了大量数据，并且迫切需要一种能够以高通量方式有效处理和分析数据的算法。为了满足这一需求，我们开发了计算算法和名为“自动数据分析管道（ADAP）”的相关软件工具。 ADAP允许数据无缝地通过数据处理步骤，包括去噪，峰检测，去卷积，对齐，化合物识别和定量。 ADAP的开发始于2009年，过去四年见证了其性能的不断提高，从ADAP-GC 1.0，ADAP-GC 2.0到当前的ADAP-GC 3.0。作为ADAP-GC性能评估的一部分，我们已将其与其他三个软件工具进行了比较。在本文中，我将介绍这三个版本的ADAP-GC的计算细节，软件工具的功能以及软件比较的结果。

著录项

  • 作者

    Ni, Yan.;

  • 作者单位

    The University of North Carolina at Charlotte.;

  • 授予单位 The University of North Carolina at Charlotte.;
  • 学科 Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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