Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Anne-Christin Hauschild, Till Schneider, Josch Pauling, Kathrin Rupp, Mi Jang, Jörg Ingo Baumbach, Jan Baumbach

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

OriginalsprogEngelsk
TidsskriftMetabolites
Vol/bind2
Udgave nummer4
Sider (fra-til)733-755
Antal sider23
ISSN2218-1989
DOI
StatusUdgivet - 2012

Fingeraftryk

Computational methods
Spectrometry
Cells
Ions
Volatile Organic Compounds
Data handling
Biomarkers
Processing
Learning systems
Statistical methods
Visualization
Information Storage and Retrieval
Health
Statistical Models
Scanning
Data storage equipment
Molecules
Air
Cell Line
Metabolomics

Citer dette

Hauschild, Anne-Christin ; Schneider, Till ; Pauling, Josch ; Rupp, Kathrin ; Jang, Mi ; Baumbach, Jörg Ingo ; Baumbach, Jan. / Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art. I: Metabolites. 2012 ; Bind 2, Nr. 4. s. 733-755.
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abstract = "Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.",
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Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art. / Hauschild, Anne-Christin; Schneider, Till; Pauling, Josch; Rupp, Kathrin; Jang, Mi; Baumbach, Jörg Ingo; Baumbach, Jan.

I: Metabolites, Bind 2, Nr. 4, 2012, s. 733-755.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

AU - Hauschild, Anne-Christin

AU - Schneider, Till

AU - Pauling, Josch

AU - Rupp, Kathrin

AU - Jang, Mi

AU - Baumbach, Jörg Ingo

AU - Baumbach, Jan

PY - 2012

Y1 - 2012

N2 - Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

AB - Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

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DO - 10.3390/metabo2040733

M3 - Journal article

C2 - 24957760

VL - 2

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JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 4

ER -