KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data

Sonia Liggi; Christine Hinz; Zoe Hall; Maria Laura Santoru; Simone Poddighe; John Fjeldsted; Luigi Atzori; Julian L Griffin

doi:10.1007/s11306-018-1349-5

KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data

Sonia Liggi^* (Corresponding Author), Christine Hinz, Zoe Hall, Maria Laura Santoru, Simone Poddighe, John Fjeldsted, Luigi Atzori^* (Corresponding Author), Julian L Griffin^* (Corresponding Author)

^*Corresponding author for this work

The Rowett Institute of Nutrition and Health

Research output: Contribution to journal › Article › peer-review

38 Citations (Scopus)

11 Downloads (Pure)

Abstract

Introduction: Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives: Merge in the same platform the steps required for metabolomics data processing.

Methods: KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results: The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion: KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.

Original language	English
Article number	52
Journal	Metabolomics
Volume	14
Issue number	4
DOIs	https://doi.org/10.1007/s11306-018-1349-5
Publication status	Published - 16 Mar 2018

Bibliographical note

Acknowledgements
We thank Evelina Charidemou for providing some of the example data.

Funding
This study was funded by Agilent Technologies, Regione Autonoma della Sardegna (L.R.7/2007, Grant Number F71J12001180002), and the Medical Research Council UK (Grant Number MR/P011705/1).

Data Availability Statement

KniMet is freely available under the 3-Clause BSD
License at https://github.com/sonial/KniMet along with
usage instructions and example data.

Keywords

Metabolomics
Data processing
GC-MS
LC-MS

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Cite this

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note = "Acknowledgements We thank Evelina Charidemou for providing some of the example data. Funding This study was funded by Agilent Technologies, Regione Autonoma della Sardegna (L.R.7/2007, Grant Number F71J12001180002), and the Medical Research Council UK (Grant Number MR/P011705/1).",

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T2 - a pipeline for the processing of chromatography-mass spectrometry metabolomics data

AU - Liggi, Sonia

AU - Hinz, Christine

AU - Hall, Zoe

AU - Santoru, Maria Laura

AU - Poddighe, Simone

AU - Fjeldsted, John

AU - Atzori, Luigi

AU - Griffin, Julian L

N1 - Acknowledgements We thank Evelina Charidemou for providing some of the example data. Funding This study was funded by Agilent Technologies, Regione Autonoma della Sardegna (L.R.7/2007, Grant Number F71J12001180002), and the Medical Research Council UK (Grant Number MR/P011705/1).

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N2 - Introduction: Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives: Merge in the same platform the steps required for metabolomics data processing.Methods: KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results: The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion: KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.

AB - Introduction: Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives: Merge in the same platform the steps required for metabolomics data processing.Methods: KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results: The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion: KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.

KW - Metabolomics

KW - Data processing

KW - GC-MS

KW - LC-MS

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KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

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Cite this