KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data
File(s)Liggi2018_Article_KniMetAPipelineForTheProcessin.pdf (894.74 KB)
Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2018-04
Date Acceptance
2018-03-09
Citation
Metabolomics, 2018, 14 (4)
ISSN
1573-3882
Publisher
Springer Verlag
Journal / Book Title
Metabolomics
Volume
14
Issue
4
Copyright Statement
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000427784400002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
Metabolomics
Data processing
GC-MS
LC-MS
Publication Status
Published
Article Number
ARTN 52
Date Publish Online
2018-03-16