KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-04

AUTHORS

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

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. More... »

PAGES

52

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-018-1349-5

DOI

http://dx.doi.org/10.1007/s11306-018-1349-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101551569

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29576760


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