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

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

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 languageEnglish
Article number52
JournalMetabolomics
Volume14
Issue number4
DOIs
Publication statusPublished - 16 Mar 2018

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