Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.

ABSTRACT:

Introduction

To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids.

Objectives

To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver.

Methods

Our matching method is more flexible than an exact identifier-

SUBMITTER: Poupin N 

PROVIDER: S-EPMC7096385 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature