rMTA: robust metabolic transformation analysis.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 11 2019
01 11 2019
Historique:
received:
27
11
2018
revised:
15
03
2019
accepted:
27
03
2019
pubmed:
30
3
2019
medline:
1
7
2020
entrez:
30
3
2019
Statut:
ppublish
Résumé
The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer's disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30923806
pii: 5421511
doi: 10.1093/bioinformatics/btz231
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4350-4355Informations de copyright
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.