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Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets

Motivation: The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most imp...

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Autores principales: Kotera, Masaaki, Tabei, Yasuo, Yamanishi, Yoshihiro, Tokimatsu, Toshiaki, Goto, Susumu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694648/
https://www.ncbi.nlm.nih.gov/pubmed/23812977
http://dx.doi.org/10.1093/bioinformatics/btt244
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author Kotera, Masaaki
Tabei, Yasuo
Yamanishi, Yoshihiro
Tokimatsu, Toshiaki
Goto, Susumu
author_facet Kotera, Masaaki
Tabei, Yasuo
Yamanishi, Yoshihiro
Tokimatsu, Toshiaki
Goto, Susumu
author_sort Kotera, Masaaki
collection PubMed
description Motivation: The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most important challenge in metabolomics is the automated de novo reconstruction of metabolic pathways, which includes the elucidation of previously unknown reactions to bridge the metabolic gaps. Results: In this article, we develop a novel method to reconstruct metabolic pathways from a large compound set in the reaction-filling framework. We define feature vectors representing the chemical transformation patterns of compound–compound pairs in enzymatic reactions using chemical fingerprints. We apply a sparsity-induced classifier to learn what we refer to as ‘enzymatic-reaction likeness’, i.e. whether compound pairs are possibly converted to each other by enzymatic reactions. The originality of our method lies in the search for potential reactions among many compounds at a time, in the extraction of reaction-related chemical transformation patterns and in the large-scale applicability owing to the computational efficiency. In the results, we demonstrate the usefulness of our proposed method on the de novo reconstruction of 134 metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG). Our comprehensively predicted reaction networks of 15 698 compounds enable us to suggest many potential pathways and to increase research productivity in metabolomics. Availability: Softwares are available on request. Supplementary material are available at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2013/. Contact: goto@kuicr.kyoto-u.ac.jp
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spelling pubmed-36946482013-06-27 Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets Kotera, Masaaki Tabei, Yasuo Yamanishi, Yoshihiro Tokimatsu, Toshiaki Goto, Susumu Bioinformatics Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Motivation: The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most important challenge in metabolomics is the automated de novo reconstruction of metabolic pathways, which includes the elucidation of previously unknown reactions to bridge the metabolic gaps. Results: In this article, we develop a novel method to reconstruct metabolic pathways from a large compound set in the reaction-filling framework. We define feature vectors representing the chemical transformation patterns of compound–compound pairs in enzymatic reactions using chemical fingerprints. We apply a sparsity-induced classifier to learn what we refer to as ‘enzymatic-reaction likeness’, i.e. whether compound pairs are possibly converted to each other by enzymatic reactions. The originality of our method lies in the search for potential reactions among many compounds at a time, in the extraction of reaction-related chemical transformation patterns and in the large-scale applicability owing to the computational efficiency. In the results, we demonstrate the usefulness of our proposed method on the de novo reconstruction of 134 metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG). Our comprehensively predicted reaction networks of 15 698 compounds enable us to suggest many potential pathways and to increase research productivity in metabolomics. Availability: Softwares are available on request. Supplementary material are available at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2013/. Contact: goto@kuicr.kyoto-u.ac.jp Oxford University Press 2013-07-01 2013-06-19 /pmc/articles/PMC3694648/ /pubmed/23812977 http://dx.doi.org/10.1093/bioinformatics/btt244 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany
Kotera, Masaaki
Tabei, Yasuo
Yamanishi, Yoshihiro
Tokimatsu, Toshiaki
Goto, Susumu
Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title_full Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title_fullStr Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title_full_unstemmed Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title_short Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
title_sort supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
topic Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694648/
https://www.ncbi.nlm.nih.gov/pubmed/23812977
http://dx.doi.org/10.1093/bioinformatics/btt244
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