Paper Info
Title
Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets.
Abstract
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.
Year
DOI
Venue
2013
10.1093/bioinformatics/btt244
BIOINFORMATICS
DocType
Volume
Issue
Journal
29
13
ISSN
Citations 
PageRank 
1367-4803
11
0.52
References 
Authors
12
5
Name
Order
Citations
PageRank
Masaaki Kotera128523.48
Yasuo Tabei221519.46
Yoshihiro Yamanishi3126883.44
Toshiaki Tokimatsu452039.56
Susumu Goto53213541.48