Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments. (10th June 2015)
- Record Type:
- Journal Article
- Title:
- Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments. (10th June 2015)
- Main Title:
- Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments
- Authors:
- Yamanishi, Yoshihiro
Tabei, Yasuo
Kotera, Masaaki - Abstract:
- Abstract : Motivation: Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized. Results: In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework. We proposed a novel chemical graph alignment algorithm, which we called PACHA (Pairwise Chemical Aligner), to detect the regioisomer-sensitive connectivities between the aligned substructures of two compounds. Unlike other existing graph alignment methods, PACHA can efficiently detect only one common subgraph between two compounds. Our results show that the proposed method outperforms previous descriptor-based methods or existing graph alignment-based methods in the enzymatic reaction-likeness prediction for isomer-enriched reactions. It is also useful for reaction annotation that assigns potential reaction characteristics such as EC (Enzyme Commission) numbers and PIERO (Enzymatic Reaction Ontology for Partial Information) terms to substrate–product pairs. Finally, we conducted a comprehensive enzymatic reaction-likeness prediction for all possible uncharacterized compound pairs, suggesting potential metabolic pathways for newly predicted substrate–product pairs.Abstract : Motivation: Recent advances in mass spectrometry and related metabolomics technologies have enabled the rapid and comprehensive analysis of numerous metabolites. However, biosynthetic and biodegradation pathways are only known for a small portion of metabolites, with most metabolic pathways remaining uncharacterized. Results: In this study, we developed a novel method for supervised de novo metabolic pathway reconstruction with an improved graph alignment-based approach in the reaction-filling framework. We proposed a novel chemical graph alignment algorithm, which we called PACHA (Pairwise Chemical Aligner), to detect the regioisomer-sensitive connectivities between the aligned substructures of two compounds. Unlike other existing graph alignment methods, PACHA can efficiently detect only one common subgraph between two compounds. Our results show that the proposed method outperforms previous descriptor-based methods or existing graph alignment-based methods in the enzymatic reaction-likeness prediction for isomer-enriched reactions. It is also useful for reaction annotation that assigns potential reaction characteristics such as EC (Enzyme Commission) numbers and PIERO (Enzymatic Reaction Ontology for Partial Information) terms to substrate–product pairs. Finally, we conducted a comprehensive enzymatic reaction-likeness prediction for all possible uncharacterized compound pairs, suggesting potential metabolic pathways for newly predicted substrate–product pairs. Contact: maskot@bio.titech.ac.jp … (more)
- Is Part Of:
- Bioinformatics. Volume 31:Number 12(2015)
- Journal:
- Bioinformatics
- Issue:
- Volume 31:Number 12(2015)
- Issue Display:
- Volume 31, Issue 12 (2015)
- Year:
- 2015
- Volume:
- 31
- Issue:
- 12
- Issue Sort Value:
- 2015-0031-0012-0000
- Page Start:
- i161
- Page End:
- i170
- Publication Date:
- 2015-06-10
- Subjects:
- Bioinformatics -- Periodicals
Genomics -- Data processing -- Periodicals
Computational biology -- Periodicals
572.80285 - Journal URLs:
- http://bioinformatics.oxfordjournals.org ↗
http://firstsearch.oclc.org ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/bioinformatics/btv224 ↗
- Languages:
- English
- ISSNs:
- 1367-4803
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2072.348000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12388.xml