Study Sessions on Bioinformatics and Related Topics - Prof. Masaaki Kotera
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Alexis Vandenbon
Aug 21, 2015, 1:30:54 AM8/21/15
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Dear All,
We are planning to have the second presentation in our "Study Sessions on Bioinformatics and Related Topics", as follows:
Speaker: Prof. Masaaki Kotera (Tokyo Institute of Technology)
Preliminary title: "Supervised learning of enzymatic reaction-likeness for
de novo metabolic pathway reconstruction"
Time: September 9th, from 10:00 AM
Place: Meeting Room 1 on the 2nd floor of the IFReC Building
Language: English
We will let you know the final title of the presentation and a summary later.
Anyone who is interested is welcome to join us.
Best regards,
Alex
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VANDENBON Alexis, PhD.
Assistant Professor
Immuno-Genomics Research Unit
IFReC, Osaka University
3-1 Yamada-oka, Suita, Osaka 565-0871, Japan
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Alexis Vandenbon
Aug 28, 2015, 2:46:43 AM8/28/15
to bioinfo-japan-en
Dear All,
For those interested, Prof. Kotera sent us the final title and abstract of his talk:
Title: Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments.
Speaker: Prof. Masaaki Kotera (Tokyo Institute of Technology)
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.
As a reminder, this is the date and place of the presentation:
Time: September 9th, from 10:00 AM
Place: Meeting Room 1 on the 2nd floor of the IFReC Building
Language: English
Best,
Alex
Alexis Vandenbon
Sep 7, 2015, 11:06:36 PM9/7/15
to bioinfo-japan-en
Dear All,
This is a reminder for tomorrow's talk by Prof. Kotera. Everyone is welcome to join.
Time: Sept. 9th, from 10:00 AM
Place: Meeting Room 1 on the 2nd floor of the IFReC Building
Language: English
Title: Metabolome-scale de novo pathway reconstruction using regioisomer-sensitive graph alignments.
Speaker: Prof. Masaaki Kotera (Tokyo Institute of Technology)
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.
Best,
Alex
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