A Pipeline for Metabolic Pathway Reconstruction in Plant Orphan Species

2020 ◽  
pp. 367-380
Author(s):  
Cristina López-Hidalgo ◽  
Mónica Escandón ◽  
Luis Valledor ◽  
Jesus V. Jorrin-Novo
Keyword(s):  
Metabolic Pathway ◽  
Pathway Reconstruction ◽  
Metabolic Pathway Reconstruction

scholarly journals Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Metabolic Pathway ◽  
Software Package ◽  
Genomic Sequence ◽  
Dimensional Space ◽  
Reference Database ◽  
Sequence Information ◽  
Layered Architecture ◽  
Pathway Reconstruction ◽  
Pathway Prediction ◽  
Metabolic Pathway Reconstruction

AbstractMetabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Here, we present pathway2vec, a software package consisting of six representational learning based modules used to automatically generate features for pathway inference. Specifically, we build a three layered network composed of compounds, enzymes, and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve pathway prediction outcomes.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

scholarly journals Leveraging heterogeneous network embedding for metabolic pathway prediction

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J Hallam
Keyword(s):  
Metabolic Pathway ◽  
Software Package ◽  
Genomic Sequence ◽  
Dimensional Space ◽  
Supplementary Information ◽  
Reference Database ◽  
Sequence Information ◽  
Pathway Reconstruction ◽  
Pathway Prediction ◽  
Metabolic Pathway Reconstruction

Abstract Motivation Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Results Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes. Availability and implementation The software package and installation instructions are published on http://github.com/pathway2vec. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Multi-Omics Analysis Pipeline for the Metabolic Pathway Reconstruction in the Orphan Species Quercus ilex

2018 ◽  
Vol 9 ◽  
Author(s):  
Cristina López-Hidalgo ◽  
Victor M. Guerrero-Sánchez ◽  
Isabel Gómez-Gálvez ◽  
Rosa Sánchez-Lucas ◽  
María A. Castillejo-Sánchez ◽  
...  
Keyword(s):  
Metabolic Pathway ◽  
Quercus Ilex ◽  
Analysis Pipeline ◽  
Pathway Reconstruction ◽  
Omics Analysis ◽  
Metabolic Pathway Reconstruction

scholarly journals Exploring Metabolic Pathway Reconstruction and Genome-Wide Expression Profiling in Lactobacillus reuteri to Define Functional Probiotic Features

PLoS ONE ◽  
2011 ◽  
Vol 6 (4) ◽  
pp. e18783 ◽  
Author(s):  
Delphine M. Saulnier ◽  
Filipe Santos ◽  
Stefan Roos ◽  
Toni-Ann Mistretta ◽  
Jennifer K. Spinler ◽  
...  
Keyword(s):  
Metabolic Pathway ◽  
Expression Profiling ◽  
Lactobacillus Reuteri ◽  
Pathway Reconstruction ◽  
Genome Wide ◽  
Metabolic Pathway Reconstruction ◽  
Genome Wide Expression

scholarly journals FMM: a web server for metabolic pathway reconstruction and comparative analysis

2009 ◽  
Vol 37 (suppl_2) ◽  
pp. W129-W134 ◽  
Author(s):  
Chih-Hung Chou ◽  
Wen-Chi Chang ◽  
Chih-Min Chiu ◽  
Chih-Chang Huang ◽  
Hsien-Da Huang
Keyword(s):  
Comparative Analysis ◽  
Metabolic Pathway ◽  
Web Server ◽  
Pathway Reconstruction ◽  
Metabolic Pathway Reconstruction

scholarly journals Metabolic pathway reconstruction of eugenol to vanillin bioconversion in Aspergillus niger

2010 ◽  
Vol 4 (7) ◽  
pp. 320-325 ◽  
Author(s):  
Suchita Srivastava ◽  
Suaib Luqman ◽  
Feroz Khan ◽  
Chandan S. Chanotiya ◽  
Mahendra P. Darokar
Keyword(s):  
Aspergillus Niger ◽  
Metabolic Pathway ◽  
Pathway Reconstruction ◽  
Metabolic Pathway Reconstruction

Metabolic Pathway Reconstruction of Fungal Genomes

2017 ◽  
Vol 23 (4) ◽  
pp. 3796-3801
Author(s):  
Faizah Aplop ◽  
Greg Butler
Keyword(s):  
Metabolic Pathway ◽  
Pathway Reconstruction ◽  
Metabolic Pathway Reconstruction ◽  
Fungal Genomes
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