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The Path-A metabolic pathway prediction web server

Pathway Analyst (Path-A) is a publicly available web server () that predicts metabolic pathways. It takes a FASTA format file containing a set of query protein sequences from a single organism (a partial or complete proteome) and identifies those sequences that are likely to participate in any of it...

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Detalles Bibliográficos
Autores principales: Pireddu, Luca, Szafron, Duane, Lu, Paul, Greiner, Russell
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1538809/
https://www.ncbi.nlm.nih.gov/pubmed/16845105
http://dx.doi.org/10.1093/nar/gkl228
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author Pireddu, Luca
Szafron, Duane
Lu, Paul
Greiner, Russell
author_facet Pireddu, Luca
Szafron, Duane
Lu, Paul
Greiner, Russell
author_sort Pireddu, Luca
collection PubMed
description Pathway Analyst (Path-A) is a publicly available web server () that predicts metabolic pathways. It takes a FASTA format file containing a set of query protein sequences from a single organism (a partial or complete proteome) and identifies those sequences that are likely to participate in any of its supported metabolic pathways (currently 10). Path-A uses a number of machine-learning and sequence analysis techniques (e.g. SVM, BLAST and HMM) to predict pathways. Each machine-learned classifier exploits similarity between sequences in the pathways of its model organisms and sequences in the query set. It predicts the pathways that are present in the query organism and annotates each predicted reaction and catalyst, using the appropriate sequences from the query set. Path-A also provides a browsable and searchable database of the pathways for the model organisms that are used to make its predictions. Path-A's predictor sets (using different classifier technologies) have been evaluated using standard cross-validation techniques on a dataset of 10 metabolic pathways across 13 model organisms—a total of 125 organism-specific pathways. The most accurate classifier technology obtained a mean precision of 78.3% and a mean recall of 92.6% in predicting all catalyst proteins, of all reactions, in all pathways present in the dataset. Although Path-A currently only supports metabolic pathways, the underlying prediction techniques are general enough for other types of pathways. Consequently, it is our intent to extend Path-A to predict other types of pathways, including signalling pathways.
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spelling pubmed-15388092006-08-18 The Path-A metabolic pathway prediction web server Pireddu, Luca Szafron, Duane Lu, Paul Greiner, Russell Nucleic Acids Res Article Pathway Analyst (Path-A) is a publicly available web server () that predicts metabolic pathways. It takes a FASTA format file containing a set of query protein sequences from a single organism (a partial or complete proteome) and identifies those sequences that are likely to participate in any of its supported metabolic pathways (currently 10). Path-A uses a number of machine-learning and sequence analysis techniques (e.g. SVM, BLAST and HMM) to predict pathways. Each machine-learned classifier exploits similarity between sequences in the pathways of its model organisms and sequences in the query set. It predicts the pathways that are present in the query organism and annotates each predicted reaction and catalyst, using the appropriate sequences from the query set. Path-A also provides a browsable and searchable database of the pathways for the model organisms that are used to make its predictions. Path-A's predictor sets (using different classifier technologies) have been evaluated using standard cross-validation techniques on a dataset of 10 metabolic pathways across 13 model organisms—a total of 125 organism-specific pathways. The most accurate classifier technology obtained a mean precision of 78.3% and a mean recall of 92.6% in predicting all catalyst proteins, of all reactions, in all pathways present in the dataset. Although Path-A currently only supports metabolic pathways, the underlying prediction techniques are general enough for other types of pathways. Consequently, it is our intent to extend Path-A to predict other types of pathways, including signalling pathways. Oxford University Press 2006-07-01 2006-07-14 /pmc/articles/PMC1538809/ /pubmed/16845105 http://dx.doi.org/10.1093/nar/gkl228 Text en © The Author 2006. Published by Oxford University Press. All rights reserved
spellingShingle Article
Pireddu, Luca
Szafron, Duane
Lu, Paul
Greiner, Russell
The Path-A metabolic pathway prediction web server
title The Path-A metabolic pathway prediction web server
title_full The Path-A metabolic pathway prediction web server
title_fullStr The Path-A metabolic pathway prediction web server
title_full_unstemmed The Path-A metabolic pathway prediction web server
title_short The Path-A metabolic pathway prediction web server
title_sort path-a metabolic pathway prediction web server
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1538809/
https://www.ncbi.nlm.nih.gov/pubmed/16845105
http://dx.doi.org/10.1093/nar/gkl228
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