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CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes

We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and sto...

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Detalles Bibliográficos
Autores principales: Mao, Xizeng, Chen, Xin, Zhang, Yu, Pangle, Spencer, Xu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3517448/
https://www.ncbi.nlm.nih.gov/pubmed/23236458
http://dx.doi.org/10.1371/journal.pone.0051252
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author Mao, Xizeng
Chen, Xin
Zhang, Yu
Pangle, Spencer
Xu, Ying
author_facet Mao, Xizeng
Chen, Xin
Zhang, Yu
Pangle, Spencer
Xu, Ying
author_sort Mao, Xizeng
collection PubMed
description We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.
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spelling pubmed-35174482012-12-12 CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes Mao, Xizeng Chen, Xin Zhang, Yu Pangle, Spencer Xu, Ying PLoS One Research Article We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/. Public Library of Science 2012-12-07 /pmc/articles/PMC3517448/ /pubmed/23236458 http://dx.doi.org/10.1371/journal.pone.0051252 Text en © 2012 Mao et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mao, Xizeng
Chen, Xin
Zhang, Yu
Pangle, Spencer
Xu, Ying
CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title_full CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title_fullStr CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title_full_unstemmed CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title_short CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes
title_sort cinper: an interactive web system for pathway prediction for prokaryotes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3517448/
https://www.ncbi.nlm.nih.gov/pubmed/23236458
http://dx.doi.org/10.1371/journal.pone.0051252
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