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Plos One : Cinper ; an Interactive Web System for Pathway Prediction for Prokaryotes, Volume 7

By Ouzounis, Christos, A.

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Book Id: WPLBN0003935095
Format Type: PDF eBook :
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Reproduction Date: 2015

Title: Plos One : Cinper ; an Interactive Web System for Pathway Prediction for Prokaryotes, Volume 7  
Author: Ouzounis, Christos, A.
Volume: Volume 7
Language: English
Subject: Journals, Science, Medical Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary)
Publication Date:
Publisher: Plos


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Ouzounis, C. A. (n.d.). Plos One : Cinper ; an Interactive Web System for Pathway Prediction for Prokaryotes, Volume 7. Retrieved from

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, proteinprotein 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:/


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