Abstract
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/.
Highlights
The availability of large-scale omic data has allowed accurate elucidation of biological pathways for different prokaryotic organisms in a systematic manner, which has led to the development of a number of pathway databases [1] such as KEGG [2,3], BioCyc [4] and SEED [5]
In the one-click mode, a user just enters a description about the target process for a selected organism using a set of keywords; and CINPER will predict a pathway model by automatically carrying out the following steps: (i) retrieve relevant template pathways by searching the entered keywords against the BioCyc [21] and SEED [5] databases; (ii) map the retrieved template pathways to the target genome to give the initial network prediction; and (iii) expand the initial network to fill pathway holes and to recruit additional genes based on operon information retrieved from the DOOR [22] operon database and proteinprotein interaction, co-evolution and co-expression information from the STRING [23] functional network database
CINPER provides a utility tool for extracting gene annotation and reference information from the NCBI Genome and PubMed databases and for adding the interactions for any gene pair in the template if they are functionally linked in the pathway models of KEGG [2,3] or BioCyc [21]
Summary
The availability of large-scale omic data has allowed accurate elucidation of biological pathways for different prokaryotic organisms in a systematic manner, which has led to the development of a number of pathway databases [1] such as KEGG [2,3], BioCyc [4] and SEED [5] These databases provide highly useful information for prediction of biological pathways for prokaryotic organisms in general as the same or similar biological processes across related organisms are typically executed through homologous pathways. A process for pathway reconstruction may take a trained biologist a substantial amount of time and effort to do in an iterative fashion
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