Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp PCC 6803 using species-independent text-mining

PeerJ
Sanna M KreulaPatrik R Jones

Abstract

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2...Continue Reading

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Methods Mentioned

BETA
two-hybrid
Y2H

Software Mentioned

CytoscapeRPC
ExpressionCorrelation
Ensembl Genomes
TEES
script
STRING
RegCyanoDB
GBA
SIB Motif Scan
Cytoscape merge

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