Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining
- Published
- Accepted
- Subject Areas
- Computational Biology, Microbiology, Plant Science
- Keywords
- Cyanobacteria, Synechocystis sp. PCC 6803, Network analysis, Text-mining, Systems Biology, Metabolism
- Copyright
- © 2017 Kreula et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining. PeerJ Preprints 5:e3472v1 https://doi.org/10.7287/peerj.preprints.3472v1
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 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) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open source resource.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Individual and merged networks
Cytoscape file containing independent and merged networks. Opens with Cytoscape 3.1
Annotations
Text-file describing the annotations in the Cytoscape files
Script for identifying candidate genes
Python file containing candidate gene script
Script for identifying hypothetical genes
Python file containing hypothetical gene script
Annotations from EVEX
Text-file containing annotations from EVEX/Cyanobase
First neighbor GBA and script-based clusters
Cytoscape file containing the first neighbour GBA and script-based clusters used in the case studies. Opens with Cytoscape 3.1
Cytoscape file with identified motifs
Cytoscape file containing all genes in the genome of Synechocystis 6803 without an annotation that forms a motif with at least two other nodes via at least two different data-types (i.e. edges), of which one is direct and the second is indirect, and at least one of the members of the motif has an existing annotation. Opens with Cytoscape 3.1
List of candidate genes
Text-file containing list of possible candidates of hypotheticals