Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining
| Autor: | Suwisa Kaewphan, Sanna M Kreula, Filip Ginter, Patrik R. Jones |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Candidate gene Computer science Association (object-oriented programming) Systems biology lcsh:Medicine Computational biology Cyanobacteria General Biochemistry Genetics and Molecular Biology 03 medical and health sciences Resource (project management) ta113 Synechocystis sp. PCC 6803 030102 biochemistry & molecular biology General Neuroscience lcsh:R ta1183 GRASP ta1182 General Medicine Construct (python library) Text-mining 030104 developmental biology Metabolism Filter (video) Network analysis General Agricultural and Biological Sciences |
| Zdroj: | PeerJ, Vol 6, p e4806 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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