A simple approach for representation of Gene Regulatory Networks (GRN)

Raza-ul-Haq, Javed Ferzund, Shahid Hussain

Research output: Contribution to journalArticlepeer-review

Abstract

Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions; however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.

Original languageEnglish (US)
Pages (from-to)288-292
Number of pages5
JournalInternational Journal of Advanced Computer Science and Applications
Volume9
Issue number11
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • General Computer Science

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