Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN)
Computational Biology and Bioinformatics
Volume 3, Issue 6, December 2015, Pages: 81-87
Received: Sep. 25, 2015; Accepted: Oct. 4, 2015; Published: Nov. 16, 2015
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Authors
Taiwo Adigun, Centre for Systems and Information Services, Covenant University, Ota, Nigeria; Bioinformatics Research Group (UNIBReG), University of Ibadan, Ibadan, Nigeria
Angela Makolo, Department of Computer Science, University of Ibadan, Ibadan, Nigeria; Bioinformatics Research Group (UNIBReG), University of Ibadan, Ibadan, Nigeria
Segun Fatumo, H3Africa Bioinformatics Network (H3ABioNet) Node, National Biotechnology Development Agency (NABDA), Federal Ministry of Science and Technology (FMST), Abuja, Nigeria
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Abstract
Understanding Gene Regulatory Network (GRN) is considered to be the fundamental approach to many biological questions, and the input dataset performs a crucial role in investigating and visualizing the gene regulatory network [5, 14, 17, 23, 34, 37, 40, 41, 44, 45]. Several software tools [2, 5, 7, 10, 11, 14, 21, 22, 25, 31-33, 37, 38, 40, 41, 44] have recently been developed for GRN inference, where some are designed for a particular dataset, an organism or a particular diseased cell. The questions that prompted this review are; what is (are) the kind of omic data needed to construct a GRN? Is there any peculiar property attached to a GRN of a particular data? And, could there be an integration of data from various omic experiments in form of a knowledge base? The input dataset for GRN are transcriptome information which is analyzed comprehensively including the two major technologies (sources) that produce them. We consider four omic datasets and two of their sources for the purpose of this review. The biological data source technologies are hybridization-based, and sequence-based. Dataset from microarray and ChIP-Chip experiments are hybridization-based while RNA-seq and ChIP-seq are sequence-based. Software tools published on Omic Tool website (http://omictools.com/gene-regulatory-networks-c435-p1.html) are analyzed for this review. However, the major disparity is whether the dataset is ChIP-X (ChIP-Chip and ChIP-seq) or expression (Microarray and RNA-seq) dataset not whether the source is from hybridization-based or sequence-based. Moreover, ChIP-X dataset gives more opportunity to investigate more biological problems. The importance of gene regulatory network suggests a GRN software template, which contains all the additional data from ChIP-X experiment and a knowledge base of biological prior knowledge, including integration of data from different omic datasets as a single knowledge base.
Keywords
In-Silico, Hybridization, Transcriptome, Microarray, ChIP-X, Epigenetics, Hybridization-Based, Next Generation Sequencing (NGS)
To cite this article
Taiwo Adigun, Angela Makolo, Segun Fatumo, Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN), Computational Biology and Bioinformatics. Vol. 3, No. 6, 2015, pp. 81-87. doi: 10.11648/j.cbb.20150306.11
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Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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