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.
| Published in | Computational Biology and Bioinformatics (Volume 3, Issue 6) |
| DOI | 10.11648/j.cbb.20150306.11 |
| Page(s) | 81-87 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
In-Silico, Hybridization, Transcriptome, Microarray, ChIP-X, Epigenetics, Hybridization-Based, Next Generation Sequencing (NGS)
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APA Style
Taiwo Adigun, Angela Makolo, Segun Fatumo. (2015). Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN). Computational Biology and Bioinformatics, 3(6), 81-87. https://doi.org/10.11648/j.cbb.20150306.11
ACS Style
Taiwo Adigun; Angela Makolo; Segun Fatumo. Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN). Comput. Biol. Bioinform. 2015, 3(6), 81-87. doi: 10.11648/j.cbb.20150306.11
AMA Style
Taiwo Adigun, Angela Makolo, Segun Fatumo. Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN). Comput Biol Bioinform. 2015;3(6):81-87. doi: 10.11648/j.cbb.20150306.11
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Download@article{10.11648/j.cbb.20150306.11,
author = {Taiwo Adigun and Angela Makolo and Segun Fatumo},
title = {Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN)},
journal = {Computational Biology and Bioinformatics},
volume = {3},
number = {6},
pages = {81-87},
doi = {10.11648/j.cbb.20150306.11},
url = {https://doi.org/10.11648/j.cbb.20150306.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20150306.11},
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.},
year = {2015}
}
TY - JOUR T1 - Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN) AU - Taiwo Adigun AU - Angela Makolo AU - Segun Fatumo Y1 - 2015/11/16 PY - 2015 N1 - https://doi.org/10.11648/j.cbb.20150306.11 DO - 10.11648/j.cbb.20150306.11 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 81 EP - 87 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20150306.11 AB - 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. VL - 3 IS - 6 ER -
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