| Peer-Reviewed

Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN)

Received: 25 September 2015     Accepted: 4 October 2015     Published: 16 November 2015
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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.

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

Keywords

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

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    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

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    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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  • @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}
    }
    

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  • 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
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    N1  - https://doi.org/10.11648/j.cbb.20150306.11
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    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
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    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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Author Information
  • Centre for Systems and Information Services, Covenant University, Ota, Nigeria

  • Department of Computer Science, University of Ibadan, Ibadan, Nigeria

  • H3Africa Bioinformatics Network (H3ABioNet) Node, National Biotechnology Development Agency (NABDA), Federal Ministry of Science and Technology (FMST), Abuja, Nigeria

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