Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN)
Taiwo Adigun,
Angela Makolo,
Segun Fatumo
Issue:
Volume 3, Issue 6, December 2015
Pages:
81-87
Received:
25 September 2015
Accepted:
4 October 2015
Published:
16 November 2015
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.
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]...
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Methods for Evaluating Agglomerative Hierarchical Clustering for Gene Expression Data: A Comparative Study
Md. Bipul Hossen,
Md. Siraj-Ud-Doulah,
Aminul Hoque
Issue:
Volume 3, Issue 6, December 2015
Pages:
88-94
Received:
5 December 2015
Accepted:
14 December 2015
Published:
30 December 2015
Abstract: Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering (AHC) methods is the cluster analysis of gene expression data; however, little work has been done to compare the performance of clustering methods on gene expression data, where some authors used three or four AHC methods and some others used at most five AHC methods. All of the authors concretely suggested complete linkage method to further researchers to determine the best method for clustering their gene expression data. This paper compared the performance of seven AHC methods for clustering gene expression data with respect to five major proximity measures. We used corrected Rand (cR) Index to compare the performance of each clustering method. To illustrate the results, we found that the clustering method Ward exhibited the best performance among all of the AHC methods as well as the proximity measure Cosine performed better in comparison to all the other measures in both type of Affymetrix and cDNA datasets.
Abstract: Microarray is already well established techniques to understand various cellular functions by profiling transcriptomics data. To capture the overall feature of high dimensional variable datasets in microarray data, various analytical and statistical approaches are already developed. One of the most widely used Agglomerative Hierarchical Clustering ...
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