Analysis of Gene Expression Patterns Using Biclustering

Roy, Swarup; Bhattacharyya, Dhruba K.; Kalita, Jugal

doi:10.1007/7651_2015_280

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…The global Eigenvector centrality is a high score of 0.95852 because the hub genes are highly connected among themselves. [28,29,3,30,31]. The node centrality was computed with the help of the four popular centrality measures, namely, degree, betweenness, closeness and eigenvector centrality.…”

Section: Inference and Analysis Of Genome Scale Ad Affected Networkmentioning

confidence: 99%

“…System and molecular biological research over the years have shifted the focus from analyzing individual components to investigating biomolecular networks. High-throughput experimental methods have made it easier to study biomolecular networks by providing enormous data about interactions, networks, functional modules [3,4], and pathways. Gene Regulatory Networks (GRNs) offer a lot of insight into the mechanisms of the complex cellular systems and hence the inference of GRN is indispensable in the quest to comprehend genes, their functions as well as their relationship with other genes.…”

Section: Introductionmentioning

confidence: 99%

“…The copyright holder for this preprint (which this version posted July 12, 2021. ; https://doi.org/10.1101/2021.07.11.451988 doi: bioRxiv preprint [28,29,3,30,31]. The node centrality was computed with the help of the four popular centrality measures, 535 namely, degree, betweenness, closeness and eigenvector centrality.…”

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confidence: 99%

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Parallel Framework for Inferring Genome Scale Gene Regulatory Networks

Sebastian

Roy

2021

Preprint

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Genome-scale network inference is essential to understand comprehensive interaction patterns. Current methods are limited to the reconstruction of small to moderate-size networks. The most obvious alternative is to propose a novel method or alter existing methods that may leverage parallel computing paradigms. Very few attempts also have been made to re-engineer existing methods by executing selective iterative steps concurrently. We propose a generic framework in this paper that leverages parallel computing without re-engineering the original methods. The proposed framework uses state-of-the-art methods as a black box to infer sub-networks of the segmented data matrix. A simple merger was designed based on preferential attachment to generate the global network by merging the sub-networks.Fifteen (15) inference methods were considered for experimentation. Qualitative and speedup analysis was carried out using DREAM challenge networks. The proposed framework was implemented on all the 15 inference methods using large expression matrices. The results were auspicious as we could infer large networks in reasonable time without compromising the qualitative aspects of the original (serial) algorithm.CLR, the top performer, was then used to infer the network from the expression profiles of an Alzheimer’s disease (AD) affected mouse model consisting of 45,101 genes. We have also highlighted few hub genes from the network that are functionally related to various diseases.

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Section: Inference and Analysis Of Genome Scale Ad Affected Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel Framework for Inferring Genome Scale Gene Regulatory Networks

Sebastian

Roy

2021

Preprint

Self Cite

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“…Li et al (2015) stated that genes having similar expression profiles are usually connected by the interaction in the network. The co-expressed genes are predicted to have the same roles and involved in the same biological processes (Roy et al 2016). All genes obtained from KEGG and AraCyc databases were used as an input to generate the coexpression network using three co-expression databases; i.e., GeneMANIA (Warde-Farley et al 2010), STRING version 10.0 (Szklarczyk et al 2017) and ATTED-II version 8.0 (Obayashi et al 2007).…”

Section: Co-expression Network Analysismentioning

confidence: 99%

Reconstruction of the Transcriptional Regulatory Network in Arabidopsis thaliana Aliphatic Glucosinolate Biosynthetic Pathway

Ashari¹,

Abdullah‐Zawawi²,

Harun³

et al. 2018

JSM

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Aliphatic glucosinolate is an important secondary metabolite responsible in plant defense mechanism and carcinogenic activity. It plays a crucial role in plant adaptation towards changes in the environment such as salinity and drought. However, in many plant genomes, there are thousands of genes encoding proteins still with putative functions and incomplete annotations. Therefore, the genome of Arabidopsis thaliana was selected to be investigated further to identify any putative genes that are potentially involved in the aliphatic glucosinolate biosynthesis pathway, most of its gene are with incomplete annotation. Known genes for aliphatic glucosinolates were retrieved from KEGG and AraCyc databases. Three co-expression databases i.e., ATTED-II, GeneMANIA and STRING were used to perform the co-expression network analysis. The integrated co-expression network was then being clustered, annotated and visualized using Cytoscape plugin, MCODE and ClueGO. Then, the regulatory network of A. thaliana from AtRegNet was mapped onto the co-expression network to build the transcriptional regulatory network. This study showed that a total of 506 genes were co-expressed with the 61 aliphatic glucosinolate biosynthesis genes. Five transcription factors have been predicted to be involved in the biosynthetic pathway of aliphatic glucosinolate, namely SEPALLATA 3 (SEP3), PHYTOCHROME INTERACTING FACTOR 3-like 5 (AtbHLH15/PIL5), ELONGATED HYPOCOTYL 5 (HY5), AGAMOUS-like 15 (AGL15) and GLABRA 3 (GL3). Meanwhile, three other genes with high potential to be involved in the aliphatic glucosinolates biosynthetic pathway were identified, i.e., methylthioalkylmalate-like synthase 4 (MAML-4) and aspartate aminotransferase (ASP1 and ASP4). These findings can be used to complete the aliphatic glucosinolate biosynthetic pathway in A. thaliana and to update the information on the glucosinolate-related pathways in public metabolic databases.

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“…The biclustering algorithm proposed by Hartigan [1] is clustering based on the rows and columns of gene expression matrices simultaneously. The biclustering algorithm is a new clustering method, it is to find local similarity in gene expression matrix [2,3,4,5]. Cheng et al [6] proposed the well-known CC algorithm, and the biclustering algorithm is applied to gene expression data for the first time, the CC algorithm can quickly get the user specified number of biclustering, But the flaw is obvious, substitution of random numbers will change the original data, which leads to inaccurate results and and Can not find overlapping biclustering.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Yang¹,

Zhao²,

Shu³

et al. 2018

JMI

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The biclustering algorithm based on greedy randomized adaptive search procedure (GRASP) has two main steps, firstly, bicluster seeds are generated by Kmeans clustering; secondly, the greedy randomized adaptive search procedure is used to expand these bicluster seeds so that to get some better biclusters. However, K-means clustering assumes that each gene belongs to only one cluster, which is not biologically valid, because some genes may not belong to only one category, and K-means clustering requires multiple runs to determine the number of "clusters," all of which will affect the identification of bicluster seeds. This paper optimizes GRASP-based bIclustering algorithm. In this paper, the single column vector clustering is used to generate biclusters seed, and then the bicluster seeds are added more rows and columns to generate the final biclusters. Experiment shows that this algorithm can generate a large number of biclusters with coexpression level, and the algorithm also finds more biclusters.

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Analysis of Gene Expression Patterns Using Biclustering

Cited by 5 publications

References 33 publications

Parallel Framework for Inferring Genome Scale Gene Regulatory Networks

Parallel Framework for Inferring Genome Scale Gene Regulatory Networks

Reconstruction of the Transcriptional Regulatory Network in Arabidopsis thaliana Aliphatic Glucosinolate Biosynthetic Pathway

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

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