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Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).

ABSTRACT: Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

SUBMITTER: Das S

PROVIDER: S-EPMC5215982 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).

Das Samarendra S Meher Prabina Kumar PK Rai Anil A Bhar Lal Mohan LM Mandal Baidya Nath BN

PloS one 20170105 1

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based ...[more]

PMID: 28056073

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Project description:BackgroundExpansins are plant cell wall loosening proteins that are involved in cell enlargement and a variety of other developmental processes. The expansin superfamily contains four subfamilies; namely, α-expansin (EXPA), β-expansin (EXPB), expansin-like A (EXLA), and expansin-like B (EXLB). Although the genome sequencing of soybeans is complete, our knowledge about the pattern of expansion and evolutionary history of soybean expansin genes remains limited.ResultsA total of 75 expansin genes were identified in the soybean genome, and grouped into four subfamilies based on their phylogenetic relationships. Structural analysis revealed that the expansin genes are conserved in each subfamily, but are divergent among subfamilies. Furthermore, in soybean and Arabidopsis, the expansin gene family has been mainly expanded through tandem and segmental duplications; however, in rice, segmental duplication appears to be the dominant process that generates this superfamily. The transcriptome atlas revealed notable differential expression in either transcript abundance or expression patterns under normal growth conditions. This finding was consistent with the differential distribution of the cis-elements in the promoter region, and indicated wide functional divergence in this superfamily. Moreover, some critical amino acids that contribute to functional divergence and positive selection were detected. Finally, site model and branch-site model analysis of positive selection indicated that the soybean expansin gene superfamily is under strong positive selection, and that divergent selection constraints might have influenced the evolution of the four subfamilies.ConclusionThis study demonstrated that the soybean expansin gene superfamily has expanded through tandem and segmental duplication. Differential expression indicated wide functional divergence in this superfamily. Furthermore, positive selection analysis revealed that divergent selection constraints might have influenced the evolution of the four subfamilies. In conclusion, the results of this study contribute novel detailed information about the molecular evolution of the expansin gene superfamily in soybean.

Dataset Information

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).

Publications

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).

Similar Datasets

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