Dataset Information

A new method of finding groups of coexpressed genes and conditions of coexpression.

ABSTRACT: To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters.We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters.Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

SUBMITTER: Anand R

PROVIDER: S-EPMC5124285 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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A new method of finding groups of coexpressed genes and conditions of coexpression.

Anand Rajat R Ravichandran Srikanth S Chatterjee Samrat S

BMC bioinformatics 20161125 1

<h4>Background</h4>To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many ...[more]

PMID: 27887568

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Project description:Regarding the possible multiple functions of a specific gene, finding the alternative roles of genes is a major challenge. Huge amount of available expression data and the central role of the promoter and its regulatory elements provide unique opportunely to address this issue. The question is that how the expression data and promoter analysis can be applied to uncover the different functions of a gene. A computational approach has been presented here by analysis of promoter regulatory elements, coexpressed gene as well as protein domain and prosite analysis. We applied our approach on Thaumatin like protein (TLP) as example. TLP is of group 5 of pathogenesis related proteins which their antifungal role has been proved previously. In contrast, Osmotin like proteins (OLPs) are basic form of TLPs with proved role only in abiotic stresses. We demonstrated the possible outstanding homolouges involving in both biotic and abiotic stresses by analyzing 300 coexpressed genes for each Arabidopsis TLP and OLP in biotic, abiotic, hormone, and light microarray experiments based on mutual ranking. In addition, promoter analysis was employed to detect transcription factor binding sites (TFBs) and their differences between OLPs and TLPs. A specific combination of five TFBs was found in all TLPs presenting the key structure in functional response of TLP to fungal stress. Interestingly, we found the fungal response TFBs in some of salt responsive OLPs, indicating the possible role of OLPs in biotic stresses. Thirteen TFBS were unique for all OLPs and some found in TLPs, proposing the possible role of these TLPs in abiotic stresses. Multivariate analysis showed the possibility of estimating models for distinguishing biotic and abiotic functions of TIPs based on promoter regulatory elements. This is the first report in identifying multiple roles of TLPs and OLPs in biotic and abiotic stresses. This study provides valuable clues for screening and discovering new genes with possible roles in tolerance against both biotic and abiotic stresses. Interestingly, principle component analysis showed that promoter regulatory elements of TLPs and OLPs are more variable than protein properties reinforcing the prominent role of promoter architecture in determining gene function alteration.

Dataset Information

A new method of finding groups of coexpressed genes and conditions of coexpression.

Publications

A new method of finding groups of coexpressed genes and conditions of coexpression.

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