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GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

Ye N, Yin H, Liu J, Dai X, Yin T - Biomed Res Int (2015)

Bottom Line: For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files.Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments.Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

View Article: PubMed Central - PubMed

Affiliation: The Southern Modern Forestry Collaborative Innovation Center, Nanjing Forestry University, Nanjing 210037, China ; College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.

ABSTRACT
The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

No MeSH data available.


Related in: MedlinePlus

An example of random selecting gene profiles of interest. (a) Display the input data before searching. (b) Resulting panel after selection and search.
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fig3: An example of random selecting gene profiles of interest. (a) Display the input data before searching. (b) Resulting panel after selection and search.

Mentions: For initial exploration of gene expression dataset, a random selecting and searching approach can be efficient to identify genes of interest. For example, we developed an in-house RNA-seq dataset containing over 90,000 transcripts (derived from de novo assembly), with 8 time points. After randomly picked profiles of interest, GESearch found a group of 304 coexpressed genes with highly similar expression patterns (Figure 3). This approach is efficient and straightforward for small or moderate scale datasets in which gene expression signatures can be easily detected and visualized. To examine the feasibility of GESearch, we retrieved and analyzed two large gene expression datasets from human and yeast cell-cycle studies.


GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

Ye N, Yin H, Liu J, Dai X, Yin T - Biomed Res Int (2015)

An example of random selecting gene profiles of interest. (a) Display the input data before searching. (b) Resulting panel after selection and search.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496643&req=5

fig3: An example of random selecting gene profiles of interest. (a) Display the input data before searching. (b) Resulting panel after selection and search.
Mentions: For initial exploration of gene expression dataset, a random selecting and searching approach can be efficient to identify genes of interest. For example, we developed an in-house RNA-seq dataset containing over 90,000 transcripts (derived from de novo assembly), with 8 time points. After randomly picked profiles of interest, GESearch found a group of 304 coexpressed genes with highly similar expression patterns (Figure 3). This approach is efficient and straightforward for small or moderate scale datasets in which gene expression signatures can be easily detected and visualized. To examine the feasibility of GESearch, we retrieved and analyzed two large gene expression datasets from human and yeast cell-cycle studies.

Bottom Line: For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files.Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments.Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

View Article: PubMed Central - PubMed

Affiliation: The Southern Modern Forestry Collaborative Innovation Center, Nanjing Forestry University, Nanjing 210037, China ; College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.

ABSTRACT
The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

No MeSH data available.


Related in: MedlinePlus