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[This corrects the article DOI: 10.1155/2016/7823429.].

Background: In order to understand the colocalization of genetic loci amongst species, synteny and collinearity analysis is a frequent task in comparative genomics research. However many analysis software packages are not effective in visualizing results. Problems include lack of graphic visualization, simple representation, or inextensible format of outputs. Moreover, higher throughput sequencing technology requires higher resolution image output., Implementation: To fill this gap, this paper publishes VGSC, the Vector Graph toolkit of genome Synteny and Collinearity, and its online service, to visualize the synteny and collinearity in the common graphical format, including both raster (JPEG, Bitmap, and PNG) and vector graphic (SVG, EPS, and PDF)., Result: Users can upload sequence alignments from blast and collinearity relationship from the synteny analysis tools. The website can generate the vector or raster graphical results automatically. We also provide a java-based bytecode binary to enable the command-line execution.

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.