科研之友
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摘要
Over the past decade, microarray experiments have become popular with a common method of high-throughput omics technologies for understanding of gene expression patterns at the genome level. The objective of microarray experiments is to identify differentially expressed genes (DEGs) or similarly patterned genes groups from microarray experiments. For these reasons, several preprocessing methods for correction of experimental or systemic bias of microarray experiments have been proposed, and a number of statistical algorithms for selection of specially expressed genes within all genes on a microarray have been developed in order to support more reliable and significant results. With the results produced by these useful tools, researchers have examined common biological features, such as functional interactions in shared biological processes, direct-indirect regulation at the molecular level, or disease relation in a biological pathway or network. Of these biological identification analyses, pathway analysis has been mainly used for functional detection of biological features between these co-expressed genes. An advantage of pathway analysis is a visualization that has an important role in understanding of the intricate phenomena of pathways containing biochemical reactions or functional relations among a gene's products, enzymes, substrates, activators, inhibitors, or other biological molecular elements. For these reasons, several bioinformatics tools for pathway analysis based on external pathway data sources, such as KEGG or BIOCARTA, have been developed. These tools have offered user-friendly and powerful interfaces, visual and graphical functions of pathway diagrams, and biological annotations. However, the problems encountered by users remain unresolved in certain respects that are a complicated input file format derived from gene expression profiles, a time-consuming work for collection of information on pathways that contain several genes identified as interesting genes, or a restriction in picturing a pathway image map that included more than two interesting genes. In an attempt to overcome these problems, we have developed Array2KEGG as a web-based tool for finding pathway diagrams from the KEGG PATHWAY database. Array2KEGG has focused on simplicity in user interface, integration in heterogeneous biological databases, and visualization in depiction of a pathway diagram that includes more than two interesting genes. Array2KEGG is freely available for use at http://www.koreagene.co.kr/cgi-bin/service/servicel.pl.
- 出版日期2010-6-20
