Kim J.S.,GenoCheck Co. and 628 |
Kim S.J.,GenoCheck Co. and 628 |
Kim S.J.,Hanyang University |
Lee S.Y.,Hanyang University |
And 5 more authors.
Biochip Journal | Year: 2010
As a result of population and universality in high-throughput omics technologies in the last decade, such as microarray methods, many researchers who study their genes of interest that are similarly or differently expressed in cellular states, diseases, functional, or environmental element conditions have been possessed of the ability to identify biological validation on the genomic scale. In parallel with the advancement of omics experiments, the number of biological databases, which contain the biological annotations, genetic functionalities, metabolic pathways, and mutational information or functional interactions between genes or proteins, has increased. Fortunately, these datasets have been mainly opened to computational network protocols, such as FTP or SSH, and have freely served as web search tools to enable users to retrieve a biological annotation with queries. Of these databases, GO (Gene Ontology) provides controlled vocabulary terms for describing biological annotation of a gene or gene product in tree aspects that are classified as biological process, cellular component, and molecular function across various species. Also, GO gives researchers a publicly accessible tool for identification of a gene or gene product with GO terms. The result of this tool displays the hierarchical tree-type html format or DAGs (Directed Acyclic Graphs) graphtype format to users according to the three aspects of GO. However, as this permits users to ask only one keyword at a time, it is difficult to search many interesting gene sets in gene expression profiles obtained from microarray experiments at once. A few GO search tools have been developed to help in analysis of GO annotation for multi genes. However they have not satisfied user demands in terms of the simplicity of the user interface and visualization of analysis results. For these reasons, Array2GO, which has been based on a web environment, has been developed and is freely accessible at http://www.koreagene.co.kr/cgi-bin/service/service1.pl. © The Korean BioChip Society and Springer 2010. Source
Kim J.-S.,GenoCheck Co. and 628 |
Kim S.-J.,GenoCheck Co. and 628 |
Kim S.-J.,Hanyang University |
Park H.-W.,GenoCheck Co. and 628 |
And 6 more authors.
Biochip Journal | Year: 2010
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/service1.pl. © The Korean BioChip Society and Springer 2010. Source