earticle

영어

Recent advancement in microarray technologies and large high throughput data generated has made it very challenging to decipher and draw a feasible biological conclusion from current microarray experiments. The difficulty arises when the number of samples available for analysis is smaller than the huge numbers of genes that need to be considered. Currently, pathway analysis is a preferable tool in extracting and understanding the biological information obtained from high throughput experiments. It is essential to analyze microarray experiments along with their biological information to represent the underlying structure of the biological network. Currently, there are numerous software developed for pathway analysis available with the same goal of mining the information from the microarray experiments with biological relevance over the extensive amounts of data. This paper discusses the comparisons between pathway analysis software in terms of their performance, advantages and limitations as well as the available pathway databases in terms of their data availability and organization. The aim of this review is to provide a better understanding of the capabilities of these software and helps to select the tools most suited for a particular purpose.