Computational Analysis of Microarray Gene Expression Profiling in Leishmania donovani and Leishmania major

Abstract

Leishmaniasis is one of the most deadly diseases in which the human genes are affected and gets altered expression. The genes of Leishmania species which are responsible for causing leishmaniasis are not known yet. The objective behind this study is to know about the genes which are differentially expressed (DE) when macrophage of mice is infected with Leishmania donovani and Leishmania major. It also aims to know the similarities of these DE genes from human genes and predict the genes responsible for leishmaniasis in human. Microarray data analyses allow us to monitor thousands of genes simultaneously. The analyses of microarray data using statistical methods predict the differentially expressed genes in diseased state. Clustering of these genes and functional analysis through GO database accentuate the predictions.

Keywords: Differentially Expressed (DE), Automated Robust Microarray Data Analysis (ARMADA), Perfect Match (PM), Gene Ontology (GO), Clustering