Prediction of Essential Genes in the Species of shigella

Abstract

Bacteria and their derivatives i.e. gram positive and gram negative have a large and expanded worldwide market. Shigella has the potential to become a model organism for bacteria world since its genome has been sequenced and genes coding for various trades has been predicted. In the current study we made efforts to reannotate the shigella strain i.e. S.flexneri and S.Sonnei by finding essential genes in their genome through machine learning and gene ontology based approach .In machine learning we perform classification and clustering of sample genes after processed them, on the other hand; gene ontology study was perform by finding molecular function ,biological process and cellular component of those gene . Total 24 genes are found to be involved in different metabolic and physiological process of shigella species which are confirmed through different statistical measures. These 24 genes found to be the area of interest regarding functional characterization.