An Efficient Approach for Differentiating the Gene Prediction Program

This thesis work presents the most rigorous approach of gene algorithims for metagenomic datasets to data .we differeniate four different programs:glimmer,metagenmark(MGK),prodigal and orphilla.The differentiation are based on their performances over simulated fragments from many species of diverse lineages.we defined? four different fragments type:two types come from coding regions and two come on non-coding region and other types are from the gene edges.In this we use 20 species to differeniate every program with their performance.In this we use various formula to predict the performance of various program the formula that we are:specificity,sensitivity,predictionerror,predictionaccuracy,annotationerrorand F-measure .so that sample is examined easily and manually by calculation based and no predecessors has separately examined fragments that contain gene edge as opposed to non ?coding region.in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.