An Integrated Approach To Predict Essential Genes In The Aspergillus Nidulans Genome

Traditional knockout techniques have largely been used to identify the essential genes in organisms; however, the high cost and ineffectiveness of the techniques make it difficult to consider genome-scale, such as the A. nidulans genome sharing over ten thousand of genes and it’s not realistic with knockout techniques. Based on this fact, we first constructed two empirical model (i.e. Model I and Model II) with genome-wide data of the organisms (S. cerevisiae and S. pombe) which essential genes have been identified to predict the essential genes in the A. nidulans genome. Model I was constructed based on homologs (i.e. orthologs and paralogs) between two close related organisms as well as essential gene datasets and the principle that the essential genes could be shown by some of characteristics of the genome sequence, using the genome of S. pombe and S. cerevisiae for training data respectively, according to the research and analysis before, choosing the sequence characteristics for identifying essentiality, establishing the empirical model for identifying essential genes. Model II was constructed based on the rule the essential genes could role in some special components, participate in some special processes, adjust some functions, using GO (Gene Ontology) terms of S. pombe and S. cerevisiae for training data respectively, establishing the empirical model for identifying essential genes. Then identifying essential genes of A. nidulans with the empirical models constructed based on S. pombe and S. cerevisiae respectively, and comparing the results. Finally, we fetch the genes which be identified identical by the two empirical models to be the essential genes of A. nidulans, and we identified 1718 putative essential genes in the A. nidulans genome. We check up our result with 19 essential genes and 12 non-essential genes which have been identified before. The results showed that:16 in the 19 essential genes are identified right and 9 in the 12 non-essential genes s are identified right, the false positive rate is 0.25 and the true positive rate is 0.842. This shows that our method is effective and feasible.