Scale and complexity of comparative genomics resources like ortholog detection tools and repositories are rapidly rising. In bioinformatics, cloud computing could be a useful alternative to large computational tools because it enables researchers to dynamically build a dedicated virtual cluster. Roundup, a large-scale comparative genomics resource, is optimized for cloud computing in this manuscript. We also detail important cost-effective procedures for ensuring maximum computation at minimal costs, as well as the proper operating principles needed to achieve cloud computing efficiency. On Amazon's Elastic Compute Cloud, we computed ortholog among 902 fully sequenced genomes using the comparative genomics tool Roundup as a case study. We devised a plan to deploy the web service Elastic Map Reduce, make the most of the cloud, and cut costs while simultaneously managing the ortholog processes. Based on the size and complexity of the genomes being compared, we developed a model that, in advance, determines the best order in which jobs should be submitted.
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