Contrast between traditional bioinformatics workflow and new cloud-based workflow. The traditional bioinformatics workflow is characterized by researchers downloading or uploading genomic and health-related data to local on-site storage (eg, computers) for processing, analysis and obtaining of results. The results are then uploaded to repositories for publishing. This process is typically slower, redundant and necessitates high IT capital expenditure. Indeed, the traditional practice of genome analysis requires researchers to spend weeks to months downloading hundreds of terabytes of data from a central repository before computations can begin. By contrast, the new cloud computing bioinformatics model eliminates the need for researchers to download the data to their own computers. Instead, it is characterized by a one-stop workflow where the compute (eg, standard and custom pipelines, workflow tools) is brought to the data. Particularly in Infrastructure as a Service cloud computing, researchers can upload their analytic software into a cloud, run the software, and download the compiled results in a secure fashion. Platform as a Service and Software as a Service cloud computing can also provide a one-stop bioinformatics workflow, albeit with less raw computing resources made available to researchers.