- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
Cloud computing offers easy and economical access to computational capacity at a scale that had previously been available to only the largest research institutions. To take advantage, large biological datasets are increasingly analyzed on various cloud computing platforms, using public, private and hybrid clouds1 with the aid of workflow systems. When employed in global projects, such systems must be flexible in their ability to operate in different environments, including academic clouds, to allow researchers to bring their computational pipelines to the data, especially in cases where the raw data themselves cannot be moved. The recently developed cloud-based scientific workflow frameworks Nextflow2, Toil3 and GenomeVIP4 focus their support largely on individual commercial cloud computing environments—mostly Amazon Web Services—and lack complete functionality for other major providers. This limits their use in studies that require multi-cloud operation due to practical and regulatory requirements5,6. Butler, in contrast, provides full support for operation on OpenStack-based commercial and academic clouds, Amazon Web Services, Microsoft Azure and Google Compute Platform, and can thus enable international collaborations involving the analysis of hundreds of thousands of samples where distributed cloud-based computation is pursued in different jurisdictions5,6,7. Show more
Journal / seriesNature Biotechnology
Pages / Article No.
PublisherNature Publishing Group
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