They simplify pipeline development, optimize resource usage, handle software installation and versions, and run on different compute platforms, enabling workflow portability and sharing. Workflow managers were developed in response to such challenges. However, transforming data into information involves running a large number of tools, optimizing parameters, and integrating dynamically changing reference data. With the increasing amount and complexity of data, scalability and reproducibility have become essential not just for experiments, but also for computational analysis. The rapid growth of high-throughput technologies has transformed biomedical research.
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