Galaxy is an open-source web-based framework that is widely used for performing computational analyses in diverse application domains, such as genome assembly, computational chemistry, ecology, and epigenetics, to name a few. The current Galaxy software framework runs on several high-performance computing platforms such as on-premise clusters, public data centers, and national lab supercomputers. These infrastructures also provide support for state-of-the-art accelerators like Graphical Processing Units (GPUs). When coupled with accelerator support, the tools executing in Galaxy can benefit from massive performance gains in terms of computation time, thereby allowing a more robust computational analysis environment for researchers. Despite tools having GPU capabilities, the current Galaxy framework does not support GPUs, and thus prevents tools from taking advantage of the performance benefits offered by GPUs. We present and experimentally evaluate GYAN, a GPU-aware computation mapping and orchestration functionality implemented in Galaxy that allows the Galaxy tools to be executed on a GPU-enabled cluster. GYAN has the capability of identifying GPU-supported tools and scheduling them on single or multiple GPU nodes based on the availability in the cluster. GYAN supports both native and containerized tool execution. We performed extensive evaluations of the implementation using popular bio-engineering tools to demonstrate the benefits of using GPU technologies. For example, the Racon consensus tool executes ~2× faster than the regular baseline CPU-only jobs, while the Bonito base calling tool shows ~50× speedup.