> For the complete documentation index, see [llms.txt](https://guide.cryosparc.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://guide.cryosparc.com/processing-data/automated-workflows.md). # Automated Workflows ### Automated repeat-target structure determination Single particle cryo-EM adoption has been transformative for structural biology and drug discovery, and advances in instrumentation and data collection continue to increase the rate at which datasets can be acquired. Cryo-EM data processing has also advanced quickly, and in CryoSPARC, processing is rapid and workflows can be highly streamlined. However, achieving high-resolution results often still requires extensive human intervention, which can become a bottleneck particularly in high-throughput settings such as structure-based drug design, where dozens or hundreds of related structures must be determined rapidly to inform drug development projects. Automated cryo-EM data processing is therefore a crucial development for cryo-EM: by removing the requirement for manual decision-making at each stage of analysis, automation can reduce time-to-structure from days to hours, enable parallel processing of large numbers of datasets, lower the barrier to entry for new practitioners, and ensure reproducible, consistent results across experiments. In the pages below, we describe the development of an end-to-end automation strategy using new tools built in CryoSPARC for “repeat-target” structure determination. Our results demonstrate that it is now possible to completely automate the data processing workflow for repeat-target scenarios, and to obtain a high quality particle stack, consensus reconstruction and local refinement in the ligand binding region that are suitable for model building. Using the Workflows tool in CryoSPARC and the strategies described here, users can replicate, adapt and extend the automated workflow for their own targets.
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Advanced automated data processing: a case study using CAK

In this case study, we demonstrate how to extend the automation strategy to handle severe preferred orientation and produce improved map quality./pages/RrIDvcAceCgjTgHhDfHo/files/BmglKcxvOzl7K68ja4Xm

Fully automated data processing: a case study using GPCRs

In this preprint, we demonstrate an automation strategy with a test set of 21 challenging GPCR datasets./pages/qk7066CloymhjPPxRlSO/files/plMLNR8h9PHpFSgWQ5sX

Practical tips: using Workflows to process your own data

The ins-and-outs of how to create, modify, and apply workflows./pages/LU6C6t5u1TOb1qcccpCW/files/5da3ilEf0qMhdD6bqbE0

Downloads: Workflow JSONs, sample volumes and masks

Need the files from any of the case studies above? Everything is aggregated here./pages/v9q0sZbaw1TUUfXqOyWt/files/5RivEEIJJzPtjNEfaOSk