The workflow is powered by new methods, including micrograph junk detection, denoising, and reference based auto selection of 2D classes and 3D volumes. Read the preprint for more insights and details.

We test across 21 challenging active and inactive GPCR datasets.
Despite small particle size and the presence of contaminants, empty micelles, broken, denatured and aggregated particles, our automated workflow yields equal or better resolution and map quality in 17 of 21 cases.

🚀 Introducing fully automated data processing for repeat-target #cryoEM.

Using new tools in #CryoSPARC, it is now possible to obtain resolutions & map quality equal to or better than manual processing, with zero user intervention.

Preprint: www.biorxiv.org/content/10.1...

Flexibility can be frustrating when processing a #cryoEM dataset. If your target has a wobbly domain, check out our new case study on FaNaC1 for continuous heterogeneity tips in #CryoSPARC!

guide.cryosparc.com/processing-d...

When particles have orientation bias, the resulting maps are often streaked along the missing directions. In this case study, we cover why orientation bias produces anisotropic #cryoEM maps and how you may be able to recover from it!

guide.cryosparc.com/processing-d...

Are you new to #CryoEM, or looking for a review of the basics?

You might enjoy our TRPV1 case study! It covers preprocessing, particle curation, refinement, and more! You can also test your skills with the advanced exercises at the end of the study.
guide.cryosparc.com/processing-d...

#CryoSPARC

#CryoSPARC v4.7 is out! 🚀 
Featuring a new automatic Micrograph Junk Detector that labels and rejects contaminants, new tools for curating subsets of particles, performance and stability fixes, and more!
Full changelog: cryosparc.com/updates

#cryoEM