This challenge aims to motivate the cryo-EM community to benchmark, evaluate, and advance single-particle processing workflows on an experimental dataset containing a heterogeneous mixture of macromolecular species.
We seek to assess the accuracy, efficiency, and scalability of heterogeneous reconstruction algorithms and pipelines. Beyond benchmarking, we hope to inspire the development of new methods focused on compositional heterogeneity problems, especially those involving pose estimation of unknown mixtures. The dataset opens the door to visual proteomics-style analyses, pushing toward the integrated processing of multiple real biological samples within a unified, automated computational framework.
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Particle .mrcs stacks and an associated .star file can be downloaded via Globus here (~540 GB).
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This dataset contains raw particle picks for a mixture of distinct, unknown species.
Entries will be evaluated by the successful recovery of all the species present in the sample, the quality (resolution) of each consensus reconstruction, the accuracy of particle assignments to each species, and the accuracy of particle poses (i.e., alignments).
Participants will also submit a descriptive summary of their complete processing workflow; entries will be separately evaluated by workflow complexity and efficiency.
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Entries can be uploaded and submitted via Google Forms here.
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Please provide a workflow summary, and for each identified class include:
Volume of the consensus reconstruction (.mrc file)
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Volumes should be centered and cubic with the same spatial extent as the particle images (290.88 Å).
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Half maps (.mrc files)
Estimated resolution
Metadata (.star or .cs file) containing the corresponding particle subset with poses and unmodified particle image paths
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Do not restack or rename particle stacks. Image paths from the provided .star file will be used to evaluate particle class assignments.
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See the submission form for more details.
For most general questions, please use the challenge discussions space on GitHub. For urgent or private questions regarding this dataset, please contact Ryan Feathers ([email protected]), Robert Heeter ([email protected]), or Ellen Zhong ([email protected]) at Princeton University.