This challenge aims to motivate the cryo-EM community to carefully consider approaches to single-particle processing for datasets containing preferred orientation, especially with regard to biased estimates of state populations.

We seek to assess the accuracy of current methods to estimate state populations in the presence of a biased pose distribution; thus, we have provided a dataset with a known population of two different states of a single protein and a non-uniform pose distribution. With this challenge, we hope to inspire the development of more accurate and robust methods in the cryo-EM community for population estimation. Not only is preferred orientation a common problem, but rarely do we see datasets with perfect uniform pose distributions, and most algorithms do not systematically account for this, especially when estimating populations. Lastly, with this relatively simple challenge where templates have been provided, we are only assessing the entanglement of pose and state populations, not reconstruction quality or resolution.

Dataset

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A particle .mrcs stack, an associated .star file, and .mrc template maps can be downloaded via Globus here (8 GB).

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This dataset contains a single two-state protein with ‘open’ and ‘closed’ template maps provided.

• 30,000 particles, 1.2 Å/pixel, 256 pixel box size
• Poses are not provided; particles are centered
• Per-particle CTF defocus values are provided

Evaluation

Entries will be evaluated by how accurately they estimate state populations within the dataset as a whole and, if applicable, how accurately they estimate per particle states and poses (i.e., alignments).

Submission

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Entries can be uploaded and submitted via Google Forms here.

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Please provide:

• Workflow summary

• ‘Open’ and ‘closed’ state population fractions

• Metadata (.star or .cs file) containing the ‘open’ state particle subset with poses and unmodified particle image paths

• Metadata (.star or .cs file) containing the ‘closed’ state particle subset with poses and unmodified particle image paths

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  All particles must be classified into an ‘open’ or ‘closed’ state.

  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.

Questions

For most general questions, please use the challenge discussions space on GitHub. For urgent or private questions regarding this dataset, please contact Pilar Cossio ([email protected]) or Sonya Hanson ([email protected]) at the Flatiron Institute.