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A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM
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A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM
The article presents a systematic meta-algorithm for spawning and aggregating multi-class cryo-EM reconstruction jobs, formalizing iterative classification and filtering strategies used by practitioners. It claims to be the first method capable of ab initio reconstruction on datasets with dozens of distinct species, achieving 97% accuracy on a 45-class subset of Tomotwin-100 and 75% on the full dataset, and successfully recovering ribosomal assembly states from an unfiltered experimental cryo-EM dataset. The approach scales with compute resources and aims to underpin automated cryo-EM workflows in contemporary experimental settings.
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