PT  - JOURNAL ARTICLE
AU  - Xiangrui Zeng
AU  - Anson Kahng
AU  - Liang Xue
AU  - Julia Mahamid
AU  - Yi-Wei Chang
AU  - Min Xu
TI  - DISCA: high-throughput cryo-ET structural pattern mining by deep unsupervised clustering
AID  - 10.1101/2021.05.16.444381
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.16.444381
4099  - http://biorxiv.org/content/early/2021/05/17/2021.05.16.444381.short
4100  - http://biorxiv.org/content/early/2021/05/17/2021.05.16.444381.full
AB  - Cryo-electron tomography directly visualizes heterogeneous macromolecular structures in complex cellular environments, but existing computer-assisted sorting approaches are low-throughput or inherently limited due to their dependency on available templates and manual labels.We introduce a high-throughput template-and-label-free deep learning approach that automatically discovers subsets of homogeneous structures by learning and modeling 3D structural features and their distributions.Diverse structures emerging from sorted subsets enable systematic unbiased recognition of macromolecular complexes in situ.Competing Interest StatementThe authors have declared no competing interest.