A realistic phantom dataset for benchmarking cryo-ET data annotation
Ariana Peck, Yue Yu, Jonathan Schwartz, Anchi Cheng, Utz H. Ermel, Joshua Hutchings, Saugat Kandel, Dari Kimanius, Elizabeth Montabana, Daniel Serwas, Hannah Siems, Feng Wang, Zhuowen Zhao, Shawn Zheng, Matthias Haury, David A. Agard, Clinton S. Potter, Bridget Carragher, Kyle Harrington, Mohammadreza Paraan
Abstract
Cryo-electron tomography (cryo-ET) is a powerful technique for imaging molecular complexes in their native cellular environments. However, identifying the vast majority of molecular species in cellular tomograms remains prohibitively difficult. Machine learning (ML) methods provide an opportunity to automate the annotation process, but algorithm development has been hindered by the lack of large, standardized datasets. Here we present an experimental phantom dataset with comprehensive ground-truth annotations for six molecular species to spur new algorithm development and benchmark existing tools. This annotated dataset is available on the CryoET Data Portal with infrastructure to streamline access for methods developers across fields.