@article{MostosiSchindelinKollmannsbergeretal.2020,
  author    = {Mostosi, Philipp and Schindelin, Hermann and Kollmannsberger, Philip and Thorn, Andrea},
  title     = {Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps},
  series = {Angewandte Chemie International Edition},
  volume    = {59},
  journal   = {Angewandte Chemie International Edition},
  number    = {35},
  doi       = {10.1002/anie.202000421},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214763},
  pages     = {14788 -- 14795},
  year      = {2020},
  abstract  = {In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main-chain placement. Due to its high recall and precision rates of 95.1 \% and 80.3 \%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.},
  language  = {en}
}