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@ARTICLE{Lawson:904333,
      author       = {Lawson, Catherine L. and Kryshtafovych, Andriy and Adams,
                      Paul D. and Afonine, Pavel V. and Baker, Matthew L. and
                      Barad, Benjamin A. and Bond, Paul and Burnley, Tom and Cao,
                      Renzhi and Cheng, Jianlin and Chojnowski, Grzegorz and
                      Cowtan, Kevin and Dill, Ken A. and DiMaio, Frank and
                      Farrell, Daniel P. and Fraser, James S. and Herzik, Mark A.
                      and Hoh, Soon Wen and Hou, Jie and Hung, Li-Wei and Igaev,
                      Maxim and Joseph, Agnel P. and Kihara, Daisuke and Kumar,
                      Dilip and Mittal, Sumit and Monastyrskyy, Bohdan and Olek,
                      Mateusz and Palmer, Colin M. and Patwardhan, Ardan and
                      Perez, Alberto and Pfab, Jonas and Pintilie, Grigore D. and
                      Richardson, Jane S. and Rosenthal, Peter B. and Sarkar,
                      Daipayan and Schäfer, Luisa U. and Schmid, Michael F. and
                      Schröder, Gunnar F. and Shekhar, Mrinal and Si, Dong and
                      Singharoy, Abishek and Terashi, Genki and Terwilliger,
                      Thomas C. and Vaiana, Andrea and Wang, Liguo and Wang, Zhe
                      and Wankowicz, Stephanie A. and Williams, Christopher J. and
                      Winn, Martyn and Wu, Tianqi and Yu, Xiaodi and Zhang,
                      Kaiming and Berman, Helen M. and Chiu, Wah},
      title        = {{C}ryo-{EM} model validation recommendations based on
                      outcomes of the 2019 {EMD}ata{R}esource challenge},
      journal      = {Nature methods},
      volume       = {18},
      number       = {2},
      issn         = {1548-7091},
      address      = {London [u.a.]},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2021-05903},
      pages        = {156 - 164},
      year         = {2021},
      abstract     = {This paper describes outcomes of the 2019 Cryo-EM Model
                      Challenge. The goals were to (1) assess the quality of
                      models that can be produced from cryogenic electron
                      microscopy (cryo-EM) maps using current modeling software,
                      (2) evaluate reproducibility of modeling results from
                      different software developers and users and (3) compare
                      performance of current metrics used for model evaluation,
                      particularly Fit-to-Map metrics, with focus on near-atomic
                      resolution. Our findings demonstrate the relatively high
                      accuracy and reproducibility of cryo-EM models derived by 13
                      participating teams from four benchmark maps, including
                      three forming a resolution series (1.8 to 3.1 Å). The
                      results permit specific recommendations to be made about
                      validating near-atomic cryo-EM structures both in the
                      context of individual experiments and structure data
                      archives such as the Protein Data Bank. We recommend the
                      adoption of multiple scoring parameters to provide full and
                      objective annotation and assessment of the model, reflective
                      of the observed cryo-EM map density.},
      cin          = {IBI-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5244 - Information Processing in Neuronal Networks
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5244},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33542514},
      UT           = {WOS:000614686600012},
      doi          = {10.1038/s41592-020-01051-w},
      url          = {https://juser.fz-juelich.de/record/904333},
}