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@ARTICLE{Walsh:186000,
      author       = {Walsh, I. and Giollo, M. and Di Domenico, T. and Ferrari,
                      C. and Zimmermann, O. and Tosatto, S. C. E.},
      title        = {{C}omprehensive large-scale assessment of intrinsic protein
                      disorder},
      journal      = {Bioinformatics},
      volume       = {31},
      number       = {2},
      issn         = {0266-7061},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2015-00109},
      pages        = {201-208},
      year         = {2015},
      abstract     = {Motivation: Intrinsically disordered regions are key for
                      the function of numerous proteins. Due to the difficulties
                      in experimental disorder characterization, many
                      computational predictors have been developed with various
                      disorder flavors. Their performance is generally measured on
                      small sets mainly from experimentally solved structures,
                      e.g. Protein Data Bank (PDB) chains. MobiDB has only
                      recently started to collect disorder annotations from
                      multiple experimental structures.Results: MobiDB annotates
                      disorder for UniProt sequences, allowing us to conduct the
                      first large-scale assessment of fast disorder predictors on
                      25 833 different sequences with X-ray crystallographic
                      structures. In addition to a comprehensive ranking of
                      predictors, this analysis produced the following interesting
                      observations. (i) The predictors cluster according to their
                      disorder definition, with a consensus giving more
                      confidence. (ii) Previous assessments appear over-reliant on
                      data annotated at the PDB chain level and performance is
                      lower on entire UniProt sequences. (iii) Long disordered
                      regions are harder to predict. (iv) Depending on the
                      structural and functional types of the proteins, differences
                      in prediction performance of up to $10\%$ are observed.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511)},
      pid          = {G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000347832300008},
      pubmed       = {pmid:25246432},
      doi          = {10.1093/bioinformatics/btu625},
      url          = {https://juser.fz-juelich.de/record/186000},
}