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@ARTICLE{Wassmer:291259,
      author       = {E. Wassmer$^*$ and G. Koppany$^*$ and M. Hermes$^*$ and S.
                      Diederichs$^*$ and M. Caudron-Herger$^*$},
      title        = {{R}efining the pool of {RNA}-binding domains advances the
                      classification and prediction of {RNA}-binding proteins.},
      journal      = {Nucleic acids research},
      volume       = {52},
      number       = {13},
      issn         = {0305-1048},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2024-01345},
      pages        = {7504-7522},
      year         = {2024},
      note         = {#EA:B150#LA:B150# / 2024 Jul 22;52(13):7504-7522},
      abstract     = {From transcription to decay, RNA-binding proteins (RBPs)
                      influence RNA metabolism. Using the RBP2GO database that
                      combines proteome-wide RBP screens from 13 species, we
                      investigated the RNA-binding features of 176 896 proteins.
                      By compiling published lists of RNA-binding domains (RBDs)
                      and RNA-related protein family (Rfam) IDs with lists from
                      the InterPro database, we analyzed the distribution of the
                      RBDs and Rfam IDs in RBPs and non-RBPs to select RBDs and
                      Rfam IDs that were enriched in RBPs. We also explored
                      proteins for their content in intrinsically disordered
                      regions (IDRs) and low complexity regions (LCRs). We found a
                      strong positive correlation between IDRs and RBDs and a
                      co-occurrence of specific LCRs. Our bioinformatic analysis
                      indicated that RBDs/Rfam IDs were strong indicators of the
                      RNA-binding potential of proteins and helped predicting new
                      RBP candidates, especially in less investigated species. By
                      further analyzing RBPs without RBD, we predicted new RBDs
                      that were validated by RNA-bound peptides. Finally, we
                      created the RBP2GO composite score by combining the RBP2GO
                      score with new quality factors linked to RBDs and Rfam IDs.
                      Based on the RBP2GO composite score, we compiled a list of
                      2018 high-confidence human RBPs. The knowledge collected
                      here was integrated into the RBP2GO database at
                      https://RBP2GO-2-Beta.dkfz.de.},
      cin          = {B150 / FR01},
      ddc          = {570},
      cid          = {I:(DE-He78)B150-20160331 / I:(DE-He78)FR01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38917322},
      doi          = {10.1093/nar/gkae536},
      url          = {https://inrepo02.dkfz.de/record/291259},
}