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| 024 | 7 | _ | |a 10.1093/nar/gkae536 |2 doi |
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| 100 | 1 | _ | |a Wassmer, Elsa |0 P:(DE-He78)fe2975a902a788af0b3e8c90dbb2fabc |b 0 |e First author |
| 245 | _ | _ | |a Refining the pool of RNA-binding domains advances the classification and prediction of RNA-binding proteins. |
| 260 | _ | _ | |a Oxford |c 2024 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Caudron-Herger, Maiwen |0 P:(DE-He78)0137d9152be648373d139f096eeebf71 |b 4 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1093/nar/gkae536 |g p. gkae536 |0 PERI:(DE-600)1472175-2 |n 13 |p 7504-7522 |t Nucleic acids research |v 52 |y 2024 |x 0305-1048 |
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