UniProt: a hub for protein information. UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.

References in zbMATH (referenced in 133 articles , 1 standard article )

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  1. Horta, Edwin Rodríguez; Lage-Castellanos, Alejandro; Weigt, Martin; Barrat-Charlaix, Pierre: Global multivariate model learning from hierarchically correlated data (2021)
  2. Perez-Verona, Isabel Cristina; Tribastone, Mirco; Vandin, Andrea: A large-scale assessment of exact lumping of quantitative models in the biomodels repository (2021)
  3. Wehbe, Diala; Wicker, Nicolas; Al-Ayoubi, Baydaa; Moulinier, Luc: Fixed-size determinantal point processes sampling for species phylogeny (2021)
  4. Zhang, Dan; Chen, Hua-Dong; Zulfiqar, Hasan; Yuan, Shi-Shi; Huang, Qin-Lai; Zhang, Zhao-Yue; Deng, Ke-Jun: iBLP: an XGBoost-based predictor for identifying bioluminescent proteins (2021)
  5. Bhardwaj, Swati; Gudur, Venkateshwarlu Yellaswamy; Acharyya, Amit: An accelerated computational approach in proteomics (2020)
  6. Åstrand, Mia; Cuellar, Julia; Hytönen, Jukka; Salminen, Tiina A.: Predicting the ligand-binding properties of \textitBorreliaburgdorferi s.s. Bmp proteins in light of the conserved features of related \textitBorreliaproteins (2019)
  7. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  8. Eggeling, Ralf; Grosse, Ivo; Koivisto, Mikko: Algorithms for learning parsimonious context trees (2019)
  9. Li, Gaoshi; Li, Min; Peng, Wei; Li, Yaohang; Pan, Yi; Wang, Jianxin: A novel extended Pareto optimality consensus model for predicting essential proteins (2019)
  10. Motik, Boris; Nenov, Yavor; Piro, Robert; Horrocks, Ian: Maintenance of datalog materialisations revisited (2019)
  11. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  12. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  13. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  14. Yang, Ruiyu; Jiang, Yuxiang; Mathews, Scott; Housworth, Elizabeth A.; Hahn, Matthew W.; Radivojac, Predrag: A new class of metrics for learning on real-valued and structured data (2019)
  15. Zamudio, Gabriel S.; Prosdocimi, Francisco; Torres de Farias, Sávio; José, Marco V.: A neutral evolution test derived from a theoretical amino acid substitution model (2019)
  16. Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
  17. Crompton, L. A.; McKnight, L. L.; Reynolds, C. K.; Mills, J. A. N.; Ellis, J. L.; Hanigan, M. D.; Dijkstra, J.; Bequette, B. J.; Bannink, A.; France, J.: An isotope dilution model for partitioning of phenylalanine and tyrosine uptake by the liver of lactating dairy cows (2018)
  18. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  19. Mezei, Mihaly: Revisiting chameleon sequences in the protein data bank (2018)
  20. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)

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