UniProt

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 136 articles , 1 standard article )

Showing results 21 to 40 of 136.
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  1. 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)
  2. Mezei, Mihaly: Revisiting chameleon sequences in the protein data bank (2018)
  3. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  4. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  5. DeBlasio, Dan; Kececioglu, John: Parameter advising for multiple sequence alignment (2017)
  6. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  7. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  8. Lèbre, Sophie; Gascuel, Olivier: The combinatorics of overlapping genes (2017)
  9. McMahan, Christopher; Baurley, James; Bridges, William; Joyner, Chase; Kacamarga, Muhamad Fitra; Lund, Robert; Pardamean, Carissa; Pardamean, Bens: A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures (2017)
  10. Mier, Pablo; Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.: Protein-protein interactions can be predicted using coiled coil co-evolution patterns (2017)
  11. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  12. Zhai, Jing-Xuan; Cao, Tian-Jie; An, Ji-Yong; Bian, Yong-Tao: Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC (2017)
  13. Barton, John P.; Chakraborty, Arup K.; Cocco, Simona; Jacquin, Hugo; Monasson, Rémi: On the entropy of protein families (2016)
  14. Clerc, Daryl G.: Nonlinear effects in evolution - an \textitabinitio study: a model in which the classical theory of evolution occurs as a special case (2016)
  15. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  16. Moon, Jucheol; Friedberg, Iddo; Eulenstein, Oliver: Highly bi-connected subgraphs for computational protein function annotation (2016)
  17. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of (\beta)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)
  18. Sebastian Gibb, Korbinian Strimmer: Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis (2015) arXiv
  19. Singh, Gautam B.: Fundamentals of bioinformatics and computational biology. Methods and exercises in MATLAB (2015)
  20. Wang, Jim Jing-Yan; Gao, Xin: Max-min distance nonnegative matrix factorization (2015)