PROSITE: a documented database using patterns and profiles as motif. Among the various databases dedicated to the identification of protein families and domains, PROSITE is the first one created and has continuously evolved since. PROSITE currently consists of a large collection of biologically meaningful motifs that are described as patterns or profiles, and linked to documentation briefly describing the protein family or domain they are designed to detect. The close relationship of PROSITE with the SWISS-PROT protein database allows the evaluation of the sensitivity and specificity of the PROSITE motifs and their periodic reviewing. In return, PROSITE is used to help annotate SWISS-PROT entries. The main characteristics and the techniques of family and domain identification used by PROSITE are reviewed in this paper. descriptors

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  1. Viljanen, Markus; Airola, Antti; Pahikkala, Tapio: Generalized vec trick for fast learning of pairwise kernel models (2022)
  2. Han, Yo-Sub; Ko, Sang-Ki; Ng, Timothy; Salomaa, Kai: Consensus string problem for multiple regular languages (2021)
  3. Han, Yo-Sub; Ko, Sang-Ki; Ng, Timothy; Salomaa, Kai: Closest substring problems for regular languages (2021)
  4. Romero Navarrete, Lise Rommel; Telles, Guilherme P.: Practical regular expression constrained sequence alignment (2020)
  5. Carmel, Amir; Tsur, Dekel; Ziv-Ukelson, Michal: On almost Monge all scores matrices (2019)
  6. Axelson-Fisk, Marina: Comparative gene finding. Models, algorithms and implementation (2015)
  7. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)
  8. Chen, Yen-Kuang; Li, Kuo-Bin: Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition (2013)
  9. Comin, Matteo; Verzotto, Davide: Filtering degenerate patterns with application to protein sequence analysis (2013)
  10. Marschall, Tobias: Construction of minimal deterministic finite automata from biological motifs (2011)
  11. Wong, Ka-Chun; Peng, Chengbin; Wong, Man-Hon; Leung, Kwong-Sak: Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm (2011) ioport
  12. Basu, Subhadip; Plewczynski, Dariusz: AMS 3.0: prediction of post-translational modifications (2010) ioport
  13. Comin, Matteo; Verzotto, Davide: Classification of protein sequences by means of irredundant patterns (2010) ioport
  14. Liu, Yichuan; Tozeren, Aydin: Modular composition predicts kinase/substrate interactions (2010) ioport
  15. Paszkowski-Rogacz, Maciej; Slabicki, Mikolaj; Pisabarro, M. Teresa; Buchholz, Frank: Phenofam-gene set enrichment analysis through protein structural information (2010) ioport
  16. Rahbar, Mohammad Reza; Rasooli, Iraj; Mousavi Gargari, Seyed Latif; Amani, Jafar; Fattahian, Yaser: In silico analysis of antibody triggering biofilm associated protein in \textitAcinetobacterbaumannii (2010)
  17. Sigrist, Christian J. A.; Cerutti, Lorenzo; De Castro, Edouard; Langendijk-Genevaux, Petra S.; Bulliard, Virginie; Bairoch, Amos; Hulo, Nicolas: PROSITE, a protein domain database for functional characterization and annotation (2010) ioport
  18. Anwar, Nadia; Hunt, Ela: \textitfrancisellatularensis novicidaproteomic and transcriptomic data integration and annotation based on semantic web technologies (2009) ioport
  19. Dyrka, Witold; Nebel, Jean-Christophe: A stochastic context free grammar based framework for analysis of protein sequences (2009) ioport
  20. Liang, Chunguang; Schmid, Alexander; López-Sánchez, María José; Moya, Andres; Gross, Roy; Bernhardt, Jörg; Dandekar, Thomas: JANE: efficient mapping of prokaryotic ests and variable length sequence reads on related template genomes (2009) ioport

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