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

References in zbMATH (referenced in 15 articles )

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  1. Axelson-Fisk, Marina: Comparative gene finding. Models, algorithms and implementation (2015)
  2. Marschall, Tobias: Construction of minimal deterministic finite automata from biological motifs (2011)
  3. 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
  4. 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
  5. Rendon, Gloria; Ger, Mao-Feng; Kantorovitz, Ruth; Natarajan, Shreedhar; Tilson, Jeffrey; Jakobsson, Eric: Understanding the “horizontal dimension” of molecular evolution to annotate, classify, and discover proteins with functional domains (2009) ioport
  6. Xu, Ying: Computational challenges in deciphering genomic structures of bacteria (2009) ioport
  7. Hernandez, David; Gras, Robin; Appel, Ron: Neighborhood functions and hill-climbing strategies dedicated to the generalized ungapped local multiple alignment (2008)
  8. Hulo, Nicolas; Bairoch, Amos; Bulliard, Virginie; Cerutti, Lorenzo; Cuche, Béatrice A.; De Castro, Edouard; Lachaize, Corinne; Langendijk-Genevaux, Petra S.; Sigrist, Christian J. A.: The 20 years of PROSITE. (2008) ioport
  9. Ng, Yen Kaow; Shinohara, Takeshi: Developments from enquiries into the learnability of the pattern languages from positive data (2008)
  10. Chung, Yun-Sheng; Lu, Chin Lung; Tang, Chuan Yi: Efficient algorithms for regular expression constrained sequence alignment (2007)
  11. Yu, Huan; Qian, Minping; Deng, Minghua: Understanding protein-protein interactions: from domain level to motif level (2007)
  12. Hulo, Nicolas; Bairoch, Amos; Bulliard, Virginie; Cerutti, Lorenzo; De Castro, Edouard; Langendijk-Genevaux, Petra S.; Pagni, Marco; Sigrist, Christian J. A.: The PROSITE database. (2006) ioport
  13. Bologna, Guido: Is it worth generating rules from neural network ensembles? (2004)
  14. Gonnet, Gaston H.: Some string matching problems from bioinformatics which still need better solutions (2004)
  15. Hernandez, David; Gras, Robin; Appel, Ron: MoDEL: an efficient strategy for ungapped local multiple alignment (2004)