Pfam

Pfam: clans, web tools and services. Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://pfam.cgb.ki.se/).


References in zbMATH (referenced in 102 articles )

Showing results 1 to 20 of 102.
Sorted by year (citations)

1 2 3 4 5 6 next

  1. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  2. Tao, Jin; Liu, Xiaoqing; Yang, Siqian; Bao, Chaohui; He, Pingan; Dai, Qi: An efficient genomic signature ranking method for genomic island prediction from a single genome (2019)
  3. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  4. Kinjo, Akira R.: Cooperative “folding transition” in the sequence space facilitates function-driven evolution of protein families (2018)
  5. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  6. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  7. Kundu, Siddhartha: Mathematical basis of improved protein subfamily classification by a HMM-based sequence filter (2017)
  8. Barton, John P.; Chakraborty, Arup K.; Cocco, Simona; Jacquin, Hugo; Monasson, Rémi: On the entropy of protein families (2016)
  9. Nánási, Michal; Vinař, Tomáš; Brejová, Broňa: Sequence annotation with HMMs: new problems and their complexity (2015)
  10. Song, Tao; Gu, Hong: Discovering short linear protein motif based on selective training of profile hidden Markov models (2015)
  11. Chandola, Varun; Mithal, Varun; Kumar, Vipin: A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences (2014) ioport
  12. Ekeberg, Magnus; Hartonen, Tuomo; Aurell, Erik: Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences (2014)
  13. Hsu, Yi-Yu; Chen, Wei-Jhih; Chen, Shu-Hui; Kao, Hung-Yu: Using hidden Markov models to predict DNA-binding proteins with sequence and structure information (2014) ioport
  14. Mondal, Sukanta; Pai, Priyadarshini P.: Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction (2014)
  15. Neuwald, Andrew F.: Protein domain hierarchy Gibbs sampling strategies (2014)
  16. Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo: Predicting DNA binding proteins using support vector machine with hybrid fractal features (2014)
  17. Binny Priya, S.; Saha, Subhojit; Anishetty, Ramesh; Anishetty, Sharmila: A matrix based algorithm for protein-protein interaction prediction using domain-domain associations (2013)
  18. Cesa-Bianchi, Nicolò; Re, Matteo; Valentini, Giorgio: Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference (2012)
  19. Chopra, Pankaj; Shin, Hanjun; Kang, Jaewoo; Lee, Sunwon: SignatureClust: a tool for landmark gene-guided clustering (2012) ioport
  20. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)

1 2 3 4 5 6 next