HMMER

HMMER web server: interactive sequence similarity searching. HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them


References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Peyravi, Farzad; Latif, Alimohammad; Moshtaghioun, Seyed Mohammad: A composite approach to protein tertiary structure prediction: hidden Markov model based on lattice (2019)
  2. Ignacio Ferres; Gregorio Iraola: Phylen: automatic phylogenetic reconstruction using the EggNOG database (2018) not zbMATH
  3. Hanyu Jiang, Narayan Ganesan, Yu-Dong Yao: CUDAMPF++: A Proactive Resource Exhaustion Scheme for Accelerating Homologous Sequence Search on CUDA-enabled GPU (2017) arXiv
  4. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  5. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  6. Figueiredo Neto, Manoel; Figueiredo, Marxa L.: Skeletal muscle signal peptide optimization for enhancing propeptide or cytokine secretion (2016)
  7. Arango-Argoty, G. A.; Jaramillo-Garzón, J. A.; Castellanos-Domínguez, G.: Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins (2015)
  8. Song, Tao; Gu, Hong: Discovering short linear protein motif based on selective training of profile hidden Markov models (2015)
  9. Desai, Dhwani K.; Nandi, Soumyadeep; Srivastava, Prashant K.; Lynn, Andrew M.: ModEnzA: accurate identification of metabolic enzymes using function specific profile HMMs with optimised discrimination threshold and modified emission probabilities (2011)
  10. Wolfsheimer, Stefan; Herms, Inke; Rahmann, Sven; Hartmann, Alexander K.: Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling (2011) ioport
  11. Milone, Diego H.; Di Persia, Leandro E.; Torres, María E.: Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees (2010)
  12. Dyrka, Witold; Nebel, Jean-Christophe: A stochastic context free grammar based framework for analysis of protein sequences (2009) ioport
  13. Newberg, Lee Aaron: Error statistics of hidden Markov model and hidden Boltzmann model results (2009) ioport
  14. Gollery, Martin: Handbook of hidden Markov models in bioinformatics. With CD-ROM (2008)
  15. Baker, Christopher J. O.; Witte, René: Mutation mining-A prospector’s tale (2006) ioport
  16. Plötz, Thomas; Fink, Gernot A.: Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs (2006)
  17. Portugaly, Elon; Harel, Amir; Linial, Nathan; Linial, Michal: EVEREST: Automatic identification and classification of protein domains in all protein sequences (2006) ioport


Further publications can be found at: http://hmmer.org/publications.html