MEME

MEME: discovering and analyzing DNA and protein sequence motifs. MEME (Multiple EM for Motif Elicitation) is one of the most widely used tools for searching for novel ‘signals’ in sets of biological sequences. Applications include the discovery of new transcription factor binding sites and protein domains. MEME works by searching for repeated, ungapped sequence patterns that occur in the DNA or protein sequences provided by the user. Users can perform MEME searches via the web server hosted by the National Biomedical Computation Resource (http://meme.nbcr.net) and several mirror sites. Through the same web server, users can also access the Motif Alignment and Search Tool to search sequence databases for matches to motifs encoded in several popular formats. By clicking on buttons in the MEME output, users can compare the motifs discovered in their input sequences with databases of known motifs, search sequence databases for matches to the motifs and display the motifs in various formats. This article describes the freely accessible web server and its architecture, and discusses ways to use MEME effectively to find new sequence patterns in biological sequences and analyze their significance.


References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
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  1. Retzlaff, Nancy; Stadler, Peter F.: Partially local multi-way alignments (2018)
  2. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  3. Qu, Jun-Feng; Yuan, Lei; Huang, Yannong; Wu, Zhao: An efficient algorithm for attribute-based subsequence matching (2016)
  4. O’Donnell, Brian; Maurer, Alexander; Papandreou-Suppappola, Antonia: Biosequence time-frequency processing: pathogen detection and identification (2015)
  5. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  6. Shtokalo, D. N.; Miginsky, D. S.; Lobanov, V. P.; St. Laurent, G. C. III: SWORD: Genetic algorithm tool for protein-RNA interaction motifs recognition (2014)
  7. Comin, Matteo; Verzotto, Davide: Filtering degenerate patterns with application to protein sequence analysis (2013)
  8. Atherton, Juli; Boley, Nathan; Brown, Ben; Ogawa, Nobuo; Davidson, Stuart M.; Eisen, Michael B.; Biggin, Mark D.; Bickel, Peter: A model for sequential evolution of ligands by exponential enrichment (SELEX) data (2012)
  9. Otto, Wolfgang; Stadler, Peter F.; Prohaska, Sonja J.: Phylogenetic footprinting and consistent sets of local aligments (2011)
  10. Hu, Jianjun; Zhang, Fan: Bayesmotif: de novo protein sorting motif discovery from impure datasets (2010) ioport
  11. Redestig, Henning; Weicht, Daniel; Selbig, Joachim; Hannah, Matthew A.: Transcription factor target prediction using multiple short expression time series from arabidopsis thaliana (2007) ioport
  12. Rouchka, Eric C.; Hardin, C. Timothy: Rmotifgen: Random motif generator for DNA and protein sequences (2007) ioport
  13. Bailey, Timothy L.; Williams, Nadya; Misleh, Chris; Li, Wilfred W.: MEME: Discovering and analyzing DNA and protein sequence motifs. (2006) ioport