Meta-MEME is a software toolkit for building and using motif-based hidden Markov models of DNA and proteins. The input to Meta-MEME is a set of similar protein sequences, as well as a set of motif models discovered by MEME. Meta-MEME combines these models into a single, motif-based hidden Markov model and uses this model to search a sequence database for homologs.
- Submit data to Meta-MEME for analysis.
- Learn more about what Meta-MEME does and how it works.
- Answer your questions with the Meta-MEME FAQ.
- Read papers about Meta-MEME and related work.
- Download the Meta-MEME ANSI C source code, as well as program binaries for selected platforms.
- Browse links to web sites related to motif discovery and hidden Markov modeling.
Meta-MEME was developed by William Stafford Noble in the Department of Genome Sciences at the University of Washington and by Timothy Bailey in the Institute for Molecular Bioscience at the University of Queensland, with input from Charles Elkan and Michael Gribskov. Meta-MEME is funded by NSF grant BDI-0078523. Web server resources are provided by the National Biomedical Computation Resource.
Copyright information. Please send comments and questions to Charles Grant at cegrant@u.washington.edu.
