T. L. Bailey and M. Gribskov. The megaprior heuristic for discovering protein sequence patterns. In D. J. States, P. Agarwal, T. Gaasterland, L. Hunter, and R. Smith, editors, Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, pages 15--24. AAAI Press, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and a bit score of 0 (the lowest possible bit score) Motif analysis Ungapped motifs are discovered using MEME version 2. 1 [4] with the default parameter settings from the web interface [20] These defaults include empirical Dirichlet mixture priors weighted according to the megaprior heuristic [5], a minimum motif width of 12 and a maximum of 55, and a motif model biased toward zero or one motif occurrence per sequence. A total of ten motifs is discovered from each query set, and motif significance is judged using the majority occurrence heuristic [21] motifs that do not appear in more ....

T. L. Bailey and M. Gribskov. The megaprior heuristic for discovering protein sequence patterns. In D. J. States, P. Agarwal, T. Gaasterland, L. Hunter, and R. Smith, editors, Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, pages 15--24. AAAI Press, 1996.

....sequence. This algorithm is summarized in Figure 1. Motif analysis Ungapped motifs were discovered using MEME version 2. 1 [5] with the default parameter settings from the web interface [19] These defaults include empirical Dirichlet mixture priors weighted according to the megaprior heuristic [6], a minimum motif width of 12 and a maximum of 55, and a motif model biased toward zero or one motif occurrence per sequence. A total of ten motifs was discovered from each query set, and motif significance was judged using the majority occurrence heuristic [20] motifs that did not appear in more ....

T. L. Bailey and M. Gribskov. The megaprior heuristic for discovering protein sequence patterns. In D. J. States, P. Agarwal, T. Gaasterland, L. Hunter, and R. Smith, editors, Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, pages 15--24. AAAI Press, 1996.

....false positives as well. Nevertheless, our experimental results suggest that accurate frequency estimates indeed appear to improve the discriminatory power of profiles. Moreover, our results indicate that the observed data may be more important than was previously thought. Previous studies [7, 1] have suggested that prior information should be weighted relatively heavily. For instance, the constant method uses 50 pseudocounts, and the unique multiple method uses 5 to 100 pseudocounts. In contrast, the minimum risk method uses a wide range of pseudocounts, and uses relies minimally on ....

T. L. Bailey and M. Gribskov. The megaprior heuristic for discovering protein sequence patterns. In D. J. States, P. Agarwal, T. Gaasterland, L. Hunter, and R. Smith, editors, Proceedings, Fourth International Conference on Intelligent Systems in Molecular Biology, pages 15--24, Menlo Park, CA, 1996. AAAI Press.

....more general because we allow motifs to represent patterns that repeat in a single sequence, and because we consider DNA as well as protein motifs. Motifs are ideally suited for, among other things, database searches [22] identifying structurally and functionally important portions of proteins [4] , characterizing DNA protein binding sites [20] and designing degenerate PCR primers [9] MEME discovers motifs automatically given only a group sequences as input. In general, discovering motifs is difficult because the patterns need not be exact; there may be only approximate similarity ....

T. L. Bailey and M. Gribskov. The megaprior heuristic for discovering protein sequence patterns. In D. J. States, P. Agarwal, T. Gaasterland, L. Hunter, and R. Smith, editors, Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, pages 15--24. AAAI Press, 1996.

....we allow motifs to represent patterns that repeat in a single sequence, and because we consider DNA as well as protein motifs. Motifs are ideally suited for, among other things, database searches [Tatusov et al. 1994] identifying structurally and functionally important portions of proteins [Bailey and Elkan, 1996] , characterizing DNA protein binding sites [Schneider et al. 1986] and designing degenerate PCR primers [D Esposito et al. 1994] MEME discovers motifs automatically given only a group sequences as input. 2 SYSTEM AND METHODS 4 In general, discovering motifs is difficult because the patterns ....

T. L. Bailey and C. Elkan. The megaprior heuristic for discovering protein sequence patterns. In Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, page in press. AAAI Press, 1996.

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