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Title: The value of prior knowledge in discovering motifs with MEME

Abstract

MEME is a tool for discovering motifs in sets of protein or DNA sequences. This paper describes several extensions to MEME which increase its ability to find motifs in a totally unsupervised fashion, but which also allow it to benefit when prior knowledge is available. When no background knowledge is asserted, MEME obtains increased robustness from a method for determining motif widths automatically, and from probabilistic models that allow motifs to be absent in some input sequences. On the other hand, MEME can exploit prior knowledge about a motif being present in all input sequences, about the length of a motif and whether it is a palindrome, and (using Dirichlet mixtures) about expected patterns in individual motif positions. Extensive experiments are reported which support the claim that MEME benefits from, but does not require, background knowledge. The experiments use seven previously studied DNA and protein sequence families and 75 of the protein families documented in the Prosite database of sites and patterns, Release 11.1.

Authors:
;  [1]
  1. Univ. of California at San Diego, La Jolla, CA (United States)
Publication Date:
Research Org.:
Stanford Univ., CA (United States)
OSTI Identifier:
401825
Report Number(s):
CONF-9507246-
TRN: 96:005602-0003
Resource Type:
Technical Report
Resource Relation:
Conference: Intelligent Systems for Molecular Biology (ISMB) conference, Cambridge (United Kingdom), 16-19 Jul 1995; Other Information: PBD: 1995; Related Information: Is Part Of ISMB-95 -- Third international conference on intelligent systems for molecular biology: Proceedings; Rawlings, C.; Clark, D.; Altman, R.; Hunter, L.; Lengauer, T.; Wodak, S. [eds.]; PB: 427 p.
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; DNA; M CODES; PROTEIN STRUCTURE; MIXTURES; MOLECULAR BIOLOGY; DNA SEQUENCING; PROBABILITY

Citation Formats

Bailey, T.L., and Elkan, C. The value of prior knowledge in discovering motifs with MEME. United States: N. p., 1995. Web.
Bailey, T.L., & Elkan, C. The value of prior knowledge in discovering motifs with MEME. United States.
Bailey, T.L., and Elkan, C. Sun . "The value of prior knowledge in discovering motifs with MEME". United States. doi:.
@article{osti_401825,
title = {The value of prior knowledge in discovering motifs with MEME},
author = {Bailey, T.L. and Elkan, C.},
abstractNote = {MEME is a tool for discovering motifs in sets of protein or DNA sequences. This paper describes several extensions to MEME which increase its ability to find motifs in a totally unsupervised fashion, but which also allow it to benefit when prior knowledge is available. When no background knowledge is asserted, MEME obtains increased robustness from a method for determining motif widths automatically, and from probabilistic models that allow motifs to be absent in some input sequences. On the other hand, MEME can exploit prior knowledge about a motif being present in all input sequences, about the length of a motif and whether it is a palindrome, and (using Dirichlet mixtures) about expected patterns in individual motif positions. Extensive experiments are reported which support the claim that MEME benefits from, but does not require, background knowledge. The experiments use seven previously studied DNA and protein sequence families and 75 of the protein families documented in the Prosite database of sites and patterns, Release 11.1.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Dec 31 00:00:00 EST 1995},
month = {Sun Dec 31 00:00:00 EST 1995}
}

Technical Report:
Other availability
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