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.affiliation: Department of Computer Science and Engineering University of California at San Diego, La Jolla 92093-0114, USA.
authors.forename: T L
authors.initials: TL
authors.lastname: Bailey
authors.forename: C
authors.initials: C
authors.lastname: Elkan
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D004247:DNA / Q000737:chemistry*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins; D004247:DNA