Document Type
Article
Publication Date
10-14-2023
Abstract
In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet, Σ. A motif G = g1g2 . . . gm is a string of m characters. In each background sequence is implanted a probabilistically-generated approximate copy of G. For a probabilistically-generated approximate copy b1b2 . . . bm of G, every character, bi , is probabilistically generated, such that the probability for bi 6= gi is at most α. We develop two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is a lower bound of four. This gives them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other software.
Recommended Citation
Fu, Bin, Yunhui Fu, and Yuan Xue. 2013. "Sublinear Time Motif Discovery from Multiple Sequences" Algorithms 6, no. 4: 636-677. https://doi.org/10.3390/a6040636
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Publication Title
Algorithms
DOI
10.3390/a6040636
Comments
© 2013 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).