Identification of Distinguishing Motifs

Author:

Feng Wangsen1,Wang Lusheng2

Affiliation:

1. Peking University, China

2. City University of Hong Kong, China

Abstract

Motif identification for DNA sequences has many important applications in biological studies, including diagnostic probe design, locating binding sites and regulatory signals, and potential drug target identification. There are two versions—the Single Group and Two Groups. Here, the occurrences of the motif in the given sequences have errors. Currently, most of existing programs can only handle the case of single group. However, most of the programs do not allow indels (insertions and deletions) in the occurrences of the motif. In this paper, the authors propose a randomized algorithm for the one group problem that can handle indels in the occurrences of the motif. Finally, an algorithm for the two groups’ problem is given along with extensive simulations evaluating algorithms.

Publisher

IGI Global

Reference26 articles.

1. Bailey, T., & Elkan, C. (1994). Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB-94) (pp. 28-36). Menlo Park, CA: AAAI Press.

2. Unsupervised learning of multiple motifs in biopolymers using expectation maximization

3. Blanchette, M. (2001). Algorithms for phylogenetic footprinting. In Proceedings of the Fifth Annual International Conference on Computational Molecular Biology (RECOMB 01) (pp. 49-58).

4. Finding Motifs Using Random Projections

5. Genetic Design of Drugs Without Side-Effects

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