NIF LinkOut Portal

Options
Only Pubmed Central
Include Pubmed Central
Sections
Title
Abstract
Introduction
Methods
Results
Supplement
Appendix
Contributions
Background
Commentary
Funding
Limitations
Caption
FILTERS

High-performance exact algorithms for motif search.

Authors:
Rajasekaran S, Balla S, Huang CH, Thapar V, Gryk M, Maciejewski M, Schiller M
Affiliation:
Journal:
Journal of clinical monitoring and computing

Abstract

OBJECTIVE: The human genome project has resulted in the generation of voluminous biological data. Novel computational techniques are called for to extract useful information from this data. One such technique is that of finding patterns that are repeated over many sequences (and possibly over many species). In this paper we study the problem of identifying meaningful patterns (i.e., motifs) from biological data, the motif search problem. METHODS: The general version of the motif search problem is NP-hard. Numerous algorithms have been proposed in the literature to solve this problem. Many of these algorithms fall under the category of heuristics. We concentrate on exact algorithms in this paper. In particular, we concentrate on two different versions of the motif search problem and offer exact algorithms for them. RESULTS: In this paper we present algorithms for two versions of the motif search problem. All of our algorithms are elegant and use only such simple data structures as arrays. For the first version of the problem described as Problem 1 in the paper, we present a simple sorting based algorithm, SMS (Simple Motif Search). This algorithm has been coded and experimental results have been obtained. For the second version of the problem (described in the paper as Problem 2), we present two different algorithms--a deterministic algorithm (called DMS) and a randomized algorithm (Monte Carlo algorithm). We also show how these algorithms can be parallelized. CONCLUSIONS: All the algorithms proposed in this paper are improvements over existing algorithms for these versions of motif search in biological sequence data. The algorithms presented have the potential of performing well in practice.

  1. Welcome

    Welcome to NIF. Explore available research resources: data, tools and materials, from across the web

  2. Community Resources

    Search for resources specially selected for NIF community

  3. More Resources

    Search across hundreds of additional biomedical databases

  4. Literature

    Search Pub Med abstracts and full text from PubMed Central

  5. Insert your Query

    Enter your search terms here and hit return. Search results for the selected tab will be returned.

  6. Join the Community

    Click here to login or register and join this community.

  7. Categories

    Narrow your search by selecting a category. For additional help in searching, view our tutorials.

  8. Query Info

    Displays the total number of search results. Provides additional information on search terms, e.g., automated query expansions, and any included categories or facets. Expansions, filters and facets can be removed by clicking on the X. Clicking on the + restores them.

  9. Search Results

    Displays individual records and a brief description. Click on the icons below each record to explore additional display options.

X