Title
EDAM: An Efficient Clique Discovery Algorithm with Frequency Transformation for Finding Motifs
Abstract
Finding motifs in DNA sequences plays an important role in deciphering transcriptional regulatory mechanisms and drug target identification. In this paper, we propose an efficient algorithm, EDAM, for finding motifs based on frequency transformation and Minimum Bounding Rectangle (MBR) techniques. It works in three phases,frequency transformation, MBR-clique searching and motif discovery. In frequency transformation, EDAM divides the sample sequences into a set of substrings by sliding windows, then transforms them to frequency vectors which are stored in MBRs. In MBR-clique searching, based on the frequency distance theorems EDAM searches for MBR-cliques used for motif discovery. In motif discovery, EDAM discovers larger cliques by extending smaller cliques with their neighbors. To accelerate the clique discovery, we propose a range query facility to avoid unnecessary computations for clique extension. The experimental results illustrate that EDAM well solves the running time bottleneck of the motif discovery problem in large DNA database.
Year
DOI
Venue
2006
10.1142/9781860947292_0015
Series on Advances in Bioinformatics and Computational Biology
Keywords
Field
DocType
dna sequence,range query,minimum bounding rectangle,drug targeting
Bottleneck,Minimum bounding rectangle,Substring,Clique,DNA database,Range query (data structures),Algorithm,Drug target,Bioinformatics,Mathematics,Computation
Conference
Volume
ISSN
Citations 
3
1751-6404
0
PageRank 
References 
Authors
0.34
13
4
Name
Order
Citations
PageRank
Yifei Ma1387.20
Guoren Wang21366159.46
Yongguang Li300.68
Yuhai Zhao410919.49