Back to Search Start Over

MADMX: A Novel Strategy for Maximal Dense Motif Extraction.

Authors :
Grossi, Roberto
Pietracaprina, Andrea
Pisanti, Nadia
Pucci, Geppino
Upfal, Eli
Vandin, Fabio
Source :
Algorithms in Bioinformatics (9783642042409); 2009, p362-374, 13p
Publication Year :
2009

Abstract

We develop, analyze and experiment with a new tool, called madmx, which extracts frequent motifs, possibly including don΄t care characters, from biological sequences. We introduce density, a simple and flexible measure for bounding the number of don΄t cares in a motif, defined as the ratio of solid (i.e., different from don΄t care) characters to the total length of the motif. By extracting only maximal dense motifs, madmx reduces the output size and improves performance, while enhancing the quality of the discoveries. The efficiency of our approach relies on a newly defined combining operation, dubbed fusion, which allows for the construction of maximal dense motifs in a bottom-up fashion, while avoiding the generation of nonmaximal ones. We provide experimental evidence of the efficiency and the quality of the motifs returned by madmx. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783642042409
Database :
Complementary Index
Journal :
Algorithms in Bioinformatics (9783642042409)
Publication Type :
Book
Accession number :
76740029
Full Text :
https://doi.org/10.1007/978-3-642-04241-6_30