Title
MADMX: a novel strategy for maximal dense motif extraction
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.
Year
DOI
Venue
2009
10.1007/978-3-642-04241-6_30
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Keywords
DocType
Volume
bottom-up fashion,total length,new tool,experimental evidence,biological sequence,novel strategy,maximal dense motif extraction,output size,maximal dense motif,flexible measure,frequent motif
Conference
abs/1002.0874
ISSN
ISBN
Citations 
0302-9743
3-642-04240-6
3
PageRank 
References 
Authors
0.41
12
6
Name
Order
Citations
PageRank
Roberto Grossi158157.47
Andrea Pietracaprina230.75
Nadia Pisanti330230.91
Geppino Pucci444350.49
Eli Upfal54310743.13
Fabio Vandin621827.55