Paper Info
Title
Computing alignment plots efficiently
Abstract
Dot plots are a standard method for local comparison of biological sequences. In a dot plot, a substring to substring distance is computed for all pairs of fixed-size windows in the input strings. Commonly, the Hamming distance is used since it can be computed in linear time. However, the Hamming distance is a rather crude measure of string similarity, and using an alignment-based edit distance can greatly improve the sensitivity of the dot plot method. In this paper, we show how to compute alignment plots of the latter type efficiently. Given two strings of length m and n and a window size w, this problem consists in computing the edit distance between all pairs of substrings of length w, one from each input string. The problem can be solved by repeated application of the standard dynamic programming algorithm in time O(mnw(2)). This paper gives an improved data-parallel algorithm, running in time O(mnw/gamma/p) using vector operations that work on gamma values in parallel and p processors.
Year
DOI
Venue
2009
10.3233/978-1-60750-530-3-158
PARALLEL COMPUTING: FROM MULTICORES AND GPU'S TO PETASCALE
Keywords
Field
DocType
cluster computing,hamming distance,dynamic programming algorithm,data structure,discrete mathematics,linear time,edit distance
String-to-string correction problem,Edit distance,Discrete mathematics,Wagner–Fischer algorithm,Algorithm,Jaro–Winkler distance,Hamming distance,Damerau–Levenshtein distance,Approximate string matching,Hamming weight,Mathematics
Journal
Volume
ISSN
Citations 
19
0927-5452
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Peter Krusche1252.83
Alexander Tiskin222015.50