scholarly journals A fast bit-vector algorithm for approximate string matching based on dynamic programming

1998 ◽  
pp. 1-13 ◽  
Author(s):  
Gene Myers
Keyword(s):  
Dynamic Programming ◽  
String Matching ◽  
Approximate String Matching ◽  
Bit Vector

scholarly journals LEAP: A Generalization of the Landau-Vishkin Algorithm with Custom Gap Penalties

2017 ◽  
Author(s):  
Hongyi Xin ◽  
Jeremie Kim ◽  
Sunny Nahar ◽  
Can Alkan ◽  
Onur Mutlu
Keyword(s):  
State Of The Art ◽  
String Matching ◽  
The State ◽  
Levenshtein Distance ◽  
Approximate String Matching ◽  
Matching Problem ◽  
De Bruijn Sequence ◽  
Scoring Schemes ◽  
Bit Vector ◽  
Selection Of

AbstractMotivationApproximate String Matching is a pivotal problem in the field of computer science. It serves as an integral component for many string algorithms, most notably, DNA read mapping and alignment. The improved LV algorithm proposes an improved dynamic programming strategy over the banded Smith-Waterman algorithm but suffers from support of a limited selection of scoring schemes. In this paper, we propose the Leaping Toad problem, a generalization of the approximate string matching problem, as well as LEAP, a generalization of the Landau-Vishkin’s algorithm that solves the Leaping Toad problem under a broader selection of scoring schemes.ResultsWe benchmarked LEAP against 3 state-of-the-art approximate string matching implementations. We show that when using a bit-vectorized de Bruijn sequence based optimization, LEAP is up to 7.4x faster than the state-of-the-art bit-vector Levenshtein distance implementation and up to 32x faster than the state-of-the-art affine-gap-penalty parallel Needleman Wunsch Implementation.AvailabilityWe provide an implementation of LEAP in C++ at github.com/CMU-SAFARI/[email protected], [email protected] or [email protected]

scholarly journals Optimum Search Schemes for Approximate String Matching Using Bidirectional FM-Index

2018 ◽  
Author(s):  
Kiavash Kianfar ◽  
Christopher Pockrandt ◽  
Bahman Torkamandi ◽  
Haochen Luo ◽  
Knut Reinert
Keyword(s):  
Dynamic Programming ◽  
Optimization Problem ◽  
Ad Hoc ◽  
Hamming Distance ◽  
String Matching ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Approximate String Matching ◽  
Optimal Search

AbstractFinding approximate occurrences of a pattern in a text using a full-text index is a central problem in bioinformatics and has been extensively researched. Bidirectional indices have opened new possibilities in this regard allowing the search to start from anywhere within the pattern and extend in both directions. In particular, use of search schemes (partitioning the pattern and searching the pieces in certain orders with given bounds on errors) can yield significant speed-ups. However, finding optimal search schemes is a difficult combinatorial optimization problem.Here for the first time, we propose a mixed integer program (MIP) capable to solve this optimization problem for Hamming distance with given number of pieces. Our experiments show that the optimal search schemes found by our MIP significantly improve the performance of search in bidirectional FM-index upon previous ad-hoc solutions. For example, approximate matching of 101-bp Illumina reads (with two errors) becomes 35 times faster than standard backtracking. Moreover, despite being performed purely in the index, the running time of search using our optimal schemes (for up to two errors) is comparable to the best state-of-the-art aligners, which benefit from combining search in index with in-text verification using dynamic programming. As a result, we anticipate a full-fledged aligner that employs an intelligent combination of search in the bidirectional FM-index using our optimal search schemes and in-text verification using dynamic programming that will outperform today’s best aligners. The development of such an aligner, called FAMOUS (Fast Approximate string Matching using OptimUm search Schemes), is ongoing as our future work.

Sublinear approximate string matching and biological applications

Algorithmica ◽  
1994 ◽  
Vol 12 (4-5) ◽  
pp. 327-344 ◽  
Author(s):  
W. I. Chang ◽  
E. L. Lawler
Keyword(s):  
String Matching ◽  
Approximate String Matching ◽  
Biological Applications
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