Mismatch sampling

Raphaël Clifford; Klim Efremenko; Benny Porat; Ely Porat; Amir Rothschild

doi:10.1007/978-3-540-89097-3_11

Mismatch sampling

Raphaël Clifford, Klim Efremenko, Benny Porat, Ely Porat, Amir Rothschild

Research output: Contribution to journal › Conference article › peer-review

Abstract

We consider the well known problem of pattern matching under the Hamming distance. Previous approaches have shown how to count the number of mismatches efficiently, especially when a bound is known for the maximum Hamming distance. Our interest is different in that we wish collect a random sample of mismatches of fixed size at each position in the text. Given a pattern p of length m and a text t of length n, we show how to sample with high probability c mismatches where possible from every alignment of p and t in O((c∈+∈logn) (n∈+∈mlogm)logm) time. Further, we guarantee that the mismatches are sampled uniformly and can therefore be seen as representative of the types of mismatches that occur.

Original language	English
Pages (from-to)	99-108
Number of pages	10
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5280 LNCS
DOIs	https://doi.org/10.1007/978-3-540-89097-3_11
State	Published - 1 Jan 2008
Externally published	Yes
Event	15th International Symposium on String Processing and Information Retrieval, SPIRE 2008 - Melbourne. VIC, Australia Duration: 10 Nov 2008 → 12 Nov 2008

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-89097-3_11

Cite this

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title = "Mismatch sampling",

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AU - Clifford, Raphaël

AU - Efremenko, Klim

AU - Porat, Benny

AU - Porat, Ely

AU - Rothschild, Amir

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Y1 - 2008/1/1

N2 - We consider the well known problem of pattern matching under the Hamming distance. Previous approaches have shown how to count the number of mismatches efficiently, especially when a bound is known for the maximum Hamming distance. Our interest is different in that we wish collect a random sample of mismatches of fixed size at each position in the text. Given a pattern p of length m and a text t of length n, we show how to sample with high probability c mismatches where possible from every alignment of p and t in O((c∈+∈logn) (n∈+∈mlogm)logm) time. Further, we guarantee that the mismatches are sampled uniformly and can therefore be seen as representative of the types of mismatches that occur.

AB - We consider the well known problem of pattern matching under the Hamming distance. Previous approaches have shown how to count the number of mismatches efficiently, especially when a bound is known for the maximum Hamming distance. Our interest is different in that we wish collect a random sample of mismatches of fixed size at each position in the text. Given a pattern p of length m and a text t of length n, we show how to sample with high probability c mismatches where possible from every alignment of p and t in O((c∈+∈logn) (n∈+∈mlogm)logm) time. Further, we guarantee that the mismatches are sampled uniformly and can therefore be seen as representative of the types of mismatches that occur.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 15th International Symposium on String Processing and Information Retrieval, SPIRE 2008

Y2 - 10 November 2008 through 12 November 2008

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Mismatch sampling

Abstract

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