Mismatch sampling

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

doi:10.1016/j.ic.2012.02.007

Mismatch sampling

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

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

We reconsider 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 to 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 up to c mismatches 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)	112-118
Number of pages	7
Journal	Information and Computation
Volume	214
DOIs	https://doi.org/10.1016/j.ic.2012.02.007
State	Published - May 2012

Bibliographical note

Funding Information:
This work was supported in part by the Binational Science Foundation (BSF) grant 2006334 and Israel Science Foundation (ISF) grant 1484/08 as well as the Engineering and Physical Sciences Research Council (EPSRC).

Funding

This work was supported in part by the Binational Science Foundation (BSF) grant 2006334 and Israel Science Foundation (ISF) grant 1484/08 as well as the Engineering and Physical Sciences Research Council (EPSRC).

Funders	Funder number
Engineering and Physical Sciences Research Council	EP/J011940/1
United States-Israel Binational Science Foundation	2006334
Israel Science Foundation	1484/08

Access to Document

10.1016/j.ic.2012.02.007

Cite this

@article{15d973d76abd4928aa0e18f5fd8378f1,

title = "Mismatch sampling",

abstract = "We reconsider 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 to 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 up to c mismatches 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.",

author = "Rapha{\"e}l Clifford and Klim Efremenko and Benny Porat and Ely Porat and Amir Rothschild",

note = "Funding Information: This work was supported in part by the Binational Science Foundation (BSF) grant 2006334 and Israel Science Foundation (ISF) grant 1484/08 as well as the Engineering and Physical Sciences Research Council (EPSRC).",

year = "2012",

month = may,

doi = "10.1016/j.ic.2012.02.007",

language = "אנגלית",

volume = "214",

pages = "112--118",

journal = "Information and Computation",

issn = "0890-5401",

publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Mismatch sampling

AU - Clifford, Raphaël

AU - Efremenko, Klim

AU - Porat, Benny

AU - Porat, Ely

AU - Rothschild, Amir

N1 - Funding Information: This work was supported in part by the Binational Science Foundation (BSF) grant 2006334 and Israel Science Foundation (ISF) grant 1484/08 as well as the Engineering and Physical Sciences Research Council (EPSRC).

PY - 2012/5

Y1 - 2012/5

N2 - We reconsider 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 to 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 up to c mismatches 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 reconsider 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 to 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 up to c mismatches 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.

UR - http://www.scopus.com/inward/record.url?scp=84858736631&partnerID=8YFLogxK

U2 - 10.1016/j.ic.2012.02.007

DO - 10.1016/j.ic.2012.02.007

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:84858736631

SN - 0890-5401

VL - 214

SP - 112

EP - 118

JO - Information and Computation

JF - Information and Computation

ER -

Mismatch sampling

Abstract

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this