Simple and practical sequence nearest neighbors with block operations

Shan Muthukrishnan, S. Cenk Ṣahinalp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Sequence nearest neighbors problemcan be defined as follows. Given a database D of n sequences, preprocess D so that given any query sequence Q, one can quickly find a sequence S in D for which d(S, Q) ≤ d(S, T) for any other sequence T in D. Here d(S, Q) denotes the “distance” between sequences S and Q, which can be defined as the minimum number of “edit operations” to transform one sequence into the other. The edit operations considered in this paper include single character edits (insertions, deletions, replacements) as well as block (substring) edits (copying, uncopying and relocating blocks). One of the main application domains for the sequence nearest neighbors problem is computational genomics where available tools for sequence comparison and search usually focus on edit operations involving single characters only. While such tools are useful for capturing certain evolutionary mechanisms (mainly point mutations), they may have limited applicability for understanding mechanisms for segmental rearrangements (duplications, translocations and deletions) underlying genome evolution. Recent improvements towards the resolution of the human genome composition suggest that such segmental rearrangements are much more common than what was estimated before. Thus there is substantial need for incorporating similarity measures that capture block edit operations in genomic sequence comparison and search.1 Unfortunately even the computation of a block edit distance between two sequences under any set of non-trivial edit operations is NP-hard. The first efficient data structure for approximate sequence nearest neighbor search for any set of non-trivial edit operations were described in [11]; the measure considered in this paper is the block edit distance. This method achieves a preprocessing time and space polynomial in size of D and query time near-linear in size of Q by allowing an approximate factor of O (log £ (log* £)). The approach involves embedding sequences into Hamming space so that approximating Hamming distances estimates sequence block edit distances within the approximation ratio above. In this study we focus on simplification and experimental evaluation of the [11] method. We first describe how we implement and test the accuracy of the transformations provided in [] in terms of estimating the block edit distance under controlled data sets. Then, based on the hamming distance estimator described in [3] we present a data structure for computing approximate nearest neighbors in hamming space; this is simpler than the well-known ones in [9,6]. We finally report on how well the combined data structure performs for sequence nearest neighbor search under block edit distance.

Original languageEnglish (US)
Title of host publicationCombinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings
EditorsAlberto Apostolico, Masayuki Takeda
PublisherSpringer Verlag
Pages262-278
Number of pages17
ISBN (Electronic)9783540438625
StatePublished - Jan 1 2002
Event13th Annual Symposium on Combinatorial Pattern Matching, CPM 2002 - Fukuoka, Japan
Duration: Jul 3 2002Jul 5 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2373
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Annual Symposium on Combinatorial Pattern Matching, CPM 2002
CountryJapan
CityFukuoka
Period7/3/027/5/02

Fingerprint

Data structures
Hamming distance
Nearest Neighbor
Genes
Edit Distance
Copying
Polynomials
Nearest Neighbor Search
Sequence Comparison
Data Structures
Hamming Distance
Chemical analysis
Rearrangement
Nearest neighbor search
Deletion
Genomics
Genome
Query
Translocation
Duplication

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Muthukrishnan, S., & Cenk Ṣahinalp, S. (2002). Simple and practical sequence nearest neighbors with block operations. In A. Apostolico, & M. Takeda (Eds.), Combinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings (pp. 262-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2373). Springer Verlag.
Muthukrishnan, Shan ; Cenk Ṣahinalp, S. / Simple and practical sequence nearest neighbors with block operations. Combinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings. editor / Alberto Apostolico ; Masayuki Takeda. Springer Verlag, 2002. pp. 262-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Sequence nearest neighbors problemcan be defined as follows. Given a database D of n sequences, preprocess D so that given any query sequence Q, one can quickly find a sequence S in D for which d(S, Q) ≤ d(S, T) for any other sequence T in D. Here d(S, Q) denotes the “distance” between sequences S and Q, which can be defined as the minimum number of “edit operations” to transform one sequence into the other. The edit operations considered in this paper include single character edits (insertions, deletions, replacements) as well as block (substring) edits (copying, uncopying and relocating blocks). One of the main application domains for the sequence nearest neighbors problem is computational genomics where available tools for sequence comparison and search usually focus on edit operations involving single characters only. While such tools are useful for capturing certain evolutionary mechanisms (mainly point mutations), they may have limited applicability for understanding mechanisms for segmental rearrangements (duplications, translocations and deletions) underlying genome evolution. Recent improvements towards the resolution of the human genome composition suggest that such segmental rearrangements are much more common than what was estimated before. Thus there is substantial need for incorporating similarity measures that capture block edit operations in genomic sequence comparison and search.1 Unfortunately even the computation of a block edit distance between two sequences under any set of non-trivial edit operations is NP-hard. The first efficient data structure for approximate sequence nearest neighbor search for any set of non-trivial edit operations were described in [11]; the measure considered in this paper is the block edit distance. This method achieves a preprocessing time and space polynomial in size of D and query time near-linear in size of Q by allowing an approximate factor of O (log £ (log* £)). The approach involves embedding sequences into Hamming space so that approximating Hamming distances estimates sequence block edit distances within the approximation ratio above. In this study we focus on simplification and experimental evaluation of the [11] method. We first describe how we implement and test the accuracy of the transformations provided in [] in terms of estimating the block edit distance under controlled data sets. Then, based on the hamming distance estimator described in [3] we present a data structure for computing approximate nearest neighbors in hamming space; this is simpler than the well-known ones in [9,6]. We finally report on how well the combined data structure performs for sequence nearest neighbor search under block edit distance.",
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Muthukrishnan, S & Cenk Ṣahinalp, S 2002, Simple and practical sequence nearest neighbors with block operations. in A Apostolico & M Takeda (eds), Combinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2373, Springer Verlag, pp. 262-278, 13th Annual Symposium on Combinatorial Pattern Matching, CPM 2002, Fukuoka, Japan, 7/3/02.

Simple and practical sequence nearest neighbors with block operations. / Muthukrishnan, Shan; Cenk Ṣahinalp, S.

Combinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings. ed. / Alberto Apostolico; Masayuki Takeda. Springer Verlag, 2002. p. 262-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2373).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Sequence nearest neighbors problemcan be defined as follows. Given a database D of n sequences, preprocess D so that given any query sequence Q, one can quickly find a sequence S in D for which d(S, Q) ≤ d(S, T) for any other sequence T in D. Here d(S, Q) denotes the “distance” between sequences S and Q, which can be defined as the minimum number of “edit operations” to transform one sequence into the other. The edit operations considered in this paper include single character edits (insertions, deletions, replacements) as well as block (substring) edits (copying, uncopying and relocating blocks). One of the main application domains for the sequence nearest neighbors problem is computational genomics where available tools for sequence comparison and search usually focus on edit operations involving single characters only. While such tools are useful for capturing certain evolutionary mechanisms (mainly point mutations), they may have limited applicability for understanding mechanisms for segmental rearrangements (duplications, translocations and deletions) underlying genome evolution. Recent improvements towards the resolution of the human genome composition suggest that such segmental rearrangements are much more common than what was estimated before. Thus there is substantial need for incorporating similarity measures that capture block edit operations in genomic sequence comparison and search.1 Unfortunately even the computation of a block edit distance between two sequences under any set of non-trivial edit operations is NP-hard. The first efficient data structure for approximate sequence nearest neighbor search for any set of non-trivial edit operations were described in [11]; the measure considered in this paper is the block edit distance. This method achieves a preprocessing time and space polynomial in size of D and query time near-linear in size of Q by allowing an approximate factor of O (log £ (log* £)). The approach involves embedding sequences into Hamming space so that approximating Hamming distances estimates sequence block edit distances within the approximation ratio above. In this study we focus on simplification and experimental evaluation of the [11] method. We first describe how we implement and test the accuracy of the transformations provided in [] in terms of estimating the block edit distance under controlled data sets. Then, based on the hamming distance estimator described in [3] we present a data structure for computing approximate nearest neighbors in hamming space; this is simpler than the well-known ones in [9,6]. We finally report on how well the combined data structure performs for sequence nearest neighbor search under block edit distance.

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Muthukrishnan S, Cenk Ṣahinalp S. Simple and practical sequence nearest neighbors with block operations. In Apostolico A, Takeda M, editors, Combinatorial Pattern Matching - 13th Annual Symposium, CPM 2002, Proceedings. Springer Verlag. 2002. p. 262-278. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).