Results 1  10
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14
A novel method for multiple alignment of sequences with repeated and shuffled elements
, 2004
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Fast and simple character classes and bounded gaps pattern matching, with application to protein searching
 Journal of Computational Biology
, 2001
"... The problem of fast exact and approximate searching for a pattern that contains classes of characters and bounded size gaps (CBG) in a text has a wide range of applications, among which a very important one is protein pattern matching (for instance, one PROSITE protein site is associated with the CB ..."
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Cited by 23 (4 self)
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The problem of fast exact and approximate searching for a pattern that contains classes of characters and bounded size gaps (CBG) in a text has a wide range of applications, among which a very important one is protein pattern matching (for instance, one PROSITE protein site is associated with the CBG [RK]  x(2,3)  [DE]  x(2,3)  Y, where the brackets match any of the letters inside, and x(2,3) a gap of length between 2 and 3). Currently, the only way to search for a CBG in a text is to convert it into a full regular expression (RE). However, a RE is more sophisticated than a CBG, and searching for it with a RE pattern matching algorithm complicates the search and makes it slow. This is the reason why we design in this article two new practical CBG matching algorithms that are much simpler and faster than all the RE search techniques. The first one looks exactly once at each text character. The second one does not need to consider all the text characters, and hence it is usually faster than the first one, but in bad cases may have to read the same text character more than once. We then propose a criterion based on the form of the CBG to choose a priori the fastest between both. We also show how to search permitting a few mistakes in the occurrences. We performed many practical experiments using the PROSITE database, and all of them show that our algorithms are the fastest in virtually all cases.
A System for Pattern Matching Applications on Biosequences
, 1993
"... ANREP is a system for finding matches to patterns composed of (1) spacing constraints called "spacers", and (2) approximate matches to "motifs" that are, recursively, patterns composed of "atomic" symbols. A user specifies such patterns via a declarative, freeformat, and strongly typed language cal ..."
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Cited by 21 (1 self)
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ANREP is a system for finding matches to patterns composed of (1) spacing constraints called "spacers", and (2) approximate matches to "motifs" that are, recursively, patterns composed of "atomic" symbols. A user specifies such patterns via a declarative, freeformat, and strongly typed language called A that is presented here in a tutorial style through a series of progressively more complex examples. The sample patterns are for protein and DNA sequences, the application domain for which ANREP was specifically created. ANREP provides a unified framework for almost all previously proposed biosequence patterns and extends them by providing approximate matching, a feature heretofore unavailable except for the limited case of individual sequences. The performance of ANREP is discussed and an appendix gives a concise specification of syntax and semantics. A portable C software package implementing ANREP is available via anonymous remote file transfer. Introduction In this paper we present...
Approximate string searching under weighted edit distance
 In Proceedings of the 3rd South American Workshop on String Processing (WSP ’96). Carleton Univ
, 1996
"... Abstract. Let p ∈ Σ ∗ be a string of length m and t ∈ Σ ∗ be a string of length n. The approximate string searching problem is to find all approximate matches of p in t having weighted edit distance at most k from p. We present a new method that preprocesses the pattern into a DFA which scans t onli ..."
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Cited by 11 (1 self)
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Abstract. Let p ∈ Σ ∗ be a string of length m and t ∈ Σ ∗ be a string of length n. The approximate string searching problem is to find all approximate matches of p in t having weighted edit distance at most k from p. We present a new method that preprocesses the pattern into a DFA which scans t online in linear time, thereby recognizing all positions in t where an approximate match ends. We show how to reduce the exponential preprocessing effort and propose two practical algorithms. The first algorithm constructs the states of the DFA up to a certain depth r ≥ 1. It runs in O(Σ  r+1 · m + q · m + n) time and O(Σ  r+1 + Σ  r ·m) space where q ≤ n decreases as r increases. The second algorithm constructs the transitions of the DFA when they are demanded. It runs in O(qs·Σ+qt·m+n) time and O(qs·(Σ+m)) space where qs ≤ qt ≤ n depend on the problem instance. Practical measurements show that our algorithms work well in practice and beat previous methods for problems of interest in molecular biology. 1
Estimating the Probability of Approximate Matches
 In CPM'97, Lecture Notes in Computer Science
, 1997
"... this paper addresses how to define S k (P ) and how to solve the algorithmic subproblems involved in an efficient realization with respect to this definition. Section 2 introduces as our choice for S k (P ) the set of what we call the condensed, canonical edit scripts. Our choice attempts to keep s ..."
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Cited by 8 (0 self)
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this paper addresses how to define S k (P ) and how to solve the algorithmic subproblems involved in an efficient realization with respect to this definition. Section 2 introduces as our choice for S k (P ) the set of what we call the condensed, canonical edit scripts. Our choice attempts to keep small, both (i) the number of edit scripts for which X(s) = 0, and (ii) the size of g(v). Doing so improves the convergence of the estimator as it places S k (P ) and CN k (P ) in closer correspondence. The remaining sections present dynamic programming algorithms for the following subtasks:
Approximate Matching of Secondary Structures
, 2001
"... Several methods have been developed for identifying more or less complex RNA structures in a genome. Whatever the method is, it is always based on the search of conserved primary and secondary structures. While various efficient methods have been developed for searching motifs of the primary structu ..."
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Cited by 4 (0 self)
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Several methods have been developed for identifying more or less complex RNA structures in a genome. Whatever the method is, it is always based on the search of conserved primary and secondary structures. While various efficient methods have been developed for searching motifs of the primary structure, usually represented as regular expressions, few effort has been expended in the efficient search of secondary structure signals. By a helix, we mean a stemloop structure defined by a combination of sequence and folding constraints. We present a flexible algorithm that searches for all approximate matches of a helix in a genome. Helices are represented by special regular expressions, that we call secondary expressions. The method is based on an alignment graph constructed from several copies of a pushdown automaton, arranged one on top of another. The worst time complexity is O(rpn), where n is the size of the genome, p the size of the secondary expression, and r its number of union symbols. We present our results of searching for specific signals of the tRNA and RNase P RNA in two genomes.
Efficient Bitparallel Algorithms for (δ, α)matching
"... Abstract. We consider the following string matching problem. Pattern p0p1p2... pm−1 (δ, α)matches the text substring ti0ti1ti2... ti m−1, if pj − ti j  ≤ δ for j ∈ {0,..., m − 1}, where 0 < ij+1 − ij ≤ α + 1. The task is then to find all text positions im−1 that (δ, α)match the pattern. For a t ..."
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Cited by 2 (2 self)
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Abstract. We consider the following string matching problem. Pattern p0p1p2... pm−1 (δ, α)matches the text substring ti0ti1ti2... ti m−1, if pj − ti j  ≤ δ for j ∈ {0,..., m − 1}, where 0 < ij+1 − ij ≤ α + 1. The task is then to find all text positions im−1 that (δ, α)match the pattern. For a text of length n, the best previously known algorithms for this string matching problem run in time O(nm) and in time O(n⌈mα/w⌉), where the former is based on dynamic programming, and the latter on bitparallelism with w bits in computer word (32 or 64 typically). We improve these to take O(nδ+⌈n/w⌉m) and O(n⌈m log(α)/w⌉), respectively, worst case time using bitparallelism. On average the algorithms run in O(⌈n/w⌉⌈αδ/σ⌉+n) and O(n) time. Our experimental results show that the algorithms work extremely well in practice. Our algorithms handle general gaps as well, having important applications in computational biology.
A Pattern Language for Molecular Biology
, 1995
"... In this paper we have formalised and studied a language for describing constrained patterns in biosequences. We have developed an efficient and elegant algorithm for finding a given pattern in a sequence. The efficiency of the algorithm is determined by the fact that it does not use backtracking unl ..."
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Cited by 2 (1 self)
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In this paper we have formalised and studied a language for describing constrained patterns in biosequences. We have developed an efficient and elegant algorithm for finding a given pattern in a sequence. The efficiency of the algorithm is determined by the fact that it does not use backtracking unlike other algorithms for dealing with constrained patterns. Key words: biosequences, patterns, search 1 Introduction During the last decade molecular biologists have focussed more and more attention to finding patterns in biosequences. There are several reasons for this interest. For instance, if we can find a common pattern present in DNA sequences believed to be related to gene regulation, then finding the same pattern elsewhere in DNA suggests that the respective part of the DNA may also plays role as a regulatory region [13]. Finding common patterns in protein sequences helps in predicting their three dimensional structure [4]. One of the many problems in research related to patterns i...
Book review: Devereux Sequence Analysis by Gribskov and Devereux
, 1992
"... pter 1, "DNA" by Peter Rice, Keith Elliston, and Michael Gribskov, covers sequence assembly (assembly of complete DNA sequences from experimentally determined fragments), identification of simple sites and transcriptional signals (patterns governing the translation of DNA into protein), coding regio ..."
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pter 1, "DNA" by Peter Rice, Keith Elliston, and Michael Gribskov, covers sequence assembly (assembly of complete DNA sequences from experimentally determined fragments), identification of simple sites and transcriptional signals (patterns governing the translation of DNA into protein), coding region identification (identifying the regions of DNA that translate into protein), and RNA and DNA structure prediction (predicting the topological structure of folded RNA and DNA). The material on sequence assembly focuses on shotgun data, as it arguably "represents the most difficult problems in sequence assembly," but mainly discusses project management issues. The material on identifying sites and signals concentrates largely on patterns derived from alignments of This book review appeared in Journal of Classification 10, 144148, 1993. y Computer Science Department, University of California at Davis, Davis, California 95616. Electronic ma