Combinatorial Approaches to Finding Subtle Signals in DNA Sequences

By Pavel A. Pevzner, Sing-Hoi Sze. Intelligent Systems in Molecular Biology, 2000

643 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c950a3da3aa755e3cc25a304e9bf25cc2b56fea1

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Citations in the corpus

Finding Motifs in Time Series (2002, methodology): “We call such patterns motifs, because of their close analogy to their discrete counterparts in computation biology [11, 16, 30, 34, 36].”
Probabilistic discovery of time series motifs (2003, methodology): “Pattern discovery algorithms for biosequences have recently received increased attention from researchers, in particular after the challenge by Pevzner and Sze [28] (see below).”
Combining phylogenetic data with co-regulated genes to identify regulatory motifs (2003, methodology): “When genes of a single species share a com-monregulatorymechanism, thetaskofﬁndingtheirregulatory motifs can be abstracted to a ‘planted motif problem’ (Pevzner and Sze, 2000), i.e. ﬁnding one or more degenerate motifs embedded in otherwise unrelated random sequences.”
Finding composite regulatory patterns in DNA sequences (2002, methodology): “Sagotandcolleagueswere the first to rebut this opinion andto develop an efficient version of the Watermanet al. (1984) algorithm that was successfully applied to analyzedifficult biological samples(Sagot(1998); Marsan& Sagot(2000); Vanet et al. (1999)).”
A survey of DNA motif finding algorithms (2007, methodology): “The algorithms WINNOWER [36] and cWINNOWER [37] uses word-based approach combined with graph-theoretic methods for motif finding.”
An algorithm for finding signals of unknown length in DNA sequences (2001, methodology): “…widely used algorithms the pattern driven approach has been abandoned, and the signal is extracted by analyzing and comparing the patterns that occur in the sequences (see for example (Lawrence et al., 1993; Bailey & Elkan, 1995; Hertz & Stormo, 1999; Pevzner & Sze, 2000; Buhler & Tompa, 2001)).”
Assessing computational tools for the discovery of transcription factor binding sites (2005, background): “There have been only a few small-scale assessments of some of these motif discovery tools, for instance, those of Pevzner & Sz”
Finding Motifs Using Random Projections (2001, unknown): “Pevzner and Sze (2000) showed such problems to be intractable to most existing motif nders even for l D 15 and d D 4, so they are natural test cases for our algorithm.”
MEME: discovering and analyzing DNA and protein sequence motifs (2006, background): “Ideally, the sequences should be less than 1000 base-pairs long [6].”