Deterministic finite automaton meme1/23/2024 ![]() The evaluated scoring functions are the log-likelihood of a PWM, Z-scores, and a sequence specificity score. They compare, for each dataset, the predicted motif's scores to the score of the true motif. Li and Tompa ( 2006) complement their earlier paper (Tompa et al., 2005) by assessing several scoring functions. Besides the motif model, the scoring function plays an important role. Consequently, a split benchmark set is proposed: the first part contains datasets with motifs that can in principle be recognized and can therefore serve as a benchmark for algorithms based on such models the second part contains the remaining datasets, useful to evaluate more powerful models. Remarkably, all these models turn out to have comparable discriminative power, but are not sufficient to capture all motifs. ( 2007) studied the ability of popular motif models (PWMs, IUPAC strings, mismatch models) to separate the true motifs from the background. Recently, steps have been taken to precisely understand what makes the problem so difficult. For a detailed overview of the field, we refer the reader to the review of Sandve and Drabløs ( 2006).ĭespite all these efforts, the problem has not satisfactorily been solved yet, as shown in the assessment of 13 common motif discovery algorithms by Tompa et al. For motif discovery the maximum density subgraph is searched. Then, a motif is represented by a subgraph. MotifCut (Fratkin et al., 2006) approaches the motif discovery problem from a graph theoretic point of view and represents every k-mer in a given set of sequences as a vertex. Seeder (Fauteux et al., 2008) is a recently published algorithm that tries to combine the merits of a pattern-driven search (used in a first phase) and alignment-based search (used in a second phase). ![]() Although not as good as Weeder, MEME performed well in the assessment by Tompa et al. Motifs are represented as position weight matrices (PWMs) and optimized using an expectation–maximization (EM) strategy. MEME (Bailey and Elkan, 1994) is an almost classical alignment-based motif discovery algorithm. ( 2005), Weeder outperformed 12 other competitors with respect to most measures. This is achieved by a pattern-driven search using a suffix tree of the given sequences. Given a set of sequences, it searches for motifs that occur (with a bounded number of mismatches) in as many sequences as possible. Weeder (Pavesi et al., 2004) models motifs as strings. Due to space constraints, we can review only a few of the methods here. In a review article, Sandve and Drabløs ( 2006) survey more than 100 published algorithms for motif discovery. Many different measures of ‘exceptionality’ have been proposed. ![]() Especially in the context of biological sequences, this problem has been extensively studied in the hope that over-represented motifs carry structural, regulatory or other biological significance. It can be obtained from ĭe novo motif discovery is the task of uncovering exceptional patterns in texts. We also propose new motifs on Mycobacterium tuberculosis.Īvailability and Implementation: The method has been implemented in Java. (iv) We justify the use of the proposed scores for motif discovery by showing our method to outperform other motif discovery algorithms (e.g. The method exploits monotonicity properties of the compound Poisson approximation and is by orders of magnitude faster than exhaustive enumeration of IUPAC strings (11.8 h compared with an extrapolated runtime of 4.8 years). (iii) We describe an algorithm to discover the optimal pattern with respect to either of the scores. (ii) We define two p-value scores for over-representation, the first one based on the total number of motif occurrences, the second one based on the number of sequences in a collection with at least one occurrence. We show how to compute the exact clump size distribution using a recently introduced device called probabilistic arithmetic automaton (PAA). In particular, (i) we use a highly accurate compound Poisson approximation for the null distribution of the number of motif occurrences. model or a Markov model as the measure of over-representation. Results: We show how to solve the motif discovery problem (almost) exactly on a practically relevant space of IUPAC generalized string patterns, using the p-value with respect to an i.i.d. ![]() Even for well-defined formalizations, the problem is frequently solved in an ad hoc manner with heuristics that do not guarantee to find the best motif. This is partly due to the large number of possibilities of defining the motif search space and the notion of over-representation. It is one of the classical sequence analysis problems, but still has not been satisfactorily solved in an exact and efficient manner. Motivation: The motif discovery problem consists of finding over-represented patterns in a collection of biosequences. ![]()
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