Abstract
Identification and characterization of gene regulatory binding motifs is one of the fundamental tasks toward systematically understanding the molecular mechanisms of transcriptional regulation. Recently, the problem has been abstracted as the challenge planted (l,d)-motif problem. Previous studies have developed numerous methods to solve the problem. But most of them need to specify the length l of a planted motif in advance and use depth first search strategy. In this study, we present an exact and efficient algorithm, called Apriori-Motif, without given the length l of a planted motif a priori. And a breadth first search strategy is used to prune search space quickly by the downward closure property utilized in Apriori, which is a classical algorithm for frequent pattern mining. Empirical stu better than some existing methods.
Highlights
In the post-genomic era, a major challenge is represented by deciphering expression regulation of thousands of annotated genes in genomes
We test the performance of Apriori-Motif on some benchmark synthetic samples for PMP under the conditions which we are concerned with
A parent motif of length l is chosen by picking l bases from nucleotides A, C, G, T at random
Summary
In the post-genomic era, a major challenge is represented by deciphering expression regulation of thousands of annotated genes in genomes. A signal (often called motif) in DNA sequences is not exactly identical but presents mutations This signal is a short subsequence, typically about 10 bp (base pairs) long, in the midst of a great amount of statistical noise and is too complicated to be discriminated by computational methods. The kind of algorithms are based on PWM (Position-Specific Weighted Matrix, called profile) model, can find motifs of any specified length very efficiently. They are inevitably to slump into a local optimal.
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