title: WORDUP: an efficient algorithm for discovering statistically significant patterns in DNA sequences.
abstract: We present here a fast and sensitive method designed to isolate short nucleotide sequences which have non-random statistical properties and may thus be biologically active. It is based on a first order Markov analysis and allows us to detect statistically significant sequence motifs from six to ten nucleotides long which are significantly shared (or avoided) in the sequences under investigation. This method has been tested on a set of 521 sequences extracted from the Eukaryotic Promoter Database (2). Our results demonstrate the accuracy and the efficiency of the method in that the sequence motifs which are known to act as eukaryotic promoters, such as the TATA-box and the CAAT-box, were clearly identified. In addition we have found other statistically significant motifs, the biological roles of which are yet to be clarified.
authors.affiliation: Dipartimento di Biochimica e Biologia Molecolare, Università di Bari, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Pesole
authors.forename: N
authors.initials: N
authors.lastname: Prunella
authors.forename: S
authors.initials: S
authors.lastname: Liuni
authors.forename: M
authors.initials: M
authors.lastname: Attimonelli
authors.forename: C
authors.initials: C
authors.lastname: Saccone
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D016208:Databases, Factual; D011401:Promoter Regions, Genetic; D012045:Regulatory Sequences, Nucleic Acid; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors