WORDUP: an efficient algorithm for discovering statistically significant patterns in DNA sequences | Zendy

Graziano Pesole | Zendy; Nicola Prunella | Zendy; Sabino Liuni | Zendy; Marcella Attimonelli | Zendy; Cecilia Saccone | Zendy

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WORDUP: an efficient algorithm for discovering statistically significant patterns in DNA sequences

Author(s) -

Graziano Pesole,

Nicola Prunella,

Sabino Liuni,

Marcella Attimonelli,

Cecilia Saccone

Publication year - 1992

Publication title -

nucleic acids research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.008

H-Index - 537

eISSN - 1362-4954

pISSN - 0305-1048

DOI - 10.1093/nar/20.11.2871

Subject(s) - biology , tata box , genetics , sequence (biology) , computational biology , set (abstract data type) , sequence motif , dna , nucleic acid sequence , dna sequencing , consensus sequence , promoter , sequence analysis , caat box , base sequence , gene , computer science , gene expression , programming language

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.

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