Seeder

Seeder: discriminative seeding DNA motif discovery. Motivation: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches. Results: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements. Availability: The Seeder Perl distribution includes four modules. It is available for download on the Comprehensive Perl Archive Network (CPAN) at http://www.cpan.org.

References in zbMATH (referenced in 2 articles )

Showing results 1 to 2 of 2.
Sorted by year (citations)

  1. Luo, Jia-wei; Wang, Ting: Motif discovery using an immune genetic algorithm (2010)
  2. Fauteux, François; Blanchette, Mathieu; Stromvik, Martina V.: Seeder: Discriminative seeding DNA motif discovery. (2008) ioport