title: De novo identification of repeat families in large genomes.
abstract: BACKGROUND
  De novo repeat family identification is a challenging algorithmic problem of great practical importance. As the number of genome sequencing projects increases, there is a pressing need to identify the repeat families present in large, newly sequenced genomes. We develop a new method for de novo identification of repeat families via extension of consensus seeds; our method enables a rigorous definition of repeat boundaries, a key issue in repeat analysis.
  
  
  RESULTS
  Our RepeatScout algorithm is more sensitive and is orders of magnitude faster than RECON, the dominant tool for de novo repeat family identification in newly sequenced genomes. Using RepeatScout, we estimate that approximately 2% of the human genome and 4% of mouse and rat genomes consist of previously unannotated repetitive sequence.
  
  
  BACKGROUND
  Source code is available for download at http://www-cse.ucsd.edu/groups/bioinformatics/software.html
authors.affiliation: Department of Computer Science and Engineering, University of California San Diego La Jolla, CA 92093-0114, USA.
authors.forename: Alkes L
authors.initials: AL
authors.lastname: Price
authors.forename: Neil C
authors.initials: NC
authors.lastname: Jones
authors.forename: Pavel A
authors.initials: PA
authors.lastname: Pevzner
mesh_terms: D000465:Algorithms; D000818:Animals; D002107:Caenorhabditis / Q000235:genetics*; D017173:Caenorhabditis elegans; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D020407:Internet; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D051381:Rats; D012091:Repetitive Sequences, Nucleic Acid; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA