title: KABOOM! A new suffix array based algorithm for clustering expression data.
abstract: BACKGROUND
  Second-generation sequencing technology has reinvigorated research using expression data, and clustering such data remains a significant challenge, with much larger datasets and with different error profiles. Algorithms that rely on all-versus-all comparison of sequences are not practical for large datasets.
  
  
  RESULTS
  We introduce a new filter for string similarity which has the potential to eliminate the need for all-versus-all comparison in clustering of expression data and other similar tasks. Our filter is based on multiple long exact matches between the two strings, with the additional constraint that these matches must be sufficiently far apart. We give details of its efficient implementation using modified suffix arrays. We demonstrate its efficiency by presenting our new expression clustering tool, wcd-express, which uses this heuristic. We compare it to other current tools and show that it is very competitive both with respect to quality and run time.
  
  
  BACKGROUND
  Source code and binaries available under GPL at http://code.google.com/p/wcdest. Runs on Linux and MacOS X.
  
  
  BACKGROUND
  scott.hazelhurst@wits.ac.za; zsuzsa@cebitec.uni-bielefeld.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Wits Bioinformatics, School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za
authors: Scott
authors: S
authors: Hazelhurst
authors: Zsuzsanna
authors: Z
authors: Lipták
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000191:economics / Q000379:methods; D020869:Gene Expression Profiling; D006801:Humans; D012984:Software