title: Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets.
abstract: BACKGROUND
  Modern pyrosequencing techniques make it possible to study complex bacterial populations, such as 16S rRNA, directly from environmental or clinical samples without the need for laboratory purification. Alignment of sequences across the resultant large data sets (100,000+ sequences) is of particular interest for the purpose of identifying potential gene clusters and families, but such analysis represents a daunting computational task. The aim of this work is the development of an efficient pipeline for the clustering of large sequence read sets.
  
  
  METHODS
  Pairwise alignment techniques are used here to calculate genetic distances between sequence pairs. These methods are pleasingly parallel and have been shown to more accurately reflect accurate genetic distances in highly variable regions of rRNA genes than do traditional multiple sequence alignment (MSA) approaches. By utilizing Needleman-Wunsch (NW) pairwise alignment in conjunction with novel implementations of interpolative multidimensional scaling (MDS), we have developed an effective method for visualizing massive biosequence data sets and quickly identifying potential gene clusters.
  
  
  RESULTS
  This study demonstrates the use of interpolative MDS to obtain clustering results that are qualitatively similar to those obtained through full MDS, but with substantial cost savings. In particular, the wall clock time required to cluster a set of 100,000 sequences has been reduced from seven hours to less than one hour through the use of interpolative MDS.
  
  
  CONCLUSIONS
  Although work remains to be done in selecting the optimal training set size for interpolative MDS, substantial computational cost savings will allow us to cluster much larger sequence sets in the future.
authors.affiliation: Pervasive Technology Institute, Indiana University, Bloomington, IN 47408, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Hughes
authors.forename: Yang
authors.initials: Y
authors.lastname: Ruan
authors.forename: Saliya
authors.initials: S
authors.lastname: Ekanayake
authors.forename: Seung-Hee
authors.initials: SH
authors.lastname: Bae
authors.forename: Qunfeng
authors.initials: Q
authors.lastname: Dong
authors.forename: Mina
authors.initials: M
authors.lastname: Rho
authors.forename: Judy
authors.initials: J
authors.lastname: Qiu
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Fox
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D056186:Metagenomics / Q000379:methods*; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D012336:RNA, Ribosomal, 16S