title: Application of canonical correlation analysis for identifying viral integration preferences.
abstract: BACKGROUND
  Gene therapy aims at using viral vectors for attaching helpful genetic code to target genes. Therefore, it is of great importance to develop methods that can discover significant patterns around viral integration sites. Canonical correlation analysis is an unsupervised statistical tool that is used to describe the relations between two related views of the same semantic object, which fits well for identifying such salient patterns.
  
  
  RESULTS
  Proposed method is demonstrated on a sequence dataset obtained from a study on HIV-1 preferred integration regions. The subsequences on the left and right sides of the integration points are given to the method as the two views, and statistically significant relations are found between sequence-driven features derived from these two views, which suggest that the viral preference must be the factor responsible for this correlation. We found that there are significant correlations at x=5 indicating a palindromic behavior surrounding the viral integration site, which complies with the previously reported results.
  
  
  BACKGROUND
  Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.
authors.affiliation: Department of Computer Engineering, Istanbul University, Istanbul 34320, Turkey. egumus@istanbul.edu.tr
authors.forename: Ergun
authors.initials: E
authors.lastname: Gumus
authors.forename: Olcay
authors.initials: O
authors.lastname: Kursun
authors.forename: Ahmet
authors.initials: A
authors.lastname: Sertbas
authors.forename: Duran
authors.initials: D
authors.lastname: Ustek
mesh_terms: D015658:HIV Infections / Q000235:genetics* / Q000821:virology*; D015497:HIV-1 / Q000502:physiology*; D006801:Humans; D015999:Multivariate Analysis; D012984:Software; D016662:Virus Integration