title: Mixture models for analysis of melting temperature data.
abstract: BACKGROUND
  In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.
  
  
  RESULTS
  Mixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.
  
  
  CONCLUSIONS
  Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.
authors.affiliation: Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se
authors.forename: Christoffer
authors.initials: C
authors.lastname: Nellåker
authors.forename: Fredrik
authors.initials: F
authors.lastname: Uhrzander
authors.forename: Joanna
authors.initials: J
authors.lastname: Tyrcha
authors.forename: Håkan
authors.initials: H
authors.lastname: Karlsson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics*; D008956:Models, Chemical; D015233:Models, Statistical; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D044366:Transition Temperature
chemical_list: D004247:DNA
references.citation: Clin Chem. 2003 Jun;49(6 Pt 1):853-60
references.pmid: 12765979
references.citation: Microbiol Immunol. 2005;49(9):859-63
references.pmid: 16172541
references.citation: BMC Med Genet. 2005 Oct 04;6:36
references.pmid: 16202172
references.citation: Arch Virol. 2005 Dec;150(12):2429-38
references.pmid: 16052279
references.citation: Biopolymers. 1999 Sep;50(3):303-18
references.pmid: 10397791
references.citation: Clin Chem. 2007 Jan;53(1):98-103
references.pmid: 17110472
references.citation: Genome Res. 1999 Jan;9(1):72-8
references.pmid: 9927486
references.citation: Genes Brain Behav. 2008 Feb;7(1):103-12
references.pmid: 17559415
references.citation: Diagn Microbiol Infect Dis. 2007 Apr;57(4):455-8
references.pmid: 17156963
references.citation: Retrovirology. 2006 Jul 06;3:44
references.pmid: 16822326
references.citation: J Clin Microbiol. 2006 Feb;44(2):487-94
references.pmid: 16455903
references.citation: Nucleic Acids Res. 2008 Jun;36(10):3401-8
references.pmid: 18448472