Mixture models for analysis of melting temperature data

Mixture models for analysis of melting temperature data

<p>Abstract</p> <p>Background</p> <p>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 hig...

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Main Authors: Tyrcha Joanna, Uhrzander Fredrik, Nellåker Christoffer, Karlsson Håkan
Format: Article
Language:English
Published: BMC 2008-09-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/9/370
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spelling doaj-96a1591030e94472abdae840c485ffd22020-11-24T21:36:20ZengBMCBMC Bioinformatics1471-21052008-09-019137010.1186/1471-2105-9-370Mixture models for analysis of melting temperature dataTyrcha JoannaUhrzander FredrikNellåker ChristofferKarlsson Håkan<p>Abstract</p> <p>Background</p> <p>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 (T<sub>m</sub>) data. However, there is currently no convention on how to statistically analyze such high-resolution T<sub>m </sub>data.</p> <p>Results</p> <p>Mixture model analysis was applied to T<sub>m </sub>data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in T<sub>m </sub>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.</p> <p>Conclusion</p> <p>Mixture model analysis of T<sub>m </sub>data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows T<sub>m </sub>data to be analyzed, classified, and compared in an unbiased manner.</p> http://www.biomedcentral.com/1471-2105/9/370
collection DOAJ
language English
format Article
sources DOAJ
author Tyrcha Joanna
Uhrzander Fredrik
Nellåker Christoffer
Karlsson Håkan
spellingShingle Tyrcha Joanna
Uhrzander Fredrik
Nellåker Christoffer
Karlsson Håkan
Mixture models for analysis of melting temperature data
BMC Bioinformatics
author_facet Tyrcha Joanna
Uhrzander Fredrik
Nellåker Christoffer
Karlsson Håkan
author_sort Tyrcha Joanna
title Mixture models for analysis of melting temperature data
title_short Mixture models for analysis of melting temperature data
title_full Mixture models for analysis of melting temperature data
title_fullStr Mixture models for analysis of melting temperature data
title_full_unstemmed Mixture models for analysis of melting temperature data
title_sort mixture models for analysis of melting temperature data
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2008-09-01
description <p>Abstract</p> <p>Background</p> <p>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 (T<sub>m</sub>) data. However, there is currently no convention on how to statistically analyze such high-resolution T<sub>m </sub>data.</p> <p>Results</p> <p>Mixture model analysis was applied to T<sub>m </sub>data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in T<sub>m </sub>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.</p> <p>Conclusion</p> <p>Mixture model analysis of T<sub>m </sub>data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows T<sub>m </sub>data to be analyzed, classified, and compared in an unbiased manner.</p>
url http://www.biomedcentral.com/1471-2105/9/370
work_keys_str_mv AT tyrchajoanna mixturemodelsforanalysisofmeltingtemperaturedata
AT uhrzanderfredrik mixturemodelsforanalysisofmeltingtemperaturedata
AT nellakerchristoffer mixturemodelsforanalysisofmeltingtemperaturedata
AT karlssonhakan mixturemodelsforanalysisofmeltingtemperaturedata
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