Mixture models for analysis of melting temperature data
<p style="text-align:justify;"> <b>Background:</b> 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 gene...
| Principais autores: | , , , |
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| Formato: | Journal article |
| Idioma: | English |
| Publicado em: |
BioMed Central
2008
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| _version_ | 1826267825620123648 |
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| author | Nellåker, C Uhrzander, F Tyrcha, J Karlsson, H |
| author_facet | Nellåker, C Uhrzander, F Tyrcha, J Karlsson, H |
| author_sort | Nellåker, C |
| collection | OXFORD |
| description | <p style="text-align:justify;"> <b>Background:</b> 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.<br/><br/> <b>Results:</b> 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.<br/><br/> <b>Conclusion:</b> 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> |
| first_indexed | 2024-03-06T21:00:09Z |
| format | Journal article |
| id | oxford-uuid:3a9ceea8-bf8d-45af-a6d3-d0f9a1ca483d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:00:09Z |
| publishDate | 2008 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:3a9ceea8-bf8d-45af-a6d3-d0f9a1ca483d2022-03-26T14:02:37ZMixture models for analysis of melting temperature dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3a9ceea8-bf8d-45af-a6d3-d0f9a1ca483dEnglishSymplectic Elements at OxfordBioMed Central2008Nellåker, CUhrzander, FTyrcha, JKarlsson, H <p style="text-align:justify;"> <b>Background:</b> 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.<br/><br/> <b>Results:</b> 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.<br/><br/> <b>Conclusion:</b> 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> |
| spellingShingle | Nellåker, C Uhrzander, F Tyrcha, J Karlsson, H Mixture models for analysis of melting temperature data |
| title | 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_short | Mixture models for analysis of melting temperature data |
| title_sort | mixture models for analysis of melting temperature data |
| work_keys_str_mv | AT nellakerc mixturemodelsforanalysisofmeltingtemperaturedata AT uhrzanderf mixturemodelsforanalysisofmeltingtemperaturedata AT tyrchaj mixturemodelsforanalysisofmeltingtemperaturedata AT karlssonh mixturemodelsforanalysisofmeltingtemperaturedata |