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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2008-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/370 |
| _version_ | 1818145278980521984 |
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| author | Tyrcha Joanna Uhrzander Fredrik Nellåker Christoffer Karlsson Håkan |
| author_facet | Tyrcha Joanna Uhrzander Fredrik Nellåker Christoffer Karlsson Håkan |
| author_sort | Tyrcha Joanna |
| collection | DOAJ |
| 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> |
| first_indexed | 2024-12-11T12:00:56Z |
| format | Article |
| id | doaj.art-96a1591030e94472abdae840c485ffd2 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T12:00:56Z |
| publishDate | 2008-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-96a1591030e94472abdae840c485ffd22022-12-22T01:08:05ZengBMCBMC 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 |
| spellingShingle | Tyrcha Joanna Uhrzander Fredrik Nellåker Christoffer Karlsson Håkan Mixture models for analysis of melting temperature data BMC Bioinformatics |
| 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 |
| url | http://www.biomedcentral.com/1471-2105/9/370 |
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