Limits...
A statistical learning approach to the modeling of chromatographic retention of oligonucleotides incorporating sequence and secondary structure data.

Sturm M, Quinten S, Huber CG, Kohlbacher O - Nucleic Acids Res. (2007)

Bottom Line: We propose a new model for predicting the retention time of oligonucleotides.Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing.We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models.

View Article: PubMed Central - PubMed

Affiliation: Simulation of Biological Systems, Eberhard Karls University, Tübingen, Germany. sturm@informatik.uni-tuebingen.de

ABSTRACT
We propose a new model for predicting the retention time of oligonucleotides. The model is based on nu support vector regression using features derived from base sequence and predicted secondary structure of oligonucleotides. Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing. This makes the prediction of oligonucleotide retention time for arbitrary temperatures possible, provided that the target temperature lies within the temperature range of the training data. We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models.

Show MeSH

Related in: MedlinePlus

Predicted secondary structure of GTGCTCAGTGTAGCCCAGGATGGG at 40° C.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC1919494&req=5

Figure 3: Predicted secondary structure of GTGCTCAGTGTAGCCCAGGATGGG at 40° C.

Mentions: Figure 3 shows the predicted secondary structure of the oligonucleotide GTGCTCAGTGTAGCCCAGGATGGG at 40° C. Examples of features calculated from the predicted structure are listed in Table 2. For the simple structural components, only the measurement temperature is considered. For the multi-temperature structural components, the secondary structure and the resulting features are calculated for different temperatures.Figure 3.


A statistical learning approach to the modeling of chromatographic retention of oligonucleotides incorporating sequence and secondary structure data.

Sturm M, Quinten S, Huber CG, Kohlbacher O - Nucleic Acids Res. (2007)

Predicted secondary structure of GTGCTCAGTGTAGCCCAGGATGGG at 40° C.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1919494&req=5

Figure 3: Predicted secondary structure of GTGCTCAGTGTAGCCCAGGATGGG at 40° C.
Mentions: Figure 3 shows the predicted secondary structure of the oligonucleotide GTGCTCAGTGTAGCCCAGGATGGG at 40° C. Examples of features calculated from the predicted structure are listed in Table 2. For the simple structural components, only the measurement temperature is considered. For the multi-temperature structural components, the secondary structure and the resulting features are calculated for different temperatures.Figure 3.

Bottom Line: We propose a new model for predicting the retention time of oligonucleotides.Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing.We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models.

View Article: PubMed Central - PubMed

Affiliation: Simulation of Biological Systems, Eberhard Karls University, Tübingen, Germany. sturm@informatik.uni-tuebingen.de

ABSTRACT
We propose a new model for predicting the retention time of oligonucleotides. The model is based on nu support vector regression using features derived from base sequence and predicted secondary structure of oligonucleotides. Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing. This makes the prediction of oligonucleotide retention time for arbitrary temperatures possible, provided that the target temperature lies within the temperature range of the training data. We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models.

Show MeSH
Related in: MedlinePlus