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Using sequence logos and information analysis of Lrp DNA binding sites to investigate discrepancies between natural selection and SELEX.

ABSTRACT: In vitro experiments that characterize DNA-protein interactions by artificial selection, such as SELEX,are often performed with the assumption that the experimental conditions are equivalent to natural ones. To test whether SELEX gives natural results, we compared sequence logos composed from naturally occurring leucine-responsive regulatory protein (Lrp) binding sites with those composed from SELEX-generated binding sites. The sequence logos were significantly different, indicating that the binding conditions are disparate. A likely explanation is that the SELEX experiment selected for a dimeric or trimeric Lrp complex bound to DNA. In contrast, natural sites appear to be bound by a monomer. This discrepancy suggests that in vitro selections do not necessarily give binding site sets comparable with the natural binding sites.

SUBMITTER: Shultzaberger RK

PROVIDER: S-EPMC148261 | biostudies-other | 1999 Feb

REPOSITORIES: biostudies-other

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Using sequence logos and information analysis of Lrp DNA binding sites to investigate discrepancies between natural selection and SELEX.

Shultzaberger R K RK Schneider T D TD

Nucleic acids research 19990201 3

In vitro experiments that characterize DNA-protein interactions by artificial selection, such as SELEX,are often performed with the assumption that the experimental conditions are equivalent to natural ones. To test whether SELEX gives natural results, we compared sequence logos composed from naturally occurring leucine-responsive regulatory protein (Lrp) binding sites with those composed from SELEX-generated binding sites. The sequence logos were significantly different, indicating that the bin ...[more]

PMID: 9889287

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Project description:BackgroundPseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement.ResultsIn this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI , and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations.ConclusionIn this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.

Dataset Information

Using sequence logos and information analysis of Lrp DNA binding sites to investigate discrepancies between natural selection and SELEX.

Publications

Using sequence logos and information analysis of Lrp DNA binding sites to investigate discrepancies between natural selection and SELEX.

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