Summary
This chapter compares the traditional dynamic programming RNA gene finding methodolgy with an alternative evolutionary computation approach. Both methods take a set of estimated covariance model parameters for a non-coding RNA family as given. The difference lies in how the score of a database position with respect to the covariance model is computed. Dynamic programming returns an exact score at the cost of very large computational resource usage. Presently, databases are prefiltered using non-structural algorithms such as BLAST in order to make dynamic programming search feasible. The evolutionary computing approach allows for faster approximate search, but uses the RNA secondary structure information in the covariance model from the start.
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Smith, S.F. (2008). Covariance-Model-Based RNA Gene Finding: Using Dynamic Programming versus Evolutionary Computing. In: Kelemen, A., Abraham, A., Chen, Y. (eds) Computational Intelligence in Bioinformatics. Studies in Computational Intelligence, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76803-6_7
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DOI: https://doi.org/10.1007/978-3-540-76803-6_7
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