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Integrating Thermodynamic And Observed-Frequency Data For Non-Coding Rna Gene Search, Jennifer Smith, Kay Wiese
Integrating Thermodynamic And Observed-Frequency Data For Non-Coding Rna Gene Search, Jennifer Smith, Kay Wiese
Jennifer A. Smith
Among the most powerful and commonly used methods for finding new members of non-coding RNA gene families in genomic data are covariance models. The parameters of these models are estimated from the observed position-specific frequencies of insertions, deletions, and mutations in a multiple alignment of known non-coding RNA family members. Since the vast majority of positions in the multiple alignment have no observed changes, yet there is no reason to rule them out, some form of prior is applied to the estimate. Currently, observed-frequency priors are generated from non-family members based on model node type and child node type allowing …
Efficient Non-Coding Rna Gene Searches Through Classical And Evolutionary Methods, Jennifer Smith
Efficient Non-Coding Rna Gene Searches Through Classical And Evolutionary Methods, Jennifer Smith
Jennifer A. Smith
Successful non-coding RNA gene searching requires examination of long-range intramolecular base pairing possibilities. This results in search algorithms with extremely long run times such that large-scale use of the algorithms often becomes computationally infeasible. Methods for the efficient search of the solution space are examined. A review of the standard dynamic-programming covariance model search algorithm is given. An analysis of the statistically probable regions of the search space is undertaken and a method of limiting the traditional dynamic-programming algorithm to this region is shown. An alternative search method using a Genetic Algorithm (GA) which favours the probable region of the …
Rna Search With Decision Trees And Partial Covariance Models, Jennifer A. Smith
Rna Search With Decision Trees And Partial Covariance Models, Jennifer A. Smith
Jennifer A. Smith
The use of partial covariance models to search for RNA family members in genomic sequence databases is explored. The partial models are formed from contiguous subranges of the overall RNA family multiple alignment columns. A binary decision-tree framework is presented for choosing the order to apply the partial models and the score thresholds on which to make the decisions. The decision trees are chosen to minimize computation time subject to the constraint that all of the training sequences are passed to the full covariance model for final evaluation. Computational intelligence methods are suggested to select the decision tree since the …
Improved Covariance Model Parameter Estimation Using Rna Thermodynamic Properties, Jennifer A. Smith, Kay C. Wiese
Improved Covariance Model Parameter Estimation Using Rna Thermodynamic Properties, Jennifer A. Smith, Kay C. Wiese
Jennifer A. Smith
Covariance models are a powerful description of non-coding RNA (ncRNA) families that can be used to search nucleotide databases for new members of these ncRNA families. Currently, estimation of the parameters of a covariance model (state transition and emission scores) is based only on the observed frequencies of mutations, insertions, and deletions in known ncRNA sequences. For families with very few known members, this can result in rather uninformative models where the consensus sequence has a good score and most deviations from consensus have a fairly uniform poor score. It is proposed here to combine the traditional observed-frequency information with …
Efficient Non-Coding Rna Gene Searches Through Classical And Evolutionary Methods, Jennifer Smith
Efficient Non-Coding Rna Gene Searches Through Classical And Evolutionary Methods, Jennifer Smith
Jennifer A. Smith
Successful non-coding RNA gene searching requires examination of long-range intramolecular base pairing possibilities. This results in search algorithms with extremely long run times such that large-scale use of the algorithms often becomes computationally infeasible. Methods for the efficient search of the solution space are examined. A review of the standard dynamic-programming covariance model search algorithm is given. An analysis of the statistically probable regions of the search space is undertaken and a method of limiting the traditional dynamic-programming algorithm to this region is shown. An alternative search method using a Genetic Algorithm (GA) which favours the probable region of the …