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Full-Text Articles in Engineering
Non-Coding Rna Covariance Model Combination Using Mixed Primary-Secondary Structure Alignment, Jennifer Smith
Non-Coding Rna Covariance Model Combination Using Mixed Primary-Secondary Structure Alignment, Jennifer Smith
Jennifer A. Smith
Covariance models are very effective for finding new members of non-coding RNA sequence families in genomic data. However, the computation burden of applying CM-based search algorithms can be prohibitive. When annotating the genome of a newly sequenced organism it is usually desired to search the sequence data using a large number of ncRNA families. Computational burden can be reduced if the families are clustered into statistically similar models and a single cluster-average representative model produced. The database is then searched with the representative model for each cluster at a relatively low detection threshold. The output of this pre-filtered database is …
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 …