pubmed-article:19714212 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:19714212 | lifeskim:mentions | umls-concept:C0026339 | lld:lifeskim |
pubmed-article:19714212 | lifeskim:mentions | umls-concept:C0026336 | lld:lifeskim |
pubmed-article:19714212 | lifeskim:mentions | umls-concept:C0035668 | lld:lifeskim |
pubmed-article:19714212 | lifeskim:mentions | umls-concept:C0205174 | lld:lifeskim |
pubmed-article:19714212 | pubmed:issue | 8 | lld:pubmed |
pubmed-article:19714212 | pubmed:dateCreated | 2009-8-28 | lld:pubmed |
pubmed-article:19714212 | pubmed:abstractText | The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a "transducer composition" algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple sequences related by a phylogenetic tree. This algorithm draws on formal models of computational linguistics as well as the 1985 protosequence algorithm of David Sankoff. The output of the composition algorithm is a multiple-sequence stochastic context-free grammar. We describe dynamic programming algorithms, which are robust to null cycles and empty bifurcations, for parsing this grammar. Example applications include structural alignment of non-coding RNAs, propagation of structural information from an experimentally-characterized sequence to its homologs, and inference of the ancestral structure of a set of diverged RNAs. We implemented the above algorithms for a simple model of pairwise RNA structural evolution; in particular, the algorithms for maximum likelihood (ML) alignment of three known RNA structures and a known phylogeny and inference of the common ancestral structure. We compared this ML algorithm to a variety of related, but simpler, techniques, including ML alignment algorithms for simpler models that omitted various aspects of the full model and also a posterior-decoding alignment algorithm for one of the simpler models. In our tests, incorporation of basepair structure was the most important factor for accurate alignment inference; appropriate use of posterior-decoding was next; and fine details of the model were least important. Posterior-decoding heuristics can be substantially faster than exact phylogenetic inference, so this motivates the use of sum-over-pairs heuristics where possible (and approximate sum-over-pairs). For more exact probabilistic inference, we discuss the use of transducer composition for ML (or MCMC) inference on phylogenies, including possible ways to make the core operations tractable. | lld:pubmed |
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pubmed-article:19714212 | pubmed:language | eng | lld:pubmed |
pubmed-article:19714212 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:19714212 | pubmed:citationSubset | IM | lld:pubmed |
pubmed-article:19714212 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:19714212 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:19714212 | pubmed:month | Aug | lld:pubmed |
pubmed-article:19714212 | pubmed:issn | 1553-7358 | lld:pubmed |
pubmed-article:19714212 | pubmed:author | pubmed-author:HolleyH SHS | lld:pubmed |
pubmed-article:19714212 | pubmed:author | pubmed-author:BradleyRobert... | lld:pubmed |
pubmed-article:19714212 | pubmed:issnType | Electronic | lld:pubmed |
pubmed-article:19714212 | pubmed:volume | 5 | lld:pubmed |
pubmed-article:19714212 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:19714212 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:19714212 | pubmed:pagination | e1000483 | lld:pubmed |
pubmed-article:19714212 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
pubmed-article:19714212 | pubmed:meshHeading | pubmed-meshheading:19714212... | lld:pubmed |
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