Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article,  present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field.  show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions.#R##N##R##N#Results: Our analysis of three publically available datasets shod a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families shod that the RNAG sampled structures re generally compact around their ensemble centroids, and at least 11 families had at least two ll-separated clusters of predicted structures. In general, the distance beten a reference structure and our predicted structure was large relative to the variation among structures within an ensemble.#R##N##R##N#Availability: The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbb.ccv.brown.edu/rnag.html#R##N##R##N#Contact: charles_lawrence@brown.edu#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Donglai Wei', 'Lauren Alpert', 'Charles E. Lawrence']
I am writing a research paper on a idea RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences
[{"from": "human", "value": "I am writing a research paper on a idea RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article,  present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field.  show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions.#R##N##R##N#Results: Our analysis of three publically available datasets shod a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families shod that the RNAG sampled structures re generally compact around their ensemble centroids, and at least 11 families had at least two ll-separated clusters of predicted structures. In general, the distance beten a reference structure and our predicted structure was large relative to the variation among structures within an ensemble.#R##N##R##N#Availability: The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbb.ccv.brown.edu/rnag.html#R##N##R##N#Contact: charles_lawrence@brown.edu#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]