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RNA modeling using Gibbs sampling and stochastic context free grammars

L Grate, M Herbster, R Hughey, D Haussler, I S Mian, H Noller
1994 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
A new method of discovering the common secondary structure of a family of homologous RNA sequences using Gibbs sampling and stochastic context-free grammars is proposed.  ...  After the Gibbs sampling has produced a crude statistical model for the family, this model is translated into a stochastic context-free grammar, which is then refined by an Expectation Maximization (EM  ...  Acknowledgements The authors thank Kevin Karplus, (',hip Lawrence, Gary Stormo, and Michael Waterman for helpful discussion of these ideas, and Michael Brown, Deirdre Des Jardins, Kimmen SjSlander, and  ... 
pmid:7584383 fatcat:u4tlmcrgyffvdetmox2c5wiaby

Predicting RNA secondary structures with pseudoknots by MCMC sampling

Dirk Metzler, Markus E. Nebel
2007 Journal of Mathematical Biology  
The most probable secondary structure of an RNA molecule, given the nucleotide sequence, can be computed efficiently if a stochastic context-free grammar (SCFG) is used as the prior distribution of the  ...  Allowing all possible configurations of pseudoknots is not compatible with context-free grammar models and makes the search for an optimal secondary structure NP-complete.  ...  Acknowledgments We would like to thank two anonymous referees for very helpful suggestions and Martina Fröhlich for preprocessing the tmRNA data.  ... 
doi:10.1007/s00285-007-0106-6 pmid:17589847 fatcat:oi3a2zsdwbgn5iy4opfditnsue

Analysis of the Free Energy in a Stochastic RNA Secondary Structure Model

Markus E. Nebel, Anika Scheid
2011 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
In fact, the stochastic model for RNA secondary structures presented in this work has for example been used as the basis of a new algorithm for the (non-uniform) generation of random RNA secondary structures  ...  In this paper, building on such a stochastic model, we will analyze the expected minimum free energy of an RNA molecule according to Turner's energy rules.  ...  Acknowledgements The authors wish to thank two anonymous reviewers for their careful and helpful remarks and suggestions made for a previous version of this article.  ... 
doi:10.1109/tcbb.2010.126 pmid:21116040 fatcat:kpr2q276cre77fhma3266zsddy

Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction

Robin D Dowell, Sean R Eddy
2004 BMC Bioinformatics  
RNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs).  ...  Such methods can readily combine different sources of information that can be expressed probabilistically, such as an evolutionary model of comparative RNA sequence analysis and a biophysical model of  ...  Some of this work was conceived at an ESF and NIH funded workshop on computational RNA biology in Benasque, Spain, in summer 2003.  ... 
doi:10.1186/1471-2105-5-71 pmid:15180907 pmcid:PMC442121 fatcat:7o454hhllrbntetzjh7krhzvrq

Learning the Language of Biological Sequences [chapter]

François Coste
2016 Topics in Grammatical Inference  
While some first successes have already been recorded, such as the inference of profile hidden Markov models or stochastic context-free grammars which are now part of the classical bioinformatics toolbox  ...  , it is still a source of open and nice inspirational problems for grammatical inference, enabling us to confront our ideas to real fundamental applications.  ...  To obtain the context-free counterpart of pHMM, named profile stochastic context-free grammars (pSCFG) [81] or covariance models (CM) [82] , each matching rule S i is completed with position-based deletion  ... 
doi:10.1007/978-3-662-48395-4_8 fatcat:eojckskf65f5phcbmjsfbq7era

HIDDEN MARKOV MODELS, GRAMMARS, AND BIOLOGY: A TUTORIAL

SHIBAJI MUKHERJEE, SUSHMITA MITRA
2005 Journal of Bioinformatics and Computational Biology  
This article surveys methods using Hidden Markov Model and functional grammars for this purpose.  ...  Biological sequences and structures have been modelled using various machine learning techniques and abstract mathematical concepts.  ...  Section 7 attempts to give a concise mathematical introduction to grammars, and Stochastic Context Free Grammar SCFG in particular.  ... 
doi:10.1142/s0219720005001077 pmid:15852517 fatcat:elwanfaze5gejgvylj3p32zmmm

RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences

Donglai Wei, Lauren V. Alpert, Charles E. Lawrence
2011 Computer applications in the biosciences : CABIOS  
It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time.  ...  We 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.  ...  ACKNOWLEDGEMENTS We thank Sean Eddy, Dave Mathews , and the referees for their many helpful suggestions.  ... 
doi:10.1093/bioinformatics/btr421 pmid:21788211 pmcid:PMC3167047 fatcat:7impruqsmjfw5e7m7vnsjv6eq4

RNA secondary structure prediction using stochastic context-free grammars and evolutionary history

B. Knudsen, J. Hein
1999 Bioinformatics  
The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures.  ...  The model consists of two distinct parts: the SCFG and the evolutionary model.  ...  Acknowledgements We would like to thank Jan Gorodkin, Carsten Wiuf and the anonymous reviewers for critically reviewing the manuscript and suggesting improvements.  ... 
doi:10.1093/bioinformatics/15.6.446 pmid:10383470 fatcat:hqrvd2qhhje3bobde322qrbp5y

Computational Analysis of RNAs

S.R. EDDY
2006 Cold Spring Harbor Symposia on Quantitative Biology  
ACKNOWLEDGMENTS I am grateful to Elena Rivas, Ariane Machado-Lima, and Eric Nawrocki for comments on the manuscript.  ...  I thank the National Institutes of Health National Human Genome Research Institute, the Howard Hughes Medical Institute, and Alvin Goldfarb for their financial support of my group.  ...  The heart of our work is a general class of statistical models called "stochastic context-free grammars" (SCFGs), which we use to create computational methods that treat RNA as both primary sequence and  ... 
doi:10.1101/sqb.2006.71.003 pmid:17381287 fatcat:vnjeokzptvdjfc7tgnj65hwgh4

Tools for simulating evolution of aligned genomic regions with integrated parameter estimation

Avinash Varadarajan, Robert K Bradley, Ian H Holmes
2008 Genome Biology  
Controlled simulations of genome evolution are useful for benchmarking tools. However, many simulators lack extensibility and cannot measure parameters directly from data.  ...  Each offers algorithms for parameter measurement and reconstruction of ancestral sequence. All three tools out-perform the leading neutral DNA simulator (DAWG) in benchmarks.  ...  Materials and methods Sampling from phylo-grammars A phylo-grammar is a stochastic context-free grammar whose every state generates alignment columns (or groups of alignment columns).  ... 
doi:10.1186/gb-2008-9-10-r147 pmid:18840304 pmcid:PMC2760874 fatcat:igmaipmpyvdbnb2u2h5xawh75q

Automatic RNA secondary structure determination with stochastic context-free grammars

L Grate
1995 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
Dynamic programming is used to recover the optimal tree made up of the best potential base pairs and to create a stochastic context-free grammar.  ...  We have developed a method for predicting the common secondary structure of large RNA multiple alignments using only the information in the alignment.  ...  Acknowledgments The author thanks Michael Brown, Saira Mian, Bryn Weiser, Jessie Reklaw and the entire UCSC Computational Biology group and our collaborators in the Biology department.  ... 
pmid:7584430 fatcat:odr27zv2nzhfrk5467ccqfafoy

Markov Chain-Based Sampling for Exploring RNA Secondary Structure under the Nearest Neighbor Thermodynamic Model and Extended Applications

Anna Kirkpatrick, Kalen Patton, Prasad Tetali, Cassie Mitchell
2020 Mathematical and Computational Applications  
Plane trees are studied as a model for RNA secondary structure, with energy assigned to each tree based on the NNTM, and a corresponding Gibbs distribution is defined on the trees.  ...  The resulting algorithm can be used as a tool for exploring the branching structure of RNA, especially for long sequences, and to examine branching structure dependence on energy model parameters.  ...  Is there a stochastic context-free grammar which generates secondary structures (in our simplified model or using the full NNTM) according to a Gibbs distribution with NNTM energy?  ... 
doi:10.3390/mca25040067 pmid:35924027 pmcid:PMC9344895 fatcat:irxod2c4svg6nlnyrgsqcreqli

Bayesian sampling of evolutionarily conserved RNA secondary structures with pseudoknots

Gero Doose, Dirk Metzler
2012 Computer applications in the biosciences : CABIOS  
We investigate the benefit of using evolutionary history and demonstrate the competitiveness of our method compared with similar methods based on RNase P RNA sequences and simulated data.  ...  Results: In this article we present a method that takes advantage of the evolutionary history of a group of aligned RNA sequences for sampling consensus secondary structures, including pseudoknots, according  ...  This project was partly funded by means of the European Social Fund and the Free State of Saxony. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/bts369 pmid:22796961 fatcat:agh32mngrjcylpi7ths7byvl3y

Computational methods in noncoding RNA research

Ariane Machado-Lima, Hernando A. del Portillo, Alan Mitchell Durham
2007 Journal of Mathematical Biology  
This article reviews the main approaches used to identify ncRNAs and predict secondary structure.  ...  Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes.  ...  AML and AMD elaborated the manuscript. HAP was responsible for biological input and for help in the revision process.  ... 
doi:10.1007/s00285-007-0122-6 pmid:17786447 fatcat:pfqoa72w5jgazjhfav73drw5te

Evolutionary Triplet Models of Structured RNA

Robert K. Bradley, Ian Holmes, Gary D. Stormo
2009 PLoS Computational Biology  
The output of the composition algorithm is a multiple-sequence stochastic context-free grammar.  ...  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  ...  We are grateful to Ben Redelings and one anonymous referee for illuminating criticism. Author Contributions Conceived and designed the experiments: RKB IH. Performed the experiments: RKB IH.  ... 
doi:10.1371/journal.pcbi.1000483 pmid:19714212 pmcid:PMC2725318 fatcat:s4vau6jkhrgyfdjlrgda7u6phm
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