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Keywords {🔍}

sequences, structure, base, rna, sequence, turbofold, pairing, probabilities, prediction, information, secondary, alignment, pubmed, article, google, scholar, structures, methods, extrinsic, datasets, sensitivity, probability, ppv, cas, number, figure, pairs, computation, accuracy, algorithm, predicted, multiple, nucleotides, input, computed, memory, coincidence, iterative, function, method, nucleotide, central, set, model, iteration, time, structural, pairwise, file, authors,

Topics {✒️}

open access article expanded nearest-neighbor model genome-scale structure-based clustering pairwise-sequence-alignment-based probabilities stochastic context-free grammars google scholar pseudo-free energy term existing multi-sequence methods full size image hidden markov model authors scientific editing '-probabilistic -consistency-transformation' option turbofold-mea runs slower iterative multi-sequence computations privacy choices/manage cookies article harmanci base pairing interactions modified free energy authors’ original file base pairing probability propagate pairing proclivities free energy minimization bmc bioinformatics 2001 bmc bioinformatics 2006 bmc bioinformatics 2007 bmc bioinformatics 2008 bmc bioinformatics 2010 bmc bioinformatics 12 high pairing probability pseudo free energy dynamic programming algorithm free energy modifications base pairing induced base pairing interaction modified partition function ribosomal rna precursor base pairing probabilities single partition function biomed central optimal abstract shape base pairing proclivity free energy modification dynamical programming matrix conserved base pairs base pairing information conserved secondary structure canonical base pairs noncoding rna genes base pairing contributes rna gene finding

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         headline:TurboFold: Iterative probabilistic estimation of secondary structures for multiple RNA sequences
         description:The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented. TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold. TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms. TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu .
         datePublished:2011-04-20T00:00:00Z
         dateModified:2011-04-20T00:00:00Z
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            Positive Predictive Value
            Extrinsic Information
            Turbo Decode
            Synechococcus PCC6717
            Base Pairing Probability
            Bioinformatics
            Microarrays
            Computational Biology/Bioinformatics
            Computer Appl. in Life Sciences
            Algorithms
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      headline:TurboFold: Iterative probabilistic estimation of secondary structures for multiple RNA sequences
      description:The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented. TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold. TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms. TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu .
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      dateModified:2011-04-20T00:00:00Z
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         Positive Predictive Value
         Extrinsic Information
         Turbo Decode
         Synechococcus PCC6717
         Base Pairing Probability
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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