The Experts below are selected from a list of 52620 Experts worldwide ranked by ideXlab platform
David H. Mathews - One of the best experts on this subject based on the ideXlab platform.
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improved rna secondary structure prediction by maximizing expected pair accuracy
RNA, 2009Co-Authors: Jason W Gloor, David H. MathewsAbstract:Free Energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical Free Energy Change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the Free Energy Change nearest-neighbor parameters is used to predict base-pair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum Free Energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring double-strandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with Free Energy minimization.
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prediction of rna secondary structure by Free Energy minimization
Current Opinion in Structural Biology, 2006Co-Authors: David H. Mathews, Douglas H TurnerAbstract:RNA secondary structure is often predicted from sequence by Free Energy minimization. Over the past two years, advances have been made in the estimation of folding Free Energy Change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.
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detection of non coding rnas on the basis of predicted secondary structure formation Free Energy Change
BMC Bioinformatics, 2006Co-Authors: Andrew V Uzilov, Joshua M Keegan, David H. MathewsAbstract:Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.
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Detection of non-coding RNAs on the basis of predicted secondary structure formation Free Energy Change
BMC bioinformatics, 2006Co-Authors: Andrew V Uzilov, Joshua M Keegan, David H. MathewsAbstract:Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding Free Energy Change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total Free Energy Change, which scores the structural alignment and the Free Energy Change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total Free Energy Change of an input sequence pair can either be compared with the total Free Energy Changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate tool than RNAz or QRNA in genomic screens, especially for low-identity regions. Dynalign provides a method for discovering ncRNAs in sequenced genomes that other methods may not identify. Significant improvements in Dynalign runtime have also been achieved.
Douglas H Turner - One of the best experts on this subject based on the ideXlab platform.
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prediction of rna secondary structure by Free Energy minimization
Current Opinion in Structural Biology, 2006Co-Authors: David H. Mathews, Douglas H TurnerAbstract:RNA secondary structure is often predicted from sequence by Free Energy minimization. Over the past two years, advances have been made in the estimation of folding Free Energy Change, the mapping of secondary structure and the implementation of computer programs for structure prediction. The trends in computer program development are: efficient use of experimental mapping of structures to constrain structure prediction; use of statistical mechanics to improve the fidelity of structure prediction; inclusion of pseudoknots in secondary structure prediction; and use of two or more homologous sequences to find a common structure.
Naoki Kamegashira - One of the best experts on this subject based on the ideXlab platform.
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studies on oxygen dissociation pressure of lnmno3 ln rare earth with the e m f technique
Journal of Alloys and Compounds, 1996Co-Authors: Taroh Atsumi, Tatsuo Ohgushi, Naoki KamegashiraAbstract:Abstract The oxygen dissociation pressure of LnMnO 3 (Ln La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Y or Sc) was measured at high temperature (1170–1400 K) by means of e.m.f. using stabilized ZrO 2 as a solid electrolyte. Assuming that the decomposition reaction proceeds in a stoichiometric form, a standard Gibbs Free Energy Change of decomposition was determined from the oxygen dissociation pressure. The variation of the Free Energy Change with Change in identity of Ln was investigated. It was found that the Energy Change depends strongly upon both the crystal structure and the ionic radius of Ln 3+ . The standard Gibbs Free Energy Change of formation for LnMnO 3 compounds was obtained from the standard Gibbs Free Energy Change for the decomposition reactions and those for the formation of the decomposition materials.
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studies on oxygen dissociation pressure of lnmno3 ln rare earth with the e m f technique
Journal of Alloys and Compounds, 1996Co-Authors: Taroh Atsumi, Tatsuo Ohgushi, Naoki KamegashiraAbstract:Abstract The oxygen dissociation pressure of LnMnO 3 (Ln La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Y or Sc) was measured at high temperature (1170–1400 K) by means of e.m.f. using stabilized ZrO 2 as a solid electrolyte. Assuming that the decomposition reaction proceeds in a stoichiometric form, a standard Gibbs Free Energy Change of decomposition was determined from the oxygen dissociation pressure. The variation of the Free Energy Change with Change in identity of Ln was investigated. It was found that the Energy Change depends strongly upon both the crystal structure and the ionic radius of Ln 3+ . The standard Gibbs Free Energy Change of formation for LnMnO 3 compounds was obtained from the standard Gibbs Free Energy Change for the decomposition reactions and those for the formation of the decomposition materials.
Tomas Osterman - One of the best experts on this subject based on the ideXlab platform.
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geminate charge recombination in polymer fullerene bulk heterojunction films and implications for solar cell function
Journal of the American Chemical Society, 2010Co-Authors: Tero Kesti, Manisankar Maiti, Fengling Zhang, Olle Inganas, Stefan Hellstrom, Mats Andersson, Frederic Oswald, Fernando Langa, Tomas Osterman, Torbjorn PascherAbstract:We have studied the influence of three different fullerene derivatives on the charge generation and recombination dynamics of polymer/fullerene bulk heterojunction (BHJ) solar cell blends. Charge generation in APFO3/[70]PCBM and APFO3/[60]PCBM is very similar and somewhat slower than charge generation in APFO3/[70]BTPF. This difference qualitatively matches the trend in Free Energy Change of electron transfer estimated from the LUMO energies of the polymer and fullerene derivatives. The first order (geminate) charge recombination rate is significantly different for the three fullerene derivatives studied and increases in the order APFO3/[70]PCBM < APFO3/[60]PCBM < APFO3/[70]BTPF. The variation in electron transfer rate cannot be explained from the LUMO energies of the fullerene derivatives and single-step electron transfer in the Marcus inverted region and simple considerations of expected trends for the reorganization Energy and Free Energy Change. Instead we suggest that geminate charge recombination oc...
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geminate charge recombination in polymer fullerene bulk heterojunction films and implications for solar cell function
Journal of the American Chemical Society, 2010Co-Authors: Suman Kalyan Pal, Tero Kesti, Manisankar Maiti, Fengling Zhang, Olle Inganas, Stefan Hellstrom, Mats Andersson, Frederic Oswald, Fernando Langa, Tomas OstermanAbstract:We have studied the influence of three different fullerene derivatives on the charge generation and recombination dynamics of polymer/fullerene bulk heterojunction (BHJ) solar cell blends. Charge generation in APFO3/[70]PCBM and APFO3/[60]PCBM is very similar and somewhat slower than charge generation in APFO3/[70]BTPF. This difference qualitatively matches the trend in Free Energy Change of electron transfer estimated from the LUMO energies of the polymer and fullerene derivatives. The first order (geminate) charge recombination rate is significantly different for the three fullerene derivatives studied and increases in the order APFO3/[70]PCBM < APFO3/[60]PCBM < APFO3/[70]BTPF. The variation in electron transfer rate cannot be explained from the LUMO energies of the fullerene derivatives and single-step electron transfer in the Marcus inverted region and simple considerations of expected trends for the reorganization Energy and Free Energy Change. Instead we suggest that geminate charge recombination occurs from a state where electrons and holes have separated to different distances in the various materials because of an initially high charge mobility, different for different materials. In a BHJ thin film this charge separation distance is not sufficient to overcome the electrostatic attraction between electrons and holes and geminate recombination occurs on the nanosecond to hundreds of nanoseconds time scale. In a BHJ solar cell, we suggest that the internal electric field in combination with polarization effects and the dynamic nature of polarons are key features to overcome electron-hole interactions to form Free extractable charges.
Andrew V Uzilov - One of the best experts on this subject based on the ideXlab platform.
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detection of non coding rnas on the basis of predicted secondary structure formation Free Energy Change
BMC Bioinformatics, 2006Co-Authors: Andrew V Uzilov, Joshua M Keegan, David H. MathewsAbstract:Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.
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Detection of non-coding RNAs on the basis of predicted secondary structure formation Free Energy Change
BMC bioinformatics, 2006Co-Authors: Andrew V Uzilov, Joshua M Keegan, David H. MathewsAbstract:Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding Free Energy Change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total Free Energy Change, which scores the structural alignment and the Free Energy Change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total Free Energy Change of an input sequence pair can either be compared with the total Free Energy Changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate tool than RNAz or QRNA in genomic screens, especially for low-identity regions. Dynalign provides a method for discovering ncRNAs in sequenced genomes that other methods may not identify. Significant improvements in Dynalign runtime have also been achieved.
