Secondary structure prediction for aligned RNA sequences.

By Ivo L. Hofacker, M. Fekete, Peter F. Stadler. Journal of Molecular Biology, 2002

682 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ce6d075db385aa006555fbd16cc7878529ec2c1a

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Citations in the corpus

ViennaRNA Package 2.0 (2011, background): “More precisely, energy contributions can be added up in a columnwise manner to yield an effective energy model for the alignment as a whole [49].”
3D RNA and Functional Interactions from Evolutionary Couplings. (2015, positive): “Comparative sequence analyses of RNA continue to contribute to successful RNA sequence alignment (Nawrocki and Eddy, 2013) and secondary structure prediction methods (Hofacker et al., 2002; Nussinov and Jacobson, 1980; Rivas and Eddy, 1999; Zuker, 2003) (review Hofacker and Lorenz, 2014), but existi”
RNAstructure: software for RNA secondary structure prediction and analysis (2010, background): “Alternatively, using two or more homologous sequences to determine a conserved structure can result in significantly more accurate structure prediction [23-27].”
A comprehensive comparison of comparative RNA structure prediction approaches (2004, methodology): “For data-sets consisting of short ( 200 bases) and diverse sequences Dynalign might do well or one might choose to use a mixture of RNAalifold or Pfold to fold similar clades and RNAforester or MARNA to align folded clades.”
Rfam: updates to the RNA families database (2008, methodology): “The covariation metric is based upon that used by RNAalifold (9).”
A fast structural multiple alignment method for long RNA sequences (2008, background): “For consensus structure prediction, RNAalifold [4], Pfold [5], and McCaskill-MEA [6] are applicable only to sets of aligned RNA sequences.”
Pfold: RNA secondary structure prediction using stochastic context-free grammars (2003, neutral): “A number of methods have been developed that combine energy calculations with evolutionary information through a given alignment (24–27).”
Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction (2004, methodology): “Consequently, nearly all consensus RNA structure prediction methods that have been introduced optimize an ad hoc weighted combination of thermodynamic parameters and comparative sequence analysis terms, either for consensus structure prediction from predetermined multiple RNA sequence alignments [18”
Genomic Organization, Differential Expression, and Interaction of SQUAMOSA Promoter-Binding-Like Transcription Factors and microRNA156 in Rice1[W] (2006, methodology): “The second structures of OsmiR156 family calculated with the RNAalifold program (Hofacker et al., 2002) revealed no consensus second structure beyond the mature miR156 encoding region (Supplemental Fig.”
PicXAA-R: Efficient structural alignment of multiple RNA sequences using a greedy approach (2011, methodology): “The SCI score is defined as SCI = E E A where EA is the minimum free energy of the consensus MSA as computed by RNAalifod [46] and Ē is the average minimum free energy of all single sequences in the alignment as computed by RNAfold [47].”
Vienna RNA secondary structure server (2003, methodology): “Recently, several methods have addressed the problem of predicting a consensus structure for a group of related RNA sequences [2, 3, 8, 6, 7, 4].”
Systematic deletion of Salmonella small RNA genes identifies CyaR, a conserved CRP‐dependent riboregulator of OmpX synthesis (2008, methodology): “This structure is conserved among all inspected species as determined using the RNAalifold algorithm (Hofacker et al., 2002).”
CMfinder - a covariance model based RNA motif finding algorithm (2006, methodology): “We have used this approach to obtain highly accurate covariance models of known RNA motifs based on small numbers of related sequences, which identified homologs in deeply-diverged species.”