Results 1 - 10
of
367
Rfam: an RNA family database
- Nucl. Acids Res
, 2003
"... Rfam is a collection of multiple sequence alignments and covariance models representing non-coding RNA families. Rfam is available on the web in the UK at ..."
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Cited by 289 (7 self)
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Rfam is a collection of multiple sequence alignments and covariance models representing non-coding RNA families. Rfam is available on the web in the UK at
RNA secondary structure prediction using stochastic context-free grammars and evolutionary history
, 1999
"... Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary ..."
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Cited by 180 (16 self)
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Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures.
Approaches to the Automatic Discovery of Patterns in Biosequences
, 1995
"... This paper is a survey of approaches and algorithms used for the automatic discovery of patterns in biosequences. Patterns with the expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering those patterns which a ..."
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Cited by 174 (21 self)
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This paper is a survey of approaches and algorithms used for the automatic discovery of patterns in biosequences. Patterns with the expressive power in the class of regular languages are considered, and a classification of pattern languages in this class is developed, covering those patterns which are the most frequently used in molecular bioinformatics. A formulation is given of the problem of the automatic discovery of such patterns from a set of sequences, and an analysis presented of the ways in which an assessment can be made of the significance and usefulness of the discovered patterns. It is shown that this problem is related to problems studied in the field of machine learning. The largest part of this paper comprises a review of a number of existing methods developed to solve this problem and how these relate to each other, focusing on the algorithms underlying the approaches. A comparison is given of the algorithms, and examples are given of patterns that have been discovered...
RSEARCH: Finding homologs of single structured RNA sequences
- BMC Bioinformatics
, 2003
"... Background: Many trans-acting noncoding RNA genes and cis-acting RNA regulatory elements conserve secondary structure rather than primary sequence. Most homology search tools only look at the primary sequence level, however. ..."
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Cited by 170 (3 self)
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Background: Many trans-acting noncoding RNA genes and cis-acting RNA regulatory elements conserve secondary structure rather than primary sequence. Most homology search tools only look at the primary sequence level, however.
The tRNAscanSE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs
- Nucleic Acids Res
, 2005
"... of tRNAs and snoRNAs ..."
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Stochastic Context-Free Grammars for tRNA Modeling
, 1994
"... Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences ' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results ..."
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Cited by 156 (9 self)
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Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences ' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mitochondrial and cytoplasmic), the model can discern general tRNA from similar-length RNA sequences of other kinds, can nd secondary structure of new tRNA sequences, and can produce multiple alignments of large sets of tRNA sequences. Our results suggest potential improvements in the alignments of the D- and T-domains in some mitochdondrial tRNAs that cannot be tted into the canonical secondary structure.
ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences
- Nucleic Acids Research
, 2004
"... A computer program, ARAGORN, identi®es tRNA and tmRNA genes. The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base-paired cloverleaf. tmRNA genes are identi®ed using a modi®ed version of the ..."
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Cited by 137 (0 self)
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A computer program, ARAGORN, identi®es tRNA and tmRNA genes. The program employs heuristic algorithms to predict tRNA secondary structure, based on homology with recognized tRNA consensus sequences and ability to form a base-paired cloverleaf. tmRNA genes are identi®ed using a modi®ed version of the BRUCE program. ARAGORN achieves a detection sensitivity of 99 % from a set of 1290 eubacterial, eukaryotic and archaeal tRNA genes and detects all complete tmRNA sequences in the tmRNA database, improving on the performance of the BRUCE program. Recently discovered tmRNA genes in the chloroplasts of two species from the `green ' algae lineage are detected. The output of the program reports the proposed tRNA secondary structure and, for tmRNA genes, the secondary structure of the tRNA domain, the tmRNA gene sequence, the tag peptide and a list of organisms with matching tmRNA peptide tags.
GtRNAdb: a database of transfer RNA genes detected in genomic sequence
- Nucleic Acids Res
, 2009
"... genomic sequence ..."
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A Memory-Efficient Dynamic Programming Algorithm for Optimal Alignment of a Sequence to an RNA Secondary Structure
, 2002
"... Background: Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N³) in memory. This is only practical for small RNAs ..."
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Cited by 104 (11 self)
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Background: Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N³) in memory. This is only practical for small RNAs. Results:...
