Results 1  10
of
176
Rfam: An RNA family database
 Nucleic Acids Res
, 2003
"... Rfam is a collection of multiple sequence alignments and covariance models representing noncoding RNA families. Rfam is available on the web in the UK at http://www.sanger.ac.uk/Software/Rfam/ and in the US at http://rfam.wustl.edu/. These websites allow the user to search a query sequence against ..."
Abstract

Cited by 169 (3 self)
 Add to MetaCart
Rfam is a collection of multiple sequence alignments and covariance models representing noncoding RNA families. Rfam is available on the web in the UK at http://www.sanger.ac.uk/Software/Rfam/ and in the US at http://rfam.wustl.edu/. These websites allow the user to search a query sequence against a library of covariance models, and view multiple sequence alignments and family annotation. The database can also be downloaded in flatfile form and searched locally using the INFERNAL package (http://infernal.wustl.edu/). The first release of Rfam (1.0) contains 25 families, which annotate over 50000 noncoding RNA genes in the taxonomic divisions of the EMBL nucleotide database.
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 ..."
Abstract

Cited by 138 (21 self)
 Add to MetaCart
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...
RNA secondary structure prediction using stochastic contextfree 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 ..."
Abstract

Cited by 123 (12 self)
 Add to MetaCart
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 contextfree grammars (SCFGs) to give a prior probability distribution of structures.
Stochastic ContextFree Grammars for tRNA Modeling
, 1994
"... Stochastic contextfree 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 ..."
Abstract

Cited by 123 (8 self)
 Add to MetaCart
Stochastic contextfree 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 similarlength 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 Tdomains in some mitochdondrial tRNAs that cannot be tted into the canonical secondary structure.
RSEARCH: Finding homologs of single structured RNA sequences
 BMC Bioinformatics
, 2003
"... Background: Many transacting noncoding RNA genes and cisacting RNA regulatory elements conserve secondary structure rather than primary sequence. Most homology search tools only look at the primary sequence level, however. ..."
Abstract

Cited by 121 (1 self)
 Add to MetaCart
Background: Many transacting noncoding RNA genes and cisacting RNA regulatory elements conserve secondary structure rather than primary sequence. Most homology search tools only look at the primary sequence level, however.
A MemoryEfficient 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. Re ..."
Abstract

Cited by 76 (6 self)
 Add to MetaCart
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:...
The language of RNA: A formal grammar that includes pseudoknots
 Bioinformatics
"... Motivation: In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking. Results: Here we show a one ..."
Abstract

Cited by 60 (1 self)
 Add to MetaCart
Motivation: In a previous paper, we presented a polynomial time dynamic programming algorithm for predicting optimal RNA secondary structure including pseudoknots. However a formal grammatical representation for RNA secondary structure with pseudoknots was still lacking. Results: Here we show a onetoone correspondence between that algorithm and a formal transformational grammar. This grammar class encompasses the contextfree grammars and goes beyond to generate pseudoknotted structures. The pseudoknot grammar avoids the use of general contextsensitive rules by introducing a small number of auxiliary symbols used to reorder the strings generated by an otherwise contextfree grammar. This formal representation of the residue correlations in RNA structure is important because it means we can build full probabilistic models of RNA secondary structure, including pseudoknots, and use them to optimally parse sequences in polynomial time. Contact: eddy@genetics.wustl.edu 1 ...
An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots
, 2004
"... Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On ..."
Abstract

Cited by 53 (2 self)
 Add to MetaCart
Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching, an algorithm for pseudoknot prediction with comparative analysis, suffers from lowprediction accuracy in many cases.