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10
A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences
, 2004
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A new distance for high level RNA secondary structure comparison
 IEEE Trans. on Computational Biology and Bioinformatics
, 2005
"... Abstract—We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us t ..."
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Cited by 19 (1 self)
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Abstract—We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore efficient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs. Index Terms—Tree comparison, edit operation, distance, RNA, secondary structure. æ 1
Consensus folding of unaligned RNA sequences revisited
 In RECOMB
, 2005
"... As one of the earliest problems in computational biology, RNA secondary structure prediction (sometimes referred to as “RNA folding”) problem has attracted attention again, thanks to the recent discoveries of many novel noncoding RNA molecules. The two common approaches to this problem are de novo ..."
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Cited by 13 (1 self)
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As one of the earliest problems in computational biology, RNA secondary structure prediction (sometimes referred to as “RNA folding”) problem has attracted attention again, thanks to the recent discoveries of many novel noncoding RNA molecules. The two common approaches to this problem are de novo prediction of RNA secondary structure based on energy minimization and the consensus folding approach (computing the common secondary structure for a set of unaligned RNA sequences). Consensus folding algorithms work well when the correct seed alignment is part of the input to the problem. However, seed alignment itself is a challenging problem for diverged RNA families. In this paper, we propose a novel framework to predict the common secondary structure for unaligned RNA sequences. By matching putative stacks in RNA sequences, we make use of both primary sequence information and thermodynamic stability for prediction at the same time. We show that our method can predict the correct common RNA secondary structures even when we are given only a limited number of unaligned RNA sequences, and it outperforms current algorithms in sensitivity and accuracy. Key words: RNA secondary structure prediction, RNA consensus folding, RNA stack configuration, dynamic programming. 1.
Novel tree edit operations for RNA secondary structure comparison
 in "Proceedings of the 4th Workshop on Algorithms in BioInformatics (WABI)", Lecture Notes in Computer Science
"... Abstract. We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two novel operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion and relabelling classically used in the literature. This allows u ..."
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Cited by 2 (0 self)
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Abstract. We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two novel operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion and relabelling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore efficient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs.
A Relational Extension to the Notion of Motifs: An application to the Common 3D Protein Substructures Searching Problem
, 2009
"... The geometric configurations of atoms in protein structures can be viewed as approximate relations among them. Then, finding similar common substructures within a set of protein structures belongs to a new class of problems that generalizes that of finding repeated motifs. The novelty lies in the ad ..."
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Cited by 1 (0 self)
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The geometric configurations of atoms in protein structures can be viewed as approximate relations among them. Then, finding similar common substructures within a set of protein structures belongs to a new class of problems that generalizes that of finding repeated motifs. The novelty lies in the addition of constraints on the motifs in terms of relations that must hold between pairs of positions of the motifs. We will hence denote them as relational motifs. For this class of problems we present an algorithm that is a suitable extension of the KMR (Karp et al., 1972) paradigm and, in particular, of the KMRC (Soldano et al., 1995) as it uses a degenerate alphabet. Our algorithm contains several improvements with respect to (Soldano et al., 1995) that become especially useful when—as it is required for relational motifs—the inference is made by partially overlapping shorter motifs, rather than concatenating them like in (Karp et al., 1972). The efficiency, correctness and completeness of the algorithm is ensured by several nontrivial properties that are proven in this paper. The algorithm has been applied in the important field of protein common 3D substructure searching. The methods implemented have been tested on several examples of protein families such as serine proteases, globins and cytochromes P450 additionally. The detected motifs have been compared to those found by multiple structural alignments methods. 1 1
Logical settings for concept learning from incomplete examples in First Order Logic.
"... Abstract. We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with data incompleteness. More precisely we are interested in extending the learning from interpretations setting int ..."
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Abstract. We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with data incompleteness. More precisely we are interested in extending the learning from interpretations setting introduced by L. De Raedt that extends to relational representations the classical propositional (or attributevalue) concept learning from examples framework. We are inspired here by ideas presented by H. Hirsh in a work extending the Version space inductive paradigm to incomplete data. H. Hirsh proposes to slightly modify the notion of solution when dealing with incomplete examples: a solution has to be a hypothesis compatible with all pieces of information concerning the examples. We identify two main classes of incompleteness. First, uncertainty deals with our state of knowledge concerning an example. Second, generalization (or abstraction) deals with what part of the description of the example is sufficient for the learning purpose. These two main sources of incompleteness can be mixed up when only part of the useful information is known. We discuss a general learning setting, referred to as &quot;learning from possibilities &quot; that formalizes these ideas, then we present a more specific learning setting, referred to as &quot;assumptionbased learning &quot; that cope with examples which uncertainty can be reduced when considering contextual information outside of the proper description of the examples. Assumptionbased learning is illustrated on a recent work concerning the prediction of a consensus secondary structure common to a set of RNA sequences. 1
A New Distance for High Level RNA
 IEEE/ACM Transactions on Computational Biology and Bioinformatics
, 2005
"... We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us to addre ..."
Abstract
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We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore efficient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs.
Inference of Approximated Motifs with Conserved Relations
, 2006
"... In this paper we define a new class of problems that generalizes that of finding repeated motifs. The novelty lies in the addition of constraints on the motifs in terms of relations that must hold between pairs of positions of the motifs. We will hence denote them as relational motifs. For this clas ..."
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In this paper we define a new class of problems that generalizes that of finding repeated motifs. The novelty lies in the addition of constraints on the motifs in terms of relations that must hold between pairs of positions of the motifs. We will hence denote them as relational motifs. For this class of problems we give an algorithm that is a suitable extension of the KMR [9] paradigm and, in particular, of the KMRC [15] as it uses a degenerate alphabet. The algorithm contains several improvements with respect to [15] that result especially useful when—as it is required for relational motifs—the inference is made by partially overlapping shorter motifs, rather than concatenating them like in [9]. The efficiency, correctness and completeness of the algorithm is assured by several nontrivial properties that we prove in this paper. Finally, we list some possible applications and we focus on one of them: the study of 3D structures of proteins. 1
and
"... Finding regularities in sequences is an important problem in various areas. Regularities are often words (in a strict or somewhat flexible meaning) "repeated " in the sequence, i.e. satisfying some constraints about their occurrence. In this paper we deal with relational values that express what rel ..."
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Finding regularities in sequences is an important problem in various areas. Regularities are often words (in a strict or somewhat flexible meaning) "repeated " in the sequence, i.e. satisfying some constraints about their occurrence. In this paper we deal with relational values that express what relates two positions in a word or a sequence. Then a strict relational word is defined as the set of relational values corresponding to all pairs of positions within a subsequence. A relational word is flexible if the constraint on a relational value is to belong to a set of relational values. We present here an algorithm, called KMRCRelat, which is derived from a previous algorithm for identification of repeated flexible words. KMRCRelat find either the klength or the longest repeated flexible relational words in a sequence or a set of sequences.
RNAMotifScan: automatic identification of
, 2010
"... RNA structural motifs using secondary structural alignment ..."