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63
PX: psRNATarget; a plant small RNA target analysis server
- Nucleic Acids Res
"... analysis server ..."
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PremRNA secondary structures influence exon recognition
- PLoS Genet
, 2007
"... The secondary structure of a pre-mRNA influences a number of processing steps including alternative splicing. Since most splicing regulatory proteins bind to single-stranded RNA, the sequestration of RNA into double strands could prevent their binding. Here, we analyzed the secondary structure conte ..."
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Cited by 40 (2 self)
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The secondary structure of a pre-mRNA influences a number of processing steps including alternative splicing. Since most splicing regulatory proteins bind to single-stranded RNA, the sequestration of RNA into double strands could prevent their binding. Here, we analyzed the secondary structure context of experimentally determined splicing enhancer and silencer motifs in their natural pre-mRNA context. We found that these splicing motifs are significantly more single-stranded than controls. These findings were validated by transfection experiments, where the effect of enhancer or silencer motifs on exon skipping was much more pronounced in single-stranded conformation. We also found that the structural context of predicted splicing motifs is under selection, suggesting a general importance of secondary structures on splicing and adding another level of evolutionary constraints on pre-mRNAs. Our results explain the action of mutations that affect splicing and indicate that the structural context of splicing motifs is part of the mRNA splicing code.
Principles and Limitations of Computational MicroRNA Gene and Target Finding
, 2007
"... In 2001 there were four PubMed entries matching the word ‘‘microRNA’’ (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of ..."
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Cited by 29 (3 self)
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In 2001 there were four PubMed entries matching the word ‘‘microRNA’’ (miRNA). Interestingly, this number has now far exceeded 1300 and is still rapidly increasing. This more than anything demonstrates the extreme attention this field has had within a short period of time. With the large amounts of sequence data being generated, the need for analysis by computational approaches is obvious. Here, we review the general principles used in computational gene and target finding, and discuss the strengths and weaknesses of the methods. Several methods rely on detection of evolutionary conserved candidates, but recent methods have challenged this paradigm by simultaneously searching for the gene and the corresponding target(s). Whereas the early methods made predictions based on sets of hand-derived rules from precursor-miRNA structure or observed target–miRNA interactions, recent methods apply machine learning techniques. Even though these methods are already powerful, the amount of data they rely on is still limited. Since it is evident that data are continuously being generated, it must be anticipated that these methods will further improve their performance.
A partition function algorithm for interacting nucleic acid strands
- BIOINFORMATICS (SPECIAL ISMB/ECCB 2009 ISSUE
, 2009
"... Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact. Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and ..."
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Cited by 22 (7 self)
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Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact. Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict basepairing probability of any two bases in interacting nucleic acids, it
Variations on RNA Folding and Alignment Lessons from Benasque
, 2006
"... Abstract Dynamic Programming Algorithms solve many standard problems of RNA bioinformatics in polynomial time. In this contribution we discuss a series of variations on these standard methods that implement refined biophysical models, such as a restriction of RNA folding to canonical structures, and ..."
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Cited by 22 (10 self)
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Abstract Dynamic Programming Algorithms solve many standard problems of RNA bioinformatics in polynomial time. In this contribution we discuss a series of variations on these standard methods that implement refined biophysical models, such as a restriction of RNA folding to canonical structures, and an extension of structural alignments to an explicit scoring of stacking propensities. Furthermore, we demonstrate that a local structural alignment can be employed for ncRNA gene finding. In this context we discuss scanning variants for folding and alignment algorithms.
Partition function and base pairing probabilities for RNA-RNA interaction prediction
- PROCEEDINGS OF THE DIVERSITY IN DOCUMENT RETRIEVAL 2001 WORKSHOP
, 2011
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More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature
, 2006
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Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res 39
, 2011
"... Considering accessibility of the 30UTR is believed to increase the precision of microRNA target predic-tions. We show that, contrary to common belief, ranking by the hybridization energy or by the sum of the opening and hybridization energies, used in currently available algorithms, is not an effici ..."
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Cited by 16 (1 self)
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Considering accessibility of the 30UTR is believed to increase the precision of microRNA target predic-tions. We show that, contrary to common belief, ranking by the hybridization energy or by the sum of the opening and hybridization energies, used in currently available algorithms, is not an efficient way to rank predictions. Instead, we describe an algo-rithm which also considers only the accessible binding sites but which ranks predictions according to over-representation. When compared with experimentally validated and refuted targets in the fruit fly and human, our algorithm shows a remark-able improvement in precision while significantly reducing the computational cost in comparison with other free energy based methods. In the human genome, our algorithm has at least twice higher precision than other methods with their default parameters. In the fruit fly, we find five times more validated targets among the top 500 pre-dictions than other methods with their default par-ameters. Furthermore, using a common statistical framework we demonstrate explicitly the advan-tages of using the canonical ensemble instead of using the minimum free energy structure alone. We also find that ‘naı̈ve ’ global folding sometimes out-performs the local folding approach.
Time and space efficient RNA-RNA interaction prediction via sparse folding
"... In the past few years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low. Thus, computational target prediction methods are on high demand. Whereas all previous approaches for predicting a general joint structure have a c ..."
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Cited by 11 (5 self)
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In the past few years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low. Thus, computational target prediction methods are on high demand. Whereas all previous approaches for predicting a general joint structure have a complexity of O(n 6) running time and O(n 4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems. In this paper we show how to reduce both the time and space complexity of RNA-RNA interaction prediction problem as described by Alkan et al. [1] by a linear factor via dynamic programming sparsification- which allows to safely discard large portions of DP tables. Applying sparsification techniques reduces the complexity of the original algorithm to O(n 4 ψ(n)) in time and O(n 2 ψ(n) + n 3) in space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice. By the use of polymer-zeta property for RNA-structures, we demonstrate that ψ(n) = O(n) on average. We evaluate our sparsified algorithm for RNA-RNA interaction prediction through total free energy minimization, based on the energy model of Chitsaz et al. [11], on a set of known interactions. Our results confirm the significant reduction of time and space requirements in practice.
