Results 1 - 10
of
809
The Vienna RNA Secondary Structure Server
- Nucleic Acids Res
, 2003
"... The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently o#ers prediction of secondary structure from a single sequence, prediction of the consensus secondary ..."
Abstract
-
Cited by 611 (23 self)
- Add to MetaCart
(Show Context)
The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently o#ers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences, and the design of sequences that will fold into a predefined structure.
Fast and reliable prediction of noncoding RNAs
- Proc Natl Acad Sci USA
"... We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scale-genomic screens. It consists of two basic components: ..."
Abstract
-
Cited by 335 (45 self)
- Add to MetaCart
(Show Context)
We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scale-genomic screens. It consists of two basic components: (1) a novel measure for RNA secondary structure conservation based on computing a consensus secondary structure, and (2) a measure for thermodynamic stability, which — in the spirit of a z-score — is normalized w.r.t. both sequence length and base composition but can be calculated without sampling from shuffled sequences. Functional RNA secondary structures can be identified in multiple sequence alignments with high sensitivity and high specificity. We demonstrate that this approach is not only much more accurate than previous methods but also significantly faster. The method is implemented in the program RNAz, which can be downloaded from
The microRNAs of Caenorhabditis elegans
- Genes Dev
, 2003
"... MicroRNAs (miRNAs) are an abundant class of tiny RNAs thought to regulate the expression of protein-coding genes in plants and animals. In the present study, we describe a computational procedure to identify miRNA genes conserved in more than one genome. Applying this program, known as MiRscan, toge ..."
Abstract
-
Cited by 186 (13 self)
- Add to MetaCart
(Show Context)
MicroRNAs (miRNAs) are an abundant class of tiny RNAs thought to regulate the expression of protein-coding genes in plants and animals. In the present study, we describe a computational procedure to identify miRNA genes conserved in more than one genome. Applying this program, known as MiRscan, together with molecular identification and validation methods, we have identified most of the miRNA genes in the nematode Caenorhabditis elegans. The total number of validated miRNA genes stands at 88, with no more than 35 genes remaining to be detected or validated. These 88 miRNA genes represent 48 gene families; 46 of these families (comprising 86 of the 88 genes) are conservedin Caenorhabditis briggsae, and22 families are conservedin humans. More than a thirdof the worm miRNAs, including newly identified members of the lin-4 and let-7 gene families, are differentially expressed during larval development, suggesting a role for these miRNAs in mediating larval developmental transitions. Most are present at very high steady-state levels—more than 1000 molecules per cell, with some exceeding 50,000 molecules per cell. Our census of the worm miRNAs andtheir expression patterns helps define this class of noncoding RNAs, lays the groundwork for functional studies, and provides the tools for more comprehensive analyses of miRNA genes in other species. [Keywords: miRNA; noncoding RNA; computational gene identification; Dicer] Supplemental material is available at
A benchmark of multiple sequence alignment programs upon structural RNAs
- Nucleic Acids Res
, 2005
"... To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we ..."
Abstract
-
Cited by 144 (20 self)
- Add to MetaCart
(Show Context)
To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structuralRNAalignmentproblem.Thisindicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate,50–60 % sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequence-based algorithms, under the broadest range of applications.
Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans
- Cell
, 2006
"... We sequenced 400,000 small RNAs from ..."
(Show Context)
CONTRAfold: RNA secondary structure prediction without physics-based models
- Bioinformatics
, 2006
"... doi:10.1093/bioinformatics/btl246 ..."
(Show Context)
A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes
- Cell
, 2006
"... These authors contributed equally to this work. ..."
(Show Context)
Landscapes and Their Correlation Functions
, 1996
"... Fitness landscapes are an important concept in molecular evolution. Many important examples of landscapes in physics and combinatorial optimation, which are widely used as model landscapes in simulations of molecular evolution and adaptation, are "elementary", i.e., they are (up to an addi ..."
Abstract
-
Cited by 105 (16 self)
- Add to MetaCart
Fitness landscapes are an important concept in molecular evolution. Many important examples of landscapes in physics and combinatorial optimation, which are widely used as model landscapes in simulations of molecular evolution and adaptation, are "elementary", i.e., they are (up to an additive constant) eigenfuctions of a graph Laplacian. It is shown that elementary landscapes are characterized by their correlation functions. The correlation functions are in turn uniquely determined by the geometry of the underlying configuration space and the nearest neighbor correlation of the elementary landscape. Two types of correlation functions are investigated here: the correlation of a time series sampled along a random walk on the landscape and the correlation function with respect to a partition of the set of all vertex pairs.
Plasticity, evolvability, and modularity in RNA
- J EXP ZOOL
, 2000
"... RNA folding from sequences into secondary structures is a simple yet powerful, biophysically grounded model of a genotype–phenotype map in which concepts like plasticity, evolvability, epistasis, and modularity can not only be precisely defined and statistically measured but also reveal simultaneou ..."
Abstract
-
Cited by 102 (3 self)
- Add to MetaCart
RNA folding from sequences into secondary structures is a simple yet powerful, biophysically grounded model of a genotype–phenotype map in which concepts like plasticity, evolvability, epistasis, and modularity can not only be precisely defined and statistically measured but also reveal simultaneous and profoundly non-independent effects of natural selection. Molecular plasticity is viewed here as the capacity of an RNA sequence to assume a variety of energetically favorable shapes by equilibrating among them at constant temperature. Through simulations based on experimental designs, we study the dynamics of a population of RNA molecules that evolve toward a predefined target shape in a constant environment. Each shape in the plastic repertoire of a sequence contributes to the overall fitness of the sequence in proportion to the time the sequence spends in that shape. Plasticity is costly, since the more shapes a sequence can assume, the less time it spends in any one of them. Unsurprisingly, selection leads to a reduction of plasticity (environmental canalization). The most striking observation, however, is the simultaneous slow-down and eventual halting of the evolutionary process. The reduction of plasticity entails genetic canalization, that is, a dramatic loss of variability (and hence a loss of evolvability) to the point of lock-in. The causal bridge between environmental canalization and genetic canalization
Generic Properties of Combinatory Maps - Neutral Networks of RNA Secondary Structures
, 1995
"... Random graph theory is used to model relationships between sequences and secondary structures of RNA molecules. Sequences folding into identical structures form neutral networks which percolate sequence space if the fraction of neutral nearest neighbors exceeds a threshold value. The networks of any ..."
Abstract
-
Cited by 90 (42 self)
- Add to MetaCart
Random graph theory is used to model relationships between sequences and secondary structures of RNA molecules. Sequences folding into identical structures form neutral networks which percolate sequence space if the fraction of neutral nearest neighbors exceeds a threshold value. The networks of any two different structures almost touch each other, and sequences folding into almost all "common" structures can be found in a small ball of an arbitrary location in sequence space. The results from random graph theory are compared with data obtained by folding large samples of RNA sequences. Differences are explained in terms of RNA molecular structures. 1.
