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137
structure for unaligned sequences
, 2011
"... RNAG: a new Gibbs sampler for predicting RNA secondary ..."
A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences
, 2004
"... ..."
RNAG: A New Gibbs Sampler for Predicting RNA Secondary Structure for Unaligned Sequences
, 2011
"... Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this paper, w ..."
Abstract
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Cited by 3 (0 self)
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, we present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability
Exploratory visualization of misclassified GPCRs from their transformed unaligned sequences using manifold learning techniques
- In Procs. of the 2nd International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO
, 2014
"... Abstract. Class C G-protein-coupled receptors (GPCRs) are cell membrane proteins of great relevance to biology and pharmacology. Previous research has revealed an upper boundary on the accuracy that can be achieved in their classification into subtypes from the unaligned transformation of their seq ..."
Abstract
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Cited by 1 (1 self)
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Abstract. Class C G-protein-coupled receptors (GPCRs) are cell membrane proteins of great relevance to biology and pharmacology. Previous research has revealed an upper boundary on the accuracy that can be achieved in their classification into subtypes from the unaligned transformation
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
- Proc Int Conf Intell Syst Mol Biol
, 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
Abstract
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Cited by 947 (5 self)
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to the data, probabilistically erasing tile occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a threshold which
BIOINFORMATICS PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences
"... Motivation: To construct a multiple sequence alignment (MSA) of a large number (>10,000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. Results: To overcome this limitation, we have develop ..."
Abstract
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Motivation: To construct a multiple sequence alignment (MSA) of a large number (>10,000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. Results: To overcome this limitation, we have
Hidden Markov models in computational biology: applications to protein modeling
- JOURNAL OF MOLECULAR BIOLOGY
, 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding moti ..."
Abstract
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Cited by 655 (39 self)
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motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the. SWISS-PROT 22 database for other sequences. that are members of the given
Phyloscan: locating transcription-regulating binding
, 2010
"... sites in mixed aligned and unaligned sequence data ..."
R: RNA sequence analysis using covariance models. Nucleic Aeids Res
, 1994
"... We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an ..."
Abstract
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Cited by 367 (9 self)
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unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA scondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences.
Hidden Markov models for sequence analysis: extension and analysis of the basic method
, 1996
"... Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maxi ..."
Abstract
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Cited by 219 (23 self)
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Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation
Results 1 - 10
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