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1,177
Taverna: a tool for building and running workflows of services
- Nucleic Acids Res
, 2006
"... Taverna is an application that eases the use and integration of the growing number of molecular biology tools and databases available on the web, especially web services. It allows bioinformaticians to construct workflows or pipelines of services to perform a range of different analyses, such as seq ..."
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Cited by 280 (12 self)
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Taverna is an application that eases the use and integration of the growing number of molecular biology tools and databases available on the web, especially web services. It allows bioinformaticians to construct workflows or pipelines of services to perform a range of different analyses, such as sequence analysis and genome annotation. These high-level workflows can integrate many different resources into a single analysis. Taverna is available freely under the terms of the GNU Lesser General Public License (LGPL) from
The group Lasso for logistic regression
- Journal of the Royal Statistical Society, Series B
, 2008
"... Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regressi ..."
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Cited by 276 (11 self)
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Summary. The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models and present an efficient algorithm, that is especially suitable for high dimensional problems, which can also be applied to generalized linear models to solve the corresponding convex optimization problem. The group lasso estimator for logistic regression is shown to be statistically consistent even if the number of predictors is much larger than sample size but with sparse true underlying structure. We further use a two-stage procedure which aims for sparser models than the group lasso, leading to improved prediction performance for some cases. Moreover, owing to the two-stage nature, the estimates can be constructed to be hierarchical. The methods are used on simulated and real data sets about splice site detection in DNA sequences.
Prediction of the coding sequences of unidentified human genes. VI. The coding sequences of 80 new genes (KIAA0201KIAA0280) deduced by analysis of cDNA clones from cell line KG-1 and brain
- DNA Res
, 1996
"... In this series of projects of sequencing human cDNA clones which correspond to relatively long transcripts, we newly determined the entire sequences of 100 cDNA clones which were screened on the basis of the potentiality of coding for large proteins in vitro. The cDNA libraries used were the fractio ..."
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Cited by 194 (15 self)
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In this series of projects of sequencing human cDNA clones which correspond to relatively long transcripts, we newly determined the entire sequences of 100 cDNA clones which were screened on the basis of the potentiality of coding for large proteins in vitro. The cDNA libraries used were the fractions with average insert sizes from 5.3 to 7.0 kb of the size-fractionated cDNA libraries from human brain. The randomly sampled clones were single-pass sequenced from both the ends to select clones that are not registered in the public database. Then their protein-coding potentialities were examined by an in vitro transcription/translation system, and the clones that generated proteins larger than 60 kDa were entirely sequenced. Each clone gave a distinct open reading frame (ORF), and the length of the ORF was roughly coincident with the approximate molecular mass of the in vitro product estimated from its mobility on SDS-polyacrylamide gel electrophoresis. The average size of the cDNA clones sequenced was 6.1 kb, and that of the ORFs corresponded to 1200 amino acid residues. By computer-assisted analysis of the sequences with DNA and protein-motif databases (GenBank and PROSITE databases), the functions of at least 73% of the gene products could be anticipated, and 88 % of them (the products of 64 clones) were assigned to the functional categories of proteins relating to cell signaling/communication, nucleic acid managing,
Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
- J. Comput. Biol
, 2004
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Gene prediction with a hidden Markov model and a new intron submodel
- Bioinformatics
, 2003
"... The problem of finding the genes in eukaryotic DNA sequences by computational methods is still not satisfactorily solved. Gene finding programs have achieved relatively high accuracy on short genomic sequences but do not perform well on longer sequences with an unknown number of genes in them. Here ..."
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Cited by 170 (8 self)
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The problem of finding the genes in eukaryotic DNA sequences by computational methods is still not satisfactorily solved. Gene finding programs have achieved relatively high accuracy on short genomic sequences but do not perform well on longer sequences with an unknown number of genes in them. Here existing programs tend to predict many false exons. We have developed a new program, AUGUSTUS, for the ab initio prediction of protein coding genes in eukaryotic genomes. The program is based on a Hidden Markov Model and integrates a number of known methods and submodels. It employs a new way of modeling intron lengths. We use a new donor splice site model, a new model for a short region directly upstream of the donor splice site model that takes the reading frame into account and apply a method that allows better GC-content dependent parameter estimation. AUGUSTUS predicts on longer sequences far more human and drosophila genes accurately than the ab initio gene prediction programs we compared it with, while at the same time being more specific. A web interface for AUGUSTUS and the executable program are located at
Human and mouse gene structure: comparative analysis and application to exon prediction. Genome Res
, 2000
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Additivity in protein–DNA interactions: how good an approximation is it
- Nucleic Acids Res
, 2002
"... Man and Stormo and Bulyk et al. recently presented their results on the study of the DNA binding af®nity of proteins. In both of these studies the main conclusion is that the additivity assumption, usually applied in methods to search for binding sites, is not true. In the ®rst study, the analysis o ..."
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Cited by 162 (24 self)
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Man and Stormo and Bulyk et al. recently presented their results on the study of the DNA binding af®nity of proteins. In both of these studies the main conclusion is that the additivity assumption, usually applied in methods to search for binding sites, is not true. In the ®rst study, the analysis of binding af®nity data from the Mnt repressor protein bound to all possible DNA (sub)targets at positions 16 and 17 of the binding site, showed that those positions are not independent. In the second study, the authors analysed DNA binding af®nity data of the wild-type mouse EGR1 protein and four variants differing on the middle ®nger. The binding af®nity of these proteins was measured to all 64 possible trinucleotide (sub)targets of the middle ®nger using microarray technology. The analysis of the measurements also showed interdependence among the positions in the DNA target. In the present report, we review the data of both studies and we reanalyse them using various statistical methods, including a comparison with a multiple regression approach. We conclude that despite the fact that the additivity assumption does not ®t the data perfectly, in most cases it provides a very good approximation of the true nature of the speci®c protein±DNA interactions. Therefore, additive models can be very useful for the discovery and prediction of binding sites in genomic DNA.
Two methods for improving performance of an HMM and their application for gene finding
, 1997
"... A hidden Markov model for gene finding consists of submodels for coding regions, splice sites, introns, intergenic regions and possibly more. It is described how to estimate the model as a whole from labeled sequences instead of estimating the individual parts independently from subsequences. It is ..."
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Cited by 147 (7 self)
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A hidden Markov model for gene finding consists of submodels for coding regions, splice sites, introns, intergenic regions and possibly more. It is described how to estimate the model as a whole from labeled sequences instead of estimating the individual parts independently from subsequences. It is argued that the standard maximum likelihood estimation criterion is not optimal for training such a model. Instead of maximizing the probability of the DNA sequence, one should maximize the probability of the correct prediction. Such a criterion, called conditional maximum likelihood, is used for the gene finder ’HMMgene’. A new (approximative) algorithm is described, which finds the most probable prediction summed over all paths yielding the same prediction. We show that these methods contribute significantly to the high performance of HMMgene.
Combining phylogenetic and hidden Markov models in biosequence analysis
- J. Comput. Biol
, 2004
"... A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individ ..."
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Cited by 135 (13 self)
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A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction—for gene finding, for example, or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higher-order states in the HMM, which allows for context-sensitive models of substitution— that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higher-order states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higher-order states being particularly pronounced.
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites
, 2000
"... Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification pro ..."
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Cited by 133 (14 self)
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Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines (SVMs) for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.
