共查询到20条相似文献,搜索用时 31 毫秒
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Marjan De Mey Jo Maertens Sarah Boogmans Wim K Soetaert Erick J Vandamme Raymond Cunin Maria R Foulquié-Moreno 《BMC biotechnology》2010,10(1):26
Background
Metabolic engineering aims at channeling the metabolic fluxes towards a desired compound. An important strategy to achieve this is the modification of the expression level of specific genes. Several methods for the modification or the replacement of promoters have been proposed, but most of them involve time-consuming screening steps. We describe here a novel optimized method for the insertion of constitutive promoters (referred to as "promoter knock-in") whose strength can be compared with the native promoter by applying a promoter strength predictive (PSP) model. 相似文献4.
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Background
gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms. 相似文献8.
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Background
Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. 相似文献10.
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Background
The expression of recombinant proteins in eukaryotic cells requires the fusion of the coding region to a promoter functional in the eukaryotic cell line. Viral promoters are very often used for this purpose. The preceding cloning procedures are usually performed in Escherichia coli and it is therefore of interest if the foreign promoter results in an expression of the gene in bacteria. In the case molecules toxic for humans are to be expressed, this knowledge is indispensable for the specification of safety measures. 相似文献12.
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Background
A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence. 相似文献15.
Background
The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. 相似文献16.
Cornelia Caragea Jivko Sinapov Adrian Silvescu Drena Dobbs Vasant Honavar 《BMC bioinformatics》2007,8(1):438
Background
Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. 相似文献17.
Background
One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.Results
We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.Conclusions
Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.Electronic supplementary material
The online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users. 相似文献18.
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Background
Experimental identification of microRNA (miRNA) targets is a difficult and time consuming process. As a consequence several computational prediction methods have been devised in order to predict targets for follow up experimental validation. Current computational target prediction methods use only the miRNA sequence as input. With an increasing number of experimentally validated targets becoming available, utilising this additional information in the search for further targets may help to improve the specificity of computational methods for target site prediction. 相似文献20.
Emily Chia-Yu Su Hua-Sheng Chiu Allan Lo Jenn-Kang Hwang Ting-Yi Sung Wen-Lian Hsu 《BMC bioinformatics》2007,8(1):330