A novel method for prokaryotic promoter prediction based on DNA stability

Background  

In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.     

A comparison study on feature selection of DNA structural properties for promoter prediction

Background  

Promoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task.     

CpGcluster: a distance-based algorithm for CpG-island detection

Background  

Despite their involvement in the regulation of gene expression and their importance as genomic markers for promoter prediction, no objective standard exists for defining CpG islands (CGIs), since all current approaches rely on a large parameter space formed by the thresholds of length, CpG fraction and G+C content.     

Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach

Background  

Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.     

Identifying novel genes in <Emphasis Type="Italic">C. elegans</Emphasis> using SAGE tags

Background  

Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required.     

A sugar beet chlorophyll a/b binding protein promoter void of G-box like elements confers strong and leaf specific reporter gene expression in transgenic sugar beet

Background  

Modification of leaf traits in sugar beet requires a strong leaf specific promoter. With such a promoter, expression in taproots can be avoided which may otherwise take away available energy resources for sugar accumulation.     

GATA elements control repression of cardiac troponin I promoter activity in skeletal muscle cells

Background  

We reported previously that the cardiac troponin I (cTnI) promoter drives cardiac-specific expression of reporter genes in cardiac muscle cells and in transgenic mice, and that disruption of GATA elements inactivates the cTnI promoter in cultured cardiomyocytes. We have now examined the role of cTnI promoter GATA elements in skeletal muscle cells.     

Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs

Background  

The canonical core promoter elements consist of the TATA box, initiator (Inr), downstream core promoter element (DPE), TFIIB recognition element (BRE) and the newly-discovered motif 10 element (MTE). The motifs for these core promoter elements are highly degenerate, which tends to lead to a high false discovery rate when attempting to detect them in promoter sequences.     

Goat uromodulin promoter directs kidney-specific expression of GFP gene in transgenic mice

Background  

Uromodulin is the most abundant protein found in the urine of mammals. In an effort to utilize the uromodulin promoter in order to target recombinant proteins in the urine of transgenic animals we have cloned a goat uromodulin gene promoter fragment (GUM promoter) and used it to drive expression of GFP in the kidney of transgenic mice.     

Association between <Emphasis Type="Italic">IL-18</Emphasis>gene polymorphisms and biopsy-proven giant cell arteritis

Introduction  

The objective was to investigate the potential implication of the IL18 gene promoter polymorphisms in the susceptibility to giant-cell arteritis (GCA).     

Improved residue contact prediction using support vector machines and a large feature set

Background  

Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved.     

Cre-LoxP-regulated expression of monoclonal antibodies driven by an ovalbumin promoter in primary oviduct cells

Background  

A promoter capable of driving high-level transgene expression in oviduct cells is important for developing transgenic chickens capable of producing therapeutic proteins, including monoclonal antibodies (mAbs), in the whites of laid eggs. Ovalbumin promoters can be used as oviduct-specific regulatory sequences in transgenic chickens, but their promoter activities are not high, according to previous reports.     

Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations

Background  

The accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations.     

Impact of RNA structure on the prediction of donor and acceptor splice sites

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.