bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low degree metabolites explain essential reactions and enhance modularity in biological networks
Authors: Samal Areejit , Singh Shalini , Giri Varun , Krishna Sandeep , Raghuram Nandula , Jain Sanjay 
Published: March 2006

Abstract

Background
Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints.

Results
In the metabolic networks of Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus, we identified metabolites with a low degree of connectivity, particularly those that are produced and/or consumed in just a single reaction. Using flux balance analysis (FBA) we also determined reactions essential for growth in these metabolic networks. We find that most reactions identified as essential in these networks turn out to be those involving the production or consumption of low degree metabolites. Applying graph theoretic methods to these metabolic networks, we identified connected clusters of these low degree metabolites. The genes involved in several operons in E. coli are correctly predicted as those of enzymes catalyzing the reactions of these clusters. Furthermore, we find that larger sized clusters are over-represented in the real network and are analogous to a 'network motif. Using FBA for the above mentioned three organisms we independently identified clusters of reactions whose fluxes are perfectly correlated. We find that the composition of the latter 'functional clusters' is also largely explained in terms of clusters of low degree metabolites in each of these organisms.

Conclusion
Our findings mean that most metabolic reactions that are essential can be tagged by one or more low degree metabolites. Those reactions are essential because they are the only ways of producing or consuming their respective tagged metabolites. Furthermore, reactions whose fluxes are strongly correlated can be thought of as 'glued together' by these low degree metabolites. The methods developed here could be used in predicting essential reactions and metabolic modules in other organisms from the list of metabolic reactions.
TF Target Mapper: A BLAST search tool for the identification of Transcription Factor target genes
Authors: Horsman Sebastiaan , Moorhouse Michael J , de Jager Victor CL , van der Spek Peter , Grosveld Frank , Strouboulis John , Katsantoni Eleni Z 
Published: March 2006

Abstract

Background
In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it.

Results
In order to facilitate this analysis we generated TF Target Mapper (T ranscription F actor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. α-globin and ζ-globin) and potentially novel GATA-1 gene targets.

Conclusion
TF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.
An unsupervised classification scheme for improving predictions of prokaryotic TIS
Authors: Tech Maike , Meinicke Peter 
Published: March 2006

AbstractBackgroundAlthough it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes.ResultsWe introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data fromE. coliandB. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance onP. aeruginosa,B. pseudomalleiandR. solanacearum.ConclusionOn reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool »TICO«(TIs COrrector) which is publicly available from our web site.
Selection of antisense oligonucleotides based on multiple predicted target mRNA structures
Authors: Bo Xiaochen , Lou Shaoke , Sun Daochun , Shu Wenjie , Yang Jing , Wang Shengqi 
Published: March 2006

Abstract

Background
Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed.

Results
Two methods were considered for merging topologically different RNA structures into integrated representations. Several parameters were derived to characterize local target site structures. Statistical analysis on a dataset with 448 ODNs against 28 different mRNAs revealed 9 features quantitatively associated with efficacy. Features of structural consistency seemed to be more highly correlated with efficacy than indices of the proportion of bases in single-stranded or double-stranded regions. The local structures of the target site 5' and 3' termini were also shown to be important in target selection. Neural network efficacy predictors using these features, defined on integrated structures as inputs, performed well in "minus-one-gene" cross-validation experiments.

Conclusion
Topologically different target mRNA structures can be merged into integrated representations and then used in computer-aided ODN design. The results of this paper imply that some features characterizing multiple predicted target site structures can be used to predict ODN efficacy.
Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate
Authors: Motulsky Harvey J , Brown Ronald E 
Published: March 2006

AbstractBackgroundNonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression.ResultsWe describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method.When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%.ConclusionOur method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives.
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Authors: Meissner Michael , Schmuker Michael , Schneider Gisbert 
Published: March 2006

Abstract

Background
Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.

Results
Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained.

Conclusion
Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
A two-sample Bayesian t-test for microarray data
Authors: Fox Richard J , Dimmic Matthew W 
Published: March 2006

Abstract

Background
Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use of t-tests with pooled estimates of the sample variance based on similarly expressed genes. These methods do not display consistent behavior across the entire range of pooling and can be biased when the prior hyperparameters are specified heuristically.

Results
A two-sample Bayesian t-test is proposed for use in determining whether a gene is differentially expressed in two different samples. The test method is an extension of earlier work that made use of point estimates for the variance. The method proposed here explicitly calculates in analytic form the marginal distribution for the difference in the mean expression of two samples, obviating the need for point estimates of the variance without recourse to posterior simulation. The prior distribution involves a single hyperparameter that can be calculated in a statistically rigorous manner, making clear the connection between the prior degrees of freedom and prior variance.

Conclusion
The test is easy to understand and implement and application to both real and simulated data shows that the method has equal or greater power compared to the previous method and demonstrates consistent Type I error rates. The test is generally applicable outside the microarray field to any situation where prior information about the variance is available and is not limited to cases where estimates of the variance are based on many similar observations.
Determination of strongly overlapping signaling activity from microarray data
Authors: Bidaut Ghislain , Suhre Karsten , Claverie Jean-Michel , Ochs Michael F 
Published: March 2006

AbstractBackgroundAs numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups.ResultsHere we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, criticially, individual signatures of the strongly coupled mating and filamentation pathways.ConclusionThis works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.
Aberrant stalk development and breakdown of tip dominance in Dictyosteliumcell lines with RNAi-silenced expression of calcineurin B
Authors: Boeckeler Katrina , Tischendorf Gilbert , Mutzel Rupert , Weissenmayer Barbara 
Published: March 2006

Abstract

Background
Calcineurin, the Ca2+/calmodulin-dependent protein phosphatase, plays important roles in various cellular processes in lower and higher eukaryotes. Here we analyze the role of calcineurin in the development of Dictyostelium discoideum by RNAi-mediated manipulation of its expression.

Results
The cnbA gene of Dictyostelium discoideum which encodes the regulatory B subunit (CNB) of calcineurin was silenced by RNAi. We found a variety of silencing levels of CNB in different recombinant cell lines. Reduction of CNB expression in a given cell line was correlated with developmental aberrations. Cell lines with strongly reduced protein levels developed slower than wild type cells and formed short stalks and spore heads with additional tips. Formation of short stalks results from incomplete vacuolization of prestalk cells during terminal differentiation. Expression of the stalk-specific gene ecmB was reduced in mutant cells. Aberrant stalk development is a cell autonomous defect, whereas the breakdown of tip dominance can be prevented by the presence of as low as 10% wild type cells in chimeras.

Conclusion
Silencing of calcineurin B in Dictyostelium by expression of RNAi reveals an unexpected link between increased intracellular calcium levels, possibly triggered by the morphogen DIF, activation of calcineurin, and the terminal stage of morphogenesis.
laminin alpha 1gene is essential for normal lens development in zebrafish
Authors: Zinkevich Natalya S , Bosenko Dmitry V , Link Brian A , Semina Elena V 
Published: March 2006

AbstractBackgroundLaminins represent major components of basement membranes and play various roles in embryonic and adult tissues. The functional laminin molecule consists of three chains, alpha, beta and gamma, encoded by separate genes. There are twelve different laminin genes identified in mammals to date that are highly homologous in their sequence but different in their tissue distribution. Thelaminin alpha -1gene was shown to have the most restricted expression pattern with strong expression in ocular structures, particularly in the developing and mature lens.ResultsWe identified the zebrafishlama1gene encoding a 3075-amino acid protein (lama1) that possesses strong identity with the human LAMA1. Zebrafishlama1transcripts were detected at all stages of embryo development with the highest levels of expression in the developing lens, somites, nervous and urogenital systems. Translation of thelama1gene was inhibited using two non-overlapping morpholino oligomers that were complementary to sequences surrounding translation initiation. Morphant embryos exhibited an arrest in lens development and abnormalities in the body axis length and curvature.ConclusionThese results underline the importance of thelaminin alpha 1for normal ocular development and provide a basis for further analysis of its developmental roles.
Evolution of the P-type II ATPase gene family in the fungi and presence of structural genomic changes among isolates of Glomus intraradices
Authors: Corradi Nicolas , Sanders Ian R 
Published: March 2006

Abstract

Background
The P-type II ATPase gene family encodes proteins with an important role in adaptation of the cell to variation in external K+, Ca2+ and Na2+ concentrations. The presence of P-type II gene subfamilies that are specific for certain kingdoms has been reported but was sometimes contradicted by discovery of previously unknown homologous sequences in newly sequenced genomes. Members of this gene family have been sampled in all of the fungal phyla except the arbuscular mycorrhizal fungi (AMF; phylum Glomeromycota), which are known to play a key-role in terrestrial ecosystems and to be genetically highly variable within populations. Here we used highly degenerate primers on AMF genomic DNA to increase the sampling of fungal P-Type II ATPases and to test previous predictions about their evolution. In parallel, homologous sequences of the P-type II ATPases have been used to determine the nature and amount of polymorphism that is present at these loci among isolates of Glomus intraradices harvested from the same field.

Results
In this study, four P-type II ATPase sub-families have been isolated from three AMF species. We show that, contrary to previous predictions, P-type IIC ATPases are present in all basal fungal taxa. Additionally, P-Type IIE ATPases should no longer be considered as exclusive to the Ascomycota and the Basidiomycota, since we also demonstrate their presence in the Zygomycota. Finally, a comparison of homologous sequences encoding P-type IID ATPases showed unexpectedly that indel mutations among coding regions, as well as specific gene duplications occur among AMF individuals within the same field.

Conclusion
On the basis of these results we suggest that the diversification of P-Type IIC and E ATPases followed the diversification of the extant fungal phyla with independent events of gene gains and losses. Consistent with recent findings on the human genome, but at a much smaller geographic scale, we provided evidence that structural genomic changes, such as exonic indel mutations and gene duplications are less rare than previously thought and that these also occur within fungal populations.
Characterization of the canine CLCN3 gene and evaluation as candidate for late-onset NCL
Authors: Wohlke Anne , Distl Ottmar , Drogemuller Cord 
Published: March 2006

AbstractBackgroundThe neuronal ceroid lipofuscinoses (NCL) are a heterogenous group of inherited progressive neurodegenerative diseases in different mammalian species. Tibetan Terrier and Polish Owczarek Nizinny (PON) dogs show rare late-onset NCL variants with autosomal recessive inheritance, which can not be explained by mutations of known human NCL genes. These dog breeds represent animal models for human late-onset NCL. In mice the chloride channel 3 gene (Clcn3) encoding an intracellular chloride channel was described to cause a phenotype similar to NCL.ResultsTwo full-length cDNA splice variants of the canineCLCN3gene are reported. The current canine whole genome sequence assembly was used for gene structure analyses and revealed 13 codingCLCN3exons in 52 kb of genomic sequence. Sequence analysis of the coding exons and flanking intron regions ofCLCN3using six NCL-affected Tibetan terrier dogs and an NCL-affected Polish Owczarek Nizinny (PON) dog, as well as eight healthy Tibetan terrier dogs revealed 13 SNPs. No consistentCLCN3haplotype was associated with NCL.ConclusionFor the examined animals we excluded the complete coding region and adjacent intronic regions of canineCLCN3to harbor disease-causing mutations. Therefore it seems to be unlikely that a mutation in this gene is responsible for the late-onset NCL phenotype in these two dog breeds.