Inferring gene regression networks with model trees

Background  

Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.     

The mitochondrial genome structure of Xenoturbella bocki (phylum Xenoturbellida) is ancestral within the deuterostomes

Background  

Mitochondrial genome comparisons contribute in multiple ways when inferring animal relationships. As well as primary sequence data, rare genomic changes such as gene order, shared gene boundaries and genetic code changes, which are unlikely to have arisen through convergent evolution, are useful tools in resolving deep phylogenies. Xenoturbella bocki is a morphologically simple benthic marine worm recently found to belong among the deuterostomes. Here we present analyses comparing the Xenoturbella bocki mitochondrial gene order, genetic code and control region to those of other metazoan groups.     

Comment on and reply to "Analysis of variation of amplitudes in cell cycle gene expression" by Liu,Gaido and Wolfinger: On the analysis of gene expression during the normal,eukaryotic, cell cycle

Background  

The paper of Liu, Gaido and Wolfinger on gene expression during the division cycle of HeLa cells using the data of Whitfield et al. are discussed in order to see whether their analysis is related to gene expression during the division cycle.     

Support for multiple classes of local expression clusters in <Emphasis Type="Italic">Drosophila melanogaster</Emphasis>, but no evidence for gene order conservation

Background  

Gene order in eukaryotic genomes is not random, with genes with similar expression profiles tending to cluster. In yeasts, the model taxon for gene order analysis, such syntenic clusters of non-homologous genes tend to be conserved over evolutionary time. Whether similar clusters show gene order conservation in other lineages is, however, undecided. Here, we examine this issue in Drosophila melanogaster using high-resolution chromosome rearrangement data.     

The mitochondrial genome of <Emphasis Type="Italic">Phallusia mammillata</Emphasis> and <Emphasis Type="Italic">Phallusia fumigata</Emphasis> (Tunicata,Ascidiacea): high genome plasticity at intra-genus level

Background  

Within Chordata, the subphyla Vertebrata and Cephalochordata (lancelets) are characterized by a remarkable stability of the mitochondrial (mt) genome, with constancy of gene content and almost invariant gene order, whereas the limited mitochondrial data on the subphylum Tunicata suggest frequent and extensive gene rearrangements, observed also within ascidians of the same genus.     

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

Background  

Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.     

Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

Background  

Cellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied.     

Classification of microarray data using gene networks

Background  

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation.     

Validation and characterization of DNA microarray gene expression data distribution and associated moments

Background  

The data from DNA microarrays are increasingly being used in order to understand effects of different conditions, exposures or diseases on the modulation of the expression of various genes in a biological system. This knowledge is then further used in order to generate molecular mechanistic hypotheses for an organism when it is exposed to different conditions. Several different methods have been proposed to analyze these data under different distributional assumptions on gene expression. However, the empirical validation of these assumptions is lacking.     

New miRNA labeling method for bead-based quantification

Background  

microRNAs (miRNAs) are small single-stranded non-coding RNAs that act as crucial regulators of gene expression. Different methods have been developed for miRNA expression profiling in order to better understand gene regulation in normal and pathological conditions. miRNAs expression values obtained from large scale methodologies such as microarrays still need a validation step with alternative technologies.     

Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training

Background  

Hidden Markov models are widely employed by numerous bioinformatics programs used today. Applications range widely from comparative gene prediction to time-series analyses of micro-array data. The parameters of the underlying models need to be adjusted for specific data sets, for example the genome of a particular species, in order to maximize the prediction accuracy. Computationally efficient algorithms for parameter training are thus key to maximizing the usability of a wide range of bioinformatics applications.     

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background  

The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge.     

Resolving deconvolution ambiguity in gene alternative splicing

Background  

For many gene structures it is impossible to resolve intensity data uniquely to establish abundances of splice variants. This was empirically noted by Wang et al. in which it was called a "degeneracy problem". The ambiguity results from an ill-posed problem where additional information is needed in order to obtain an unique answer in splice variant deconvolution.     

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Background  

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.     

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.     

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation

Background  

External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.     

Organization of the mitochondrial genomes of whiteflies,aphids, and psyllids (Hemiptera,Sternorrhyncha)

Background  

With some exceptions, mitochondria within the class Insecta have the same gene content, and generally, a similar gene order allowing the proposal of an ancestral gene order. The principal exceptions are several orders within the Hemipteroid assemblage including the order Thysanoptera, a sister group of the order Hemiptera. Within the Hemiptera, there are available a number of completely sequenced mitochondrial genomes that have a gene order similar to that of the proposed ancestor. None, however, are available from the suborder Sternorryncha that includes whiteflies, psyllids and aphids.