MODELING DEPENDENT GENE EXPRESSION.
Ontology highlight
ABSTRACT:
SUBMITTER: Telesca D
PROVIDER: S-EPMC5412732 | biostudies-literature | 2012
REPOSITORIES: biostudies-literature
Publications
MODELING DEPENDENT GENE EXPRESSION.
Telesca Donatello D Müller Peter P Parmigiani Giovanni G Freedman Ralph S RS
Annals of statistics 20120611 2
In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as ...[more]
Similar Datasets
Project description:BackgroundIn-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted.ResultsWe developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results.ConclusionsThe analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.
| S-EPMC8626902 | biostudies-literature
Project description:About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group.
| S-EPMC2367561 | biostudies-literature
Project description:ERK2-dependent gene expression
| PRJNA514405 | ENA
Project description:Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, coexpression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Modeling such spatial correlation explicitly may lead to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this paper, we explore chromosomal spatial correlation induced by 3D chromosome structures, and propose a hierarchical Bayesian method based on helical structures to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarrays. Simulation studies show computing feasibility of the proposed method and that, under the assumption of helical chromosome structures, it can lead to precise estimation of structural parameters and gene expression levels. Real data applications demonstrate an intriguing biological phenomenon that functionally associated genes, which are far apart along the chromosome chain, are brought into physical proximity by chromosomal folding in 3D space to facilitate their coexpression. It leads to important biological insight into relationship between chromosome structure and function.
| S-EPMC3134274 | biostudies-literature
Project description:The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr) on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations.
| S-EPMC3426564 | biostudies-literature
Project description:Postnatal cortical synaptic development is characterized by stages of exuberant growth, pruning, and stabilization during adulthood. How gene expression orchestrates these stages of synaptic development is poorly understood. Here we report that synaptic growth-related gene expression alone does not determine cortical synaptic density changes across the human lifespan, but instead, the dynamics of cortical synaptic density can be accurately simulated by a first-order kinetic model of synaptic growth and elimination that incorporates two separate gene expression patterns. Surprisingly, modeling of cortical synaptic density is optimized when genes related to oligodendrocytes are used to determine synaptic elimination rates. Expression of synaptic growth and oligodendrocyte genes varies regionally, resulting in different predictions of synaptic density among cortical regions that concur with previous regional data in humans. Our analysis suggests that modest rates of synaptic growth persist in adulthood, but that this is counterbalanced by increasing rates of synaptic elimination, resulting in stable synaptic number and ongoing synaptic turnover in the human adult cortex. Our approach provides a promising avenue for exploring how complex interactions among genes may contribute to neurobiological phenomena across the human lifespan.
| S-EPMC3631628 | biostudies-literature
Project description:MotivationMethods to model dynamic changes in gene expression at a genome-wide level are not currently sufficient for large (temporally rich or single-cell) datasets. Variational autoencoders offer means to characterize large datasets and have been used effectively to characterize features of single-cell datasets. Here we extend these methods for use with gene expression time series data.ResultsWe present RVAgene: a recurrent variational autoencoder to model gene expression dynamics. RVAgene learns to accurately and efficiently reconstruct temporal gene profiles. It also learns a low dimensional representation of the data via a recurrent encoder network that can be used for biological feature discovery, and from which we can generate new gene expression data by sampling the latent space. We test RVAgene on simulated and real biological datasets, including embryonic stem cell differentiation and kidney injury response dynamics. In all cases, RVAgene accurately reconstructed complex gene expression temporal profiles. Via cross validation, we show that a low-error latent space representation can be learnt using only a fraction of the data. Through clustering and gene ontology term enrichment analysis on the latent space, we demonstrate the potential of RVAgene for unsupervised discovery. In particular, RVAgene identifies new programs of shared gene regulation of Lox family genes in response to kidney injury.AvailabilityRVAgene is available in Python, at gihub: https://github.com/maclean-lab/RVAgene; Zenodo archive: http://doi.org/10.5281/zenodo.4271097.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8504625 | biostudies-literature
Project description:MotivationModeling of Transcription Factor (TF) binding from both ChIP-seq and chromatin accessibility data has become prevalent in computational biology. Several models have been proposed to generate new hypotheses on transcriptional regulation. However, there is no distinct approach to derive TF binding scores from ChIP-seq and open chromatin experiments. Here, we review biases of various scoring approaches and their effects on the interpretation and reliability of predictive gene expression models.ResultsWe generated predictive models for gene expression using ChIP-seq and DNase1-seq data from DEEP and ENCODE. Via randomization experiments, we identified confounders in TF gene scores derived from both ChIP-seq and DNase1-seq data. We reviewed correction approaches for both data types, which reduced the influence of identified confounders without harm to model performance. Also, our analyses highlighted further quality control measures, in addition to model performance, that may help to assure model reliability and to avoid misinterpretation in future studies.Availability and implementationThe software used in this study is available online at https://github.com/SchulzLab/TEPIC.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6530814 | biostudies-literature
Project description:BACKGROUND:Actinomyces oris is a Gram-positive bacterium that has been associated with healthy and diseased sites in the human oral cavity. Most pathogenic bacteria require iron to survive, and in order to acquire iron in the relatively iron-scarce oral cavity A. oris has been shown to produce iron-binding molecules known as siderophores. The genes encoding these siderophores and transporters are thought to be regulated by the amount of iron in the growth medium and by the metal-dependent repressor, AmdR, which we showed previously binds to the promoter of proposed iron-regulated genes. OBJECTIVE:The purpose of this study was to characterize siderophore and associated iron transport systems in A. oris. DESIGN:We examined gene expression of the putative iron transport genes fetA and sidD in response to low- and high-iron environments. One of these genes, sidD, encoding a putative Fe ABC transporter protein, was insertionally inactivated and was examined for causing growth defects. To gain a further understanding of the role of iron metabolism in oral diseases, clinical isolates of Actinomyces spp. were examined for the presence of the gene encoding AmdR, a proposed global regulator of iron-dependent gene expression in A. oris. RESULTS:When A. oris was grown under iron-limiting conditions, the genes encoding iron/siderophore transporters fetA and sidD showed increased expression. One of these genes (sidD) was mutated, and the sidD::Km strain exhibited a 50% reduction in growth in late log and stationary phase cells in media that contained iron. This growth defect was restored when the sidD gene was provided in a complemented strain. We were able to isolate the AmdR-encoding gene in seven clinical isolates of Actinomyces. When these protein sequences were aligned to the laboratory strain, there was a high degree of sequence similarity. CONCLUSIONS:The growth of the sidD::Km mutant in iron-replete medium mirrored the growth of the wild-type strain grown in iron-limiting medium, suggesting that the sidD::Km mutant was compromised in iron uptake. The known iron regulator AmdR is well conserved in clinical isolates of A. oris. This work provides additional insight into iron metabolism in this important oral microbe.
| S-EPMC4684579 | biostudies-literature
Project description:The Xenopus community has made concerted efforts over the last 10-12 years systematically to improve the available sequence information for this amphibian model organism ideally suited to the study of early development in vertebrates. Here I review progress in the collection of both sequence data and physical clone reagents for protein coding genes. I conclude that we have cDNA sequences for around 50% and full-length clones for about 35% of the genes in Xenopus tropicalis, and similar numbers but a smaller proportion for Xenopus laevis. In addition, I demonstrate that the gaps in the current genome assembly create problems for the computational elucidation of gene sequences, and suggest some ways to ameliorate the effects of this.
| S-EPMC3488295 | biostudies-literature