Discovering modulators of gene expression.
Ontology highlight
ABSTRACT:
SUBMITTER: Babur O
PROVIDER: S-EPMC2943625 | biostudies-literature | 2010 Sep
REPOSITORIES: biostudies-literature
Publications
Discovering modulators of gene expression.
Babur Ozgün O Demir Emek E Gönen Mithat M Sander Chris C Dogrusoz Ugur U
Nucleic acids research 20100513 17
Proteins that modulate the activity of transcription factors, often called modulators, play a critical role in creating tissue- and context-specific gene expression responses to the signals cells receive. GEM (Gene Expression Modulation) is a probabilistic framework that predicts modulators, their affected targets and mode of action by combining gene expression profiles, protein-protein interactions and transcription factor-target relationships. Using GEM, we correctly predicted a significant nu ...[more]
Similar Datasets
Project description:High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/.
| S-EPMC2896193 | biostudies-literature
Project description:Venoms are a diverse and complex group of natural toxins that have been adapted to treat many types of human disease, but rigorous computational approaches for discovering new therapeutic activities are scarce. We have designed and validated a new platform-named VenomSeq-to systematically identify putative associations between venoms and drugs/diseases via high-throughput transcriptomics and perturbational differential gene expression analysis. In this study, we describe the architecture of VenomSeq and its evaluation using the crude venoms from 25 diverse animal species and 9 purified teretoxin peptides. By integrating comparisons to public repositories of differential expression, associations between regulatory networks and disease, and existing knowledge of venom activity, we provide a number of new therapeutic hypotheses linking venoms to human diseases supported by multiple layers of preliminary evidence.
| S-EPMC10467105 | biostudies-literature
Project description:Every protein progresses through a natural lifecycle from birth to maturation to death; this process is coordinated by the protein homeostasis system. Environmental or physiological conditions trigger pathways that maintain the homeostasis of the proteome. An open question is how these pathways are modulated to respond to the many stresses that an organism encounters during its lifetime. To address this question, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in Caulobacter crescentus. We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. This pipeline uses a combination of general linear models (GLMs), statistical knockoffs, and a nonparametric Bayesian statistical model to identify essential genetic network components that are shared across environmental perturbations. This analysis allows us to quantify the similarity of proteotoxic environmental perturbations from the perspective of the fitness landscape. We find that essential genes vary more by genetic background than by environmental conditions, with limited overlap among mutant strains targeting different facets of the protein homeostasis system. We also identified 146 unique fitness determinants across different strains, with 19 genes common to at least two strains, showing varying resilience to proteotoxic stresses. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect.
2024-10-03 | GSE244581 | GEO
Project description:Neutrophils form the most abundant leukocyte subset and are central to many disease processes. Technical challenges in transcriptomic profiling have prohibited genomic approaches to date. Here we map expression quantitative trait loci (eQTL) in peripheral blood CD16+ neutrophils from 101 healthy European adults. We identify cis-eQTL for 3281 neutrophil-expressed genes including many implicated in neutrophil function, with 450 of these not previously observed in myeloid or lymphoid cells. Paired comparison with monocyte eQTL demonstrates nuanced conditioning of genetic regulation of gene expression by cellular context, which relates to cell-type-specific DNA methylation and histone modifications. Neutrophil eQTL are markedly enriched for trait-associated variants particularly autoimmune, allergy and infectious disease. We further demonstrate how eQTL in PADI4 and NOD2 delineate risk variant function in rheumatoid arthritis, leprosy and Crohn's disease. Taken together, these data help advance understanding of the genetics of gene expression, neutrophil biology and immune-related diseases.
| S-EPMC4507005 | biostudies-literature
Project description:OBJECTIVE:The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. RESULTS:The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.
| S-EPMC7038619 | biostudies-literature
Project description:Epitranscriptomics, the study of posttranscriptional chemical moieties placed on RNA, has blossomed in recent years. This is due in part to the emergence of high-throughput detection methods as well as the burst of discoveries showing biological function of select chemical marks. RNA modifications have been shown to affect RNA structure, localization, and functions such as alternative splicing, stabilizing transcripts, nuclear export, cap-dependent and cap-independent translation, microRNA biogenesis and binding, RNA degradation, and immune regulation. As such, the deposition of chemical marks on RNA has the unique capability to spatially and temporally regulate gene expression. The goal of this article is to present the exciting convergence of the epitranscriptomic and virology fields, specifically the deposition and biological impact of N7-methylguanosine, ribose 2'-O-methylation, pseudouridine, inosine, N6-methyladenosine, and 5-methylcytosine epitranscriptomic marks on gene expression of RNA viruses. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.
| S-EPMC7169815 | biostudies-literature
Project description:MotivationIdentifying the target genes regulated by transcription factors (TFs) is the most basic step in understanding gene regulation. Recent advances in high-throughput sequencing technology, together with chromatin immunoprecipitation (ChIP), enable mapping TF binding sites genome wide, but it is not possible to infer function from binding alone. This is especially true in mammalian systems, where regulation often occurs through long-range enhancers in gene-rich neighborhoods, rather than proximal promoters, preventing straightforward assignment of a binding site to a target gene.ResultsWe present EMBER (Expectation Maximization of Binding and Expression pRofiles), a method that integrates high-throughput binding data (e.g. ChIP-chip or ChIP-seq) with gene expression data (e.g. DNA microarray) via an unsupervised machine learning algorithm for inferring the gene targets of sets of TF binding sites. Genes selected are those that match overrepresented expression patterns, which can be used to provide information about multiple TF regulatory modes. We apply the method to genome-wide human breast cancer data and demonstrate that EMBER confirms a role for the TFs estrogen receptor alpha, retinoic acid receptors alpha and gamma in breast cancer development, whereas the conventional approach of assigning regulatory targets based on proximity does not. Additionally, we compare several predicted target genes from EMBER to interactions inferred previously, examine combinatorial effects of TFs on gene regulation and illustrate the ability of EMBER to discover multiple modes of regulation.AvailabilityAll code used for this work is available at http://dinner-group.uchicago.edu/downloads.html.
| S-EPMC3259433 | biostudies-literature
Project description:BACKGROUND: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. RESULTS: In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. CONCLUSION: We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis.
| S-EPMC2386490 | biostudies-other
Project description:Many facets of human behaviour are likely to have developed in part due to evolutionary changes in the regulation of neuropeptide and other brain-related genes. This has allowed species-specific expression patterns and unique epigenetic modulation in response to our environment, regulating response not only at the molecular level, but also contributing to differences in behaviour between individuals. As such, genetic variants or epigenetic changes that may alter neuropeptide gene expression are predicted to play a role in behavioural conditions and psychiatric illness. It is therefore of interest to identify regulatory elements that have the potential to drive differential gene expression. Retrotransposons are mobile genetic elements that are known to be drivers of genomic diversity, with the ability to alter expression of nearby genes. In particular, the SINE-VNTR-Alu (SVA) class of retrotransposons is specific to hominids, and its appearance and expansion across the genome has been associated with the evolution of numerous behavioural traits, presumably through their ability to confer unique regulatory properties at the site of their insertion. We review the evidence for SVAs as regulatory elements, exploring how polymorphic variation within these repetitive sequences can drive allele specific gene expression, which would be associated with changes in behaviour and disease risk through the alteration of molecular pathways that are central to healthy brain function.
| S-EPMC5529292 | biostudies-other
Project description:Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.
| S-EPMC5288723 | biostudies-literature