Stochastic gene expression
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Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction.
12p vialfrednobel 29-01-2022 11 0 Download
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Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other’s expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs.
14p vialfrednobel 29-01-2022 9 0 Download
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Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing allows the comparison of expression distribution between the two alleles of a diploid organism and the characterization of allele-specific bursting.
15p vialfrednobel 29-01-2022 12 0 Download
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Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features.
13p viaristotle 29-01-2022 13 0 Download
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Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, the analysis of time series scRNA-seq data could be compromised by 1) distortion created by assorted sources of data collection and generation across time samples and 2) inheritance of cell-to-cell variations by stochastic dynamic patterns of gene expression.
16p visilicon2711 20-08-2021 8 1 Download
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Adjusting the capacity of metabolic pathways in response to rapidly changing environmental conditions is an important component of microbial adaptation strategies to stochastic environments.
13p vikentucky2711 24-11-2020 10 2 Download
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The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.
13p vioklahoma2711 19-11-2020 18 1 Download
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SgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA).
12p viflorida2711 30-10-2020 12 2 Download
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The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis.
8p viconnecticut2711 29-10-2020 17 1 Download
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Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression.
12p vicoachella2711 27-10-2020 16 2 Download
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Large mega base-pair genomic regions show robust alterations in DNA methylation levels in multiple cancers. A vast majority of these regions are hypomethylated in cancers. These regions are generally enriched for CpG islands, Lamin Associated Domains and Large organized chromatin lysine modification domains, and are associated with stochastic variability in gene expression.
10p vinaypyidaw2711 26-08-2020 15 2 Download
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Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2011, Article ID 572876, 5 pages doi:10.1155/2011/572876 Research Article Inference of Kinetic Parameters of Delayed Stochastic Models of Gene Expression Using a Markov Chain Approximation Henrik Mannerstrom,1 Olli Yli-Harja,1, 2 and Andre S. Ribeiro1 1 Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology, P.O.
6p dauphong13 10-02-2012 48 5 Download