scholarly journals DRUID: A pipeline for reproducible transcriptome-wide measurements of mRNA stability

2017 ◽  
Author(s):  
Andrew Lugowski ◽  
Beth Nicholson ◽  
Olivia S. Rissland
Keyword(s):  
Mrna Stability ◽  
Messenger Rna ◽  
Time Course ◽  
High Throughput Sequencing ◽  
Computational Algorithms ◽  
Metabolic Labeling ◽  
Computational Pipeline ◽  
Time Course Data ◽  
Labeled Rna

SUMMARYControl of messenger RNA (mRNA) stability is an important aspect of gene regulation. The gold standard for measuring mRNA stability transcriptome-wide uses metabolic labeling, biochemical isolation of labeled RNA populations, and high-throughput sequencing. However, difficult normalization procedures and complex computational algorithms have inhibited widespread adoption of this approach. Here, we present DRUID (for Determination of Rates Using Intron Dynamics), a computational pipeline that is robust, easy to use, and freely available. Our pipeline uses endogenous introns to normalize time course data and yields more reproducible half-lives than other methods, even with datasets that were otherwise unusable. DRUID can handle datasets from a variety of organisms, spanning yeast to humans, and we even applied it retroactively on published datasets. We anticipate that DRUID will allow broad application of metabolic labeling for studies of mRNA stability.

scholarly journals Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays

2015 ◽  
Vol 112 (42) ◽  
pp. 13115-13120 ◽  
Author(s):  
Antti Honkela ◽  
Jaakko Peltonen ◽  
Hande Topa ◽  
Iryna Charapitsa ◽  
Filomena Matarese ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Transcriptional Activation ◽  
Time Course ◽  
Degradation Rate ◽  
High Throughput Sequencing ◽  
Intron Retention ◽  
Activation Kinetics ◽  
Genome Wide ◽  
Time Course Data ◽  
Production Delay

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.

scholarly journals Uses and misuses of progress curve analysis in enzyme kinetics

2008 ◽  
Vol 3 (4) ◽  
pp. 345-350 ◽  
Author(s):  
Natalia Nikolova ◽  
Kiril Tenekedjiev ◽  
Krasimir Kolev
Keyword(s):  
Kinetic Parameters ◽  
Time Course ◽  
Curve Analysis ◽  
Progress Curve ◽  
Time Course Data ◽  
Mathematical Evaluation ◽  
Enzymatic Mechanisms ◽  
Single Reaction

AbstractProgress curve analysis is a convenient tool for the characterization of enzyme action: a single reaction mixture provides multiple experimental measured points for continuously varying amounts of substrates and products with exactly the same enzyme and modulator concentrations. The determination of kinetic parameters from the progress curves, however, requires complex mathematical evaluation of the time-course data. Some freely available programs (e.g. FITSIM, DYNAFIT) are widely applied to fit kinetic parameters to user-defined enzymatic mechanisms, but users often overlook the stringent requirements of the analytic procedures for appropriate design of the input experiments. Flaws in the experimental setup result in unreliable parameters with consequent misinterpretation of the biological phenomenon under study. The present commentary suggests some helpful mathematical tools to improve the analytic procedure in order to diagnose major errors in concept and design of kinetic experiments.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

scholarly journals Maternal provision of non-sex-specific transformer messenger RNA in sex determination of the wasp Asobara tabida

2017 ◽  
Vol 27 (1) ◽  
pp. 99-109 ◽  
Author(s):  
E. Geuverink ◽  
E. C. Verhulst ◽  
M. van Leussen ◽  
L. van de Zande ◽  
L. W. Beukeboom
Keyword(s):  
Sex Determination ◽  
Messenger Rna ◽  
Asobara Tabida

Exosite-dependent regulation of factor VIIIa by activated protein C

Blood ◽  
2003 ◽  
Vol 101 (12) ◽  
pp. 4802-4807 ◽  
Author(s):  
Chandrashekhara Manithody ◽  
Philip J. Fay ◽  
Alireza R. Rezaie
Keyword(s):  
Protein C ◽  
Time Course ◽  
Polyacrylamide Gel Electrophoresis ◽  
Sodium Dodecyl ◽  
Limited Proteolysis ◽  
Activated Protein C ◽  
Coagulation Cascade ◽  
Factor Va ◽  
Factor Viiia

AbstractActivated protein C (APC) is a natural anticoagulant serine protease in plasma that down-regulates the coagulation cascade by degrading cofactors Va and VIIIa by limited proteolysis. Recent results have indicated that basic residues of 2 surface loops known as the 39-loop (Lys37-Lys39) and the Ca2+-binding 70-80–loop (Arg74 and Arg75) are critical for the anticoagulant function of APC. Kinetics of factor Va degradation by APC mutants in purified systems have demonstrated that basic residues of these loops are involved in determination of the cleavage specificity of the Arg506 scissile bond on the A2 domain of factor Va. In this study, we characterized the properties of the same exosite mutants of APC with respect to their ability to interact with factor VIIIa. Time course of the factor VIIIa degradation by APC mutants suggested that the same basic residues of APC are also critical for recognition and degradation of factor VIIIa. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) of the factor VIIIa cleavage reactions revealed that these residues are involved in determination of the specificity of both A1 and A2 subunits in factor VIIIa, thus facilitating the cleavages of both Arg336 and Arg562 scissile bonds in the cofactor.

scholarly journals Stoichiometric Relationship between Na+ Ions Transported and Glucose Consumed in Human Erythrocytes: Bayesian Analysis of 23Na and 13C NMR Time Course Data

2013 ◽  
Vol 104 (8) ◽  
pp. 1676-1684 ◽  
Author(s):  
Max Puckeridge ◽  
Bogdan E. Chapman ◽  
Arthur D. Conigrave ◽  
Stuart M. Grieve ◽  
Gemma A. Figtree ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
13C Nmr ◽  
Time Course ◽  
Human Erythrocytes ◽  
Na Ions ◽  
Time Course Data
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