scholarly journals Prediction Interval Ranking Score: Identification of Invariant Expression from Time Series

2018 ◽  
Author(s):  
Alexander M Crowell ◽  
Jennifer J. Loros ◽  
Jay C Dunlap
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
High Throughput ◽  
Reference Genes ◽  
Time Series Data ◽  
Prediction Interval ◽  
Series Data ◽  
Throughput Time ◽  
Additional Information ◽  
Ranking Score

AbstractMotivationIdentification of constitutive reference genes is critical for analysis of gene expression. Large numbers of high throughput time series expression data are available, but current methods for identifying invariant expression are not tailored for time series. Identification of reference genes from these data sets can benefit from methods which incorporate the additional information they provide.ResultsHere we show that we can improve identification of invariant expression from time series by modelling the time component of the data. We implement the Prediction Interval Ranking Score (PIRS) software, which screens high throughput time series data and provides a ranked list of reference candidates. We expect that PIRS will improve the quality of gene expression analysis by allowing researchers to identify the best reference genes for their system from publicly available time series.AvailabilityPIRS can be downloaded and installed with dependencies using ‘pip install pirs’ and Python code and documentation is available for download at https://github.com/aleccrowell/[email protected]

scholarly journals A Novel Method for Analyzing [Ca2+] Flux Kinetics in High-Throughput Screening

2006 ◽  
Vol 11 (5) ◽  
pp. 511-518 ◽  
Author(s):  
Philip Gribbon ◽  
Chris Chambers ◽  
Kaupo Palo ◽  
Juergen Kupper ◽  
Juergen Mueller ◽  
...  
Keyword(s):  
Time Series ◽  
High Throughput ◽  
High Throughput Screening ◽  
Time Series Data ◽  
Calcium Flux ◽  
Series Data ◽  
Imaging Plate ◽  
Additional Information ◽  
Novel Method ◽  
G Protein Coupled

Driven by multiparameter fluorescence readouts and the analysis of kinetic responses from biological assay systems, the amount and complexity of high-throughput screening data are constantly increasing. As a consequence, the reduction of data to a simple number, reflecting a percentage activity/inhibition, is no longer an adequate approach because valuable additional information, for example, about compound-or process-induced artifacts, is lost. Time series data such as the transient calcium flux observed after activation of Gq-coupled G protein-coupled receptors (GPCRs), are especially challenging with respect to quantity of data; typically, responses are followed for several minutes. Based on measurements taken on the fluorometric imaging plate reader, the authors have introduced a mathematical model to describe the time traces of cellular calcium fluxes mediated by the activation of GPCRs. The model describes the time series using 13 parameters, reducing the amount of data by 90% while guiding the detection of compound-induced artifacts as well as the selection of compounds for further characterization.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

2017 ◽  
Author(s):  
Anthony Szedlak ◽  
Spencer Sims ◽  
Nicholas Smith ◽  
Giovanni Paternostro ◽  
Carlo Piermarocchi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Cell Cycle ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

Inference of gene regulatory networks based on nonlinear ordinary differential equations

2020 ◽  
Vol 36 (19) ◽  
pp. 4885-4893 ◽  
Author(s):  
Baoshan Ma ◽  
Mingkun Fang ◽  
Xiangtian Jiao
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data ◽  
State Data ◽  
Gene Regulatory

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data

2008 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Zidong Wang* ◽  
Fuwen Yang ◽  
Daniel W. C. Ho ◽  
Stephen Swift ◽  
Allan Tucker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Dynamic Modeling ◽  
Time Series Data ◽  
Series Data ◽  
Stochastic Dynamic ◽  
Gene Expression Time Series ◽  
Expression Time
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