scholarly journals Approximate inference of gene regulatory network models from RNA-Seq time series data

2017 ◽  
Author(s):  
Thomas Thorne
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Stem Cell Differentiation ◽  
Series Data ◽  
Model Parameters ◽  
Rna Seq ◽  
Sparse Regression ◽  
Gene Regulatory

AbstractInference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for the observations, and uses sparse regression with a horseshoe prior to learn a dynamic Bayesian network of interactions between genes. We use a variational inference scheme to learn approximate posterior distributions for the model parameters. The methodology is benchmarked on synthetic data designed to replicate the distribution of real world RNA-Seq data. We compare our method to other sparse regression approaches and information theoretic methods. We demonstrate an application of our method to a publicly available human neuronal stem cell differentiation RNA-Seq time series.

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

scholarly journals PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks from Time Series Data

2020 ◽  
Author(s):  
Yukun Tan ◽  
Fernando B. Lima Neto ◽  
Ulisses Braga Neto
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Series Data ◽  
Particle Swarm Algorithm ◽  
Penalized Maximum Likelihood ◽  
Particle Swarms ◽  
Gene Regulatory

AbstractWe present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to large networks under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs efficiently and accurately. PALLAS is a fully-fledged program with a commandline user interface, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

scholarly journals Identification of transcription factors regulating senescence in wheat through gene regulatory network modelling

2018 ◽  
Author(s):  
Philippa Borrill ◽  
Sophie A. Harrington ◽  
James Simmonds ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Nutrient Content ◽  
Series Data ◽  
Network Modelling ◽  
Polyploid Wheat ◽  
Gene Regulatory ◽  
Developmental Programme

AbstractSenescence is a tightly regulated developmental programme which is coordinated by transcription factors. Identifying these transcription factors in crops will provide opportunities to tailor the senescence process to different environmental conditions and regulate the balance between yield and grain nutrient content. Here we use ten time points of gene expression data alongside gene network modelling to identify transcription factors regulating senescence in polyploid wheat. We observe two main phases of transcription changes during senescence: early downregulation of housekeeping and metabolic processes followed by upregulation of transport and hormone related genes. We have identified transcription factor families associated with these early and later waves of differential expression. Using gene regulatory network modelling alongside complementary publicly available datasets we identified candidate transcription factors for controlling senescence. We validated the function of one of these candidate transcription factors in senescence using wheat chemically-induced mutants. This study lays the ground work to understand the transcription factors which regulate senescence in polyploid wheat and exemplifies the integration of time-series data with publicly available expression atlases and networks to identify candidate regulatory genes.

scholarly journals HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model

2018 ◽  
Vol 19 (10) ◽  
pp. 3178 ◽  
Author(s):  
Bin Yang ◽  
Yuehui Chen ◽  
Wei Zhang ◽  
Jiaguo Lv ◽  
Wenzheng Bao ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Repair System ◽  
Tree Model ◽  
Scoring Method ◽  
Maximum Information ◽  
Gene Regulatory ◽  
Complex Valued

Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in E. coli and Saccharomyces cerevisiae are utilized to evaluate the performance of the HSCVFNT algorithm. As a result, HSCVFNT obtains outstanding F-scores of 0.923, 0.8 and 0.625 for SOS network and (In vivo Reverse-Engineering and Modeling Assessment) IRMA network inference, respectively, which are 5.5%, 14.3% and 72.2% higher than the best performance of other state-of-the-art GRN inference methods and time-delayed methods.

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