A novel approach GRNTSTE to reconstruct gene regulatory interactions applied to a case study for rat pineal rhythm gene

Abstract

Accurate inference and prediction of gene regulatory network are very important for understanding dynamic cellular processes. The large-scale time series genomics data are helpful to reveal the molecular dynamics and dynamic biological processes of complex biological systems. Firstly, we collected the time series data of the rat pineal gland tissue in the natural state according to a fixed sampling rate, and performed whole-genome sequencing. The large-scale time-series sequencing data set of rat pineal gland was constructed, which includes 480 time points, the time interval between adjacent time points is 3 min, and the sampling period is 24 h. Then, we proposed a new method of constructing gene expression regulatory network, named the gene regulatory network based on time series data and entropy transfer (GRNTSTE) method. The method is based on transfer entropy and large-scale time-series gene expression data to infer the causal regulatory relationship between genes in a data-driven mode. The comparative experiments prove that GRNTSTE has better performance than dynamical gene network inference with ensemble of trees (dynGENIE3) and SCRIBE, and has similar performance to TENET. Meanwhile, we proved that the performance of GRNTSTE is slightly lower than that of SINCERITIES method and better than other gene regulatory network construction methods in BEELINE framework, which is based on the BEELINE data set. Finally, the rat pineal rhythm gene expression regulatory network was constructed by us based on the GRNTSTE method, which provides an important reference for the study of the pineal rhythm mechanism, and is of great significance to the study of the pineal rhythm mechanism.

Inferring Circadian Gene Regulatory Relationships from Gene Expression Data with a Hybrid Framework

“…There are seven main steps for the experiment: (1) Take the rat to the test bench; (2) Euthanize the rat by decapitation; (3) Open the skull and take out the brain tissues; (4) Isolate the rhythm centre-pineal gland; (5) Identify second microstructure; (6) Remove rat pineal gland and put it in a 2 ml Corning Freezer Tube; (7) Label the sample and cryopreserved in liquid nitrogen immediately. More experimental details can be found in [19,20].…”

Subcellular spatially resolved gene neighborhood networks in single cells

“… 17 Gene regulatory networks (GRNs) have been widely used to describe the regulation of gene expression, and a wide range of tools have been developed to discover the regulatory relationship between genes ( Table S1 ). 18 , 19 , 20 , 21 GRNs provide intuitive visualization by representing genes as nodes in graphs, and regulatory relationships as edges. Coexpression networks reveal functional and regulatory commonalities between genes.…”

“… 18 Statistical inference models have also been proposed to infer regulatory networks from single-cell transcriptomic profiles. 20 , 21 Spatial transcriptomics data has also been used to infer gene regulatory relationships. 26 , 27 These computational approaches utilize existing transcriptomics data to predict regulatory relationships.…”

Subcellular spatially resolved gene neighborhood networks in single cells

“…33 Gene regulatory networks (GRNs) have been widely used to describe the regulation of gene expression, and a wide range of tools have been developed to discover the regulatory relationship between genes. [34][35][36][37] GRNs provide intuitive visualization by representing genes as nodes in graphs, and regulatory relationships as edges. Co-expression networks reveal functional and regulatory commonalities between genes.…”

“…34 Statistical inference models have also been proposed to infer regulatory networks from single-cell transcriptomic profiles. 36, 37 Spatial transcriptomics data has also been used to infer gene regulatory relationships. 42, 43 These computational approaches utilize existing transcriptomics data to predict regulatory relationships.…”