scholarly journals Inference of gene regulatory networks and compound mode of action from time course gene expression profiles

2006 ◽  
Vol 22 (7) ◽  
pp. 815-822 ◽  
Author(s):  
M. Bansal ◽  
G. D. Gatta ◽  
D. di Bernardo
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Mode Of Action ◽  
Regulatory Networks ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Regulatory

scholarly journals An Evaluation of Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Component

2018 ◽  
Author(s):  
Sergii Babichev
Keyword(s):  
Gene Expression ◽  
Information Technology ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Noise Component ◽  
Gene Regulatory ◽  
Processing Stability ◽  
Different Levels

The paper presents the results of the research concerning an evaluation of information 1 technology of gene expression profiles processing stability with the use of gene expression profiles 2 with different levels of noise component. The information technology is presented as a structural 3 block-chart, which contains all stages of the studied data processing. The hybrid model of objective 4 clustering based on SOTA algorithm and the technology of gene regulatory networks reconstruction 5 have been studied to evaluate the stability to the level of the noise component. The results of the 6 simulation have shown that the hybrid model of objective clustering has high level of stability 7 to noise component and vice versa, the technology of gene regulatory networks reconstruction is 8 very sensitivity to level of noise component. The obtained results indicate the importance of gene 9 expression profiles preprocessing at early stage of gene regulatory network reconstruction in order to 10 remove background noise and non-informative genes in terms of used criteria

scholarly journals An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 48 ◽  
Author(s):  
Sergii Babichev
Keyword(s):  
Gene Expression ◽  
Information Technology ◽  
Hybrid Model ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Regulatory ◽  
Processing Stability ◽  
Different Levels

This paper presents the results of research concerning the evaluation of stability of information technology of gene expression profiles processing with the use of gene expression profiles, which contain different levels of noise components. The information technology is presented as a structural block-chart, which contains all stages of the studied data processing. The hybrid model of objective clustering based on the SOTA algorithm and the technology of gene regulatory networks reconstruction have been investigated to evaluate the stability to the level of the noise components. The results of the simulation have shown that the hybrid model of the objective clustering has high level of stability to noise components and vice versa, the technology of gene regulatory networks reconstruction is rather sensitive to the level of noise component. The obtained results indicate the importance of gene expression profiles preprocessing at the early stage of the gene regulatory network reconstruction in order to remove background noise and non-informative genes in terms of the used criteria.

scholarly journals TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

2018 ◽  
Author(s):  
Maria Angels de Luis Balaguer ◽  
Ryan J. Spurney ◽  
Natalie M. Clark ◽  
Adam P. Fisher ◽  
Rosangela Sozzani
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Course ◽  
Expression Profiles ◽  
Expression Data ◽  
Sequencing Data ◽  
Simple Interface ◽  
Gene Regulatory

ABSTRACTPredicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.SUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

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