GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
User Interface ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
Batch Correction ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

scholarly journals GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

2018 ◽  
Vol 8 (1) ◽  
pp. 94-105
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Graphical User Interface ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Expression Data ◽  
High Throughput Data ◽  
Batch Correction ◽  
Different Types ◽  
User Friendly

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

Extracting Gradual Rules to reveal regulation between genes

2020 ◽  
Vol 15 ◽  
Author(s):  
Manel Gouider ◽  
Ines Hamdi ◽  
Henda Ben Ghezala
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
High Throughput ◽  
Explicit Knowledge ◽  
Biological Data ◽  
Genetic Interactions ◽  
Human Lung Cancer ◽  
Biological Knowledge ◽  
Good Representation ◽  
Expression Data

Background: The gene regulation represents a very complex mechanism produced in the cell in order to increase or decrease the gene expression. This regulation of genes forms a Gene regulatory Network GRN composed of a collection of genes and products of genes in interaction. The high throughput technologies that generate a huge volume of gene expression data are useful for analyzing the GRN. The biologists are interested in the relevant genetic knowledge hidden in these data sources. Although, the knowledge extracted by the different data mining approaches of the literature are insufficient for inferring the GRN topology or do not give a good representation of the real genetic regulation in the cell. Objective: In this work, we are interested in the extraction of genetic interactions from the high throughput technologies, such as the microarrays or DNA chips. Methods: In this paper, in order to extract expressive and explicit knowledge about the interactions between genes, we use the method of gradual patterns and rules extraction applied on numerical data that extracts the frequent co-variations between gene expression values. Furthermore, we choose to integrate experimental biological data and biological knowledge in the process of knowledge extraction of genetic interactions. Results: The validation results on real gene expression data of the model plant Arabidopsis and human lung cancer shows the performance of this approach. Conclusion: The extracted gradual rules express the genetic interactions composed a GRN, these rules help to understand complex systems and cellular functions.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Fast and Efficient 5′P Degradome Library Preparation for Analysis of Co-Translational Decay in Arabidopsis

Plants ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 466
Author(s):  
Marie-Christine Carpentier ◽  
Cécile Bousquet-Antonelli ◽  
Rémy Merret
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
Transcriptional Regulation ◽  
High Throughput ◽  
Library Preparation ◽  
Working Day ◽  
Set Up ◽  
Post Transcriptional Regulation ◽  
Commercial Kit

The recent development of high-throughput technologies based on RNA sequencing has allowed a better description of the role of post-transcriptional regulation in gene expression. In particular, the development of degradome approaches based on the capture of 5′monophosphate decay intermediates allows the discovery of a new decay pathway called co-translational mRNA decay. Thanks to these approaches, ribosome dynamics could now be revealed by analysis of 5′P reads accumulation. However, library preparation could be difficult to set-up for non-specialists. Here, we present a fast and efficient 5′P degradome library preparation for Arabidopsis samples. Our protocol was designed without commercial kit and gel purification and can be easily done in one working day. We demonstrated the robustness and the reproducibility of our protocol. Finally, we present the bioinformatic reads-outs necessary to assess library quality control.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa

2015 ◽  
Vol 11 (11) ◽  
pp. 3137-3148
Author(s):  
Nazanin Hosseinkhan ◽  
Peyman Zarrineh ◽  
Hassan Rokni-Zadeh ◽  
Mohammad Reza Ashouri ◽  
Ali Masoudi-Nejad
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Systems Biology ◽  
Gene Expression Data ◽  
High Throughput ◽  
Expression Data ◽  
P Gene ◽  
Stress Related Genes ◽  
High Throughput Gene Expression

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data.

scholarly journals Bioinformatics in otolaryngology research. Part one: concepts in DNA sequencing and gene expression analysis

2014 ◽  
Vol 128 (10) ◽  
pp. 848-858 ◽  
Author(s):  
T J Ow ◽  
K Upadhyay ◽  
T J Belbin ◽  
M B Prystowsky ◽  
H Ostrer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Storage ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Expression Analysis ◽  
Biological Data ◽  
Biological Research ◽  
Nucleotide Sequencing ◽  
New Era ◽  
Crucial Component

AbstractBackground:Advances in high-throughput molecular biology, genomics and epigenetics, coupled with exponential increases in computing power and data storage, have led to a new era in biological research and information. Bioinformatics, the discipline devoted to storing, analysing and interpreting large volumes of biological data, has become a crucial component of modern biomedical research. Research in otolaryngology has evolved along with these advances.Objectives:This review highlights several modern high-throughput research methods, and focuses on the bioinformatics principles necessary to carry out such studies. Several examples from recent literature pertinent to otolaryngology are provided. The review is divided into two parts; this first part discusses the bioinformatics approaches applied in nucleotide sequencing and gene expression analysis.Conclusion:This paper demonstrates how high-throughput nucleotide sequencing and transcriptomics are changing biology and medicine, and describes how these changes are affecting otorhinolaryngology. Sound bioinformatics approaches are required to obtain useful information from the vast new sources of data.

Transcriptomics Analysis using Galaxy and other Online Servers for Rheumatoid Arthritis

2021 ◽  
Vol 9 (VII) ◽  
pp. 459-466
Author(s):  
Sourabh Parmar
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Mining ◽  
Gene Ontology ◽  
Complex Disease ◽  
Differential Expression Analysis ◽  
Biological Data ◽  
Systemic Autoimmune Disease ◽  
Transcriptomic Data ◽  
Pathway Enrichment ◽  
User Friendly

Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.

scholarly journals Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data

2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
High Throughput ◽  
System Level ◽  
Expression Data ◽  
Differential Analysis ◽  
Computational Systems Biology ◽  
Microarray Gene Expression ◽  
High Throughput Data ◽  
Computational Systems

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.

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