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

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

Libi Hertzberg, Assif Yitzhaky, Metsada Pasmanik-Chor
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJKDB.2018010107
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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.
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Advanced high-throughput technologies have been developed and extensively used in the last decades (microarrays since the mid-1980s (Schena, Shalon, Davis, & Brown, 1995) and Next Generation Sequencing (NGS) (Goodwin, McPherson, & McCombie, 2016) since the mid-2000s). Mass spectrometry technologies were originally invented almost 100 years ago and further developed during the 1990s (Glish & Vachet, 2003). All these technologies and others are being routinely used by Bio-medical (Bio-Med) scientists for production of high-throughput data. However, currently, it is very difficult for Bio-Med researchers with no computational skills to analyze their own data. Various free, online and user-friendly tools have been developed and are routinely used for gene expression and proteomics data analysis. For example, Expander (Ulitsky et al., 2010), Chipster (Kallio et al., 2011), SAM (Tusher, Tibshirani, & Chu, 2001), Limma (Ritchie et al., 2015), DESeq (Love, Huber, & Anders, 2014), Morpheus ( It includes a convenient “help” menu and an example test data (Teuffel et al., 2004).

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