GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of...

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Published inOmics (Larchmont, N.Y.) Vol. 20; no. 3; p. 139
Main Authors Ben-Ari Fuchs, Shani, Lieder, Iris, Stelzer, Gil, Mazor, Yaron, Buzhor, Ella, Kaplan, Sergey, Bogoch, Yoel, Plaschkes, Inbar, Shitrit, Alina, Rappaport, Noa, Kohn, Asher, Edgar, Ron, Shenhav, Liraz, Safran, Marilyn, Lancet, Doron, Guan-Golan, Yaron, Warshawsky, David, Shtrichman, Ronit
Format Journal Article
LanguageEnglish
Published United States 01.03.2016
Subjects
Online AccessGet full text
ISSN1557-8100
1536-2310
1557-8100
DOI10.1089/omi.2015.0168

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Abstract Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.
AbstractList Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.
Author Shenhav, Liraz
Shitrit, Alina
Safran, Marilyn
Edgar, Ron
Mazor, Yaron
Bogoch, Yoel
Lieder, Iris
Rappaport, Noa
Ben-Ari Fuchs, Shani
Stelzer, Gil
Guan-Golan, Yaron
Plaschkes, Inbar
Lancet, Doron
Buzhor, Ella
Kohn, Asher
Warshawsky, David
Kaplan, Sergey
Shtrichman, Ronit
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  organization: 7 Bonus BioGroup Ltd., Haifa, Israel
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26983021$$D View this record in MEDLINE/PubMed
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Snippet Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine....
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pubmed
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StartPage 139
SubjectTerms Algorithms
Computational Biology - methods
Data Mining
Databases, Factual
Databases, Genetic
Gene Regulatory Networks
Genome, Human
High-Throughput Nucleotide Sequencing - statistics & numerical data
Humans
Metabolic Networks and Pathways - genetics
Microarray Analysis - statistics & numerical data
Software
Title GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data
URI https://www.ncbi.nlm.nih.gov/pubmed/26983021
https://www.liebertpub.com/doi/pdf/10.1089/omi.2015.0168
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