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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Mar 24, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

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

Shani Ben-Ari Fuchs¹, Iris Lieder¹, Gil Stelzer^1,2

¹1 LifeMap Sciences Ltd. , Tel Aviv, Israel .

Omics : a Journal of Integrative Biology

|March 17, 2016

Summary

This summary is machine-generated.

GeneAnalytics provides a user-friendly tool for contextualizing large-scale omics data, crucial for advancing precision medicine. It integrates gene, disease, and pathway databases to translate big data into actionable knowledge for innovation.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Postgenomics data, including Next Generation Sequencing (NGS) and RNAseq, are generated in large volumes for precision medicine.
A key challenge is understanding the biological and clinical contexts of this big data to drive innovation.
Gene set enrichment tools are emerging as solutions to bridge this knowledge gap.

Purpose of the Study:

To introduce GeneAnalytics, a comprehensive and user-friendly gene set analysis tool.
To enable rapid contextualization of expression patterns and functional signatures in postgenomics data.
To facilitate the translation of big data into knowledge for innovation in life sciences and clinical applications.

Main Methods:

GeneAnalytics utilizes proprietary unified data from the LifeMap Science GeneCards suite (GeneCards, MalaCards, PathCards).
It incorporates expression-based analysis using the LifeMap Discovery database for gene-tissue association.
The tool features evidence-based scoring algorithms and an intuitive user interface.

Main Results:

GeneAnalytics provides rapid contextualization of expression patterns and functional signatures.
It enables advanced gene-tissue association for evaluating differentiation protocols and discovering biomarkers.
Results are directly linked to comprehensive gene, disease, and cell information within the GeneCards suite.

Conclusions:

GeneAnalytics is a broadly applicable tool for postgenomics data analysis and interpretation.
It supports the translation of big data into knowledge-based innovation in fields like precision medicine, ecogenomics, and pharmacogenomics.
Future developments will enhance its algorithms and data visualization capabilities.