RNA-seq Analysis

With a few mouse clicks aligned BAM files are imported (including normalization) and the discriminating genes are identified and visualized.


Software solutions for analysis of RNA-seq (rnaseq) data tend to be complex and very specialized. Qlucore Omics Explorer makes the analysis of RNA-seq data as easy and accessible for biologists and bench scientists.

The main application is to work with digital gene expression. The inbuilt workflow for RNA-seq data includes a first step of import of aligned BAM files. The second step is normalization based on the TMM method. After this you can start the analysis of digital gene expression data within minutes. 

All functionality in Qlucore Omics Explorer can be used to provide new and interesting insights also to the RNA-seq based analysis. If you have experience with array data it will all be very familiar.


Key functionality:

  • Import and normalize aligned BAM files
  • Identify discriminating features(genes) with a few mouse clicks
  • Visualize data and do data mining and data exporation
  • Show results in the flexible and easy-to-use heatmap with hierarchical clustering
  • Verify your hypotheses using powerful statistics including ANOVA and different forms of regressions
  • Compare with pathways and other biological information using the integrated and user friendly Gene Set Enrichment analysis (GSEA) Workbench
  • No wait, all analysis, statistics and plots are updated directly
  • Extensive plot library both in 2D and 3D


Easy Data Import

Qlucore Omics Explorer supports many data file formats. Import can be done in several ways, with or without normalization. 

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Case studies

RNA-seq case study

RNA-Seq analysis using Qlucore

Performing gene expression analysis based on RNA sequencing data, in Dilated Cardiomyopathy studies.

Stanford University, US

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Using Qlucore in epigenetics research studies

A range of samples including DNA from patient blood, primary tissue from tumors, and cell lines, are studied.

Cancer Genetics Program at the Hospital for Sick Children (SickKids) in Toronto, Canada.

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Analysis of public data using Qlucore

This case study is an example of how the use of public information from multiple sources was used to propose a new classification for glioma cancer.

Beijing Normal University, China

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Protein data analysis in Hepatitis studies

Understanding viral signatures in Hepatitis C. Qlucore Omics Explorer is used to identify differences in chemokines between the virally infected groups.

Institute Pasteur, France

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