Qlucore

Gene expression

With Qlucore Omics Explorer you can examine and analyze data from gene expression experiments. Easy visualization is built into the tool and configuration is easy and fast for many different plots such as heatmaps and PCA plots.

Variable PCA plot

Qlucore Omics Explorer is a D.I.Y next-generation bioinformatics software for research in life science, biotech, food and plant industries, as well as academia. The powerful visualization-based data analysis tool with inbuilt powerful statistics delivers immediate results and provides instant exploration and visualization of big data.   

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Gene Expression Analysis made easy

With a few mouse clicks the discriminating genes are identified and results are available in publication ready lists and plots.

Data can be generated either by RNA-seq techniques or through microarrays . Direct import and normalization is available for aligned BAM files coming from any NGS platform and for data from ThermoFischer (Affymetrix) and Agilent.

Direct download of data from TCGA and Gene Expression Omnibus is available.

RNA-seq data can be analyzed with the Qlucore Omics Explorer base module. If you add the NGS module many more options are available - it will for instance be possible to analyze data in a synchronized model both doing statistical filtering on expression levels and filter on genomic entities. All this can be done with synchronized plots such as a Genome browser and a heatmap or PCA plot. Gene expression analysis can be combined with Chip-seq data.

Scripting and automation of workflows is possible with the Python based scripts.

Key functionalities:

  • Identify discriminating genes with a few mouse clicks
  • Show results in the flexible and easy-to-use heat map 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
  • Explore new ideas focusing on gene and sample subsets with Dynamic PCA
  • Heat maps, box plots, bar plots, line plots, PCA plots and more, both in 2D and 3D

Does it work on my data?

Answer the four quick questions below and find out if you can use Qlucore on your data. 

For more details about supported data formats and data import see Data Import or Contact us with questions.

Case Studies

RNA-seq case study

RNA-Seq analysis using Qlucore

Stanford University, US

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

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Using Qlucore in epigenetics research studies

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

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

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Qlucore analysis of transcriptomic data

University of Manchester, UK

The study includes working with data from more than 400 arrays. Visualization is used in the effort to understand the human growth process.

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Analysis of public data using Qlucore

Beijing Normal University, China

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.

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