Bioinformatic software for ChIP-seq and ATAC-seq

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.                         


Analyze ChIP-seq and ATAC-seq data 

The peak analysis support in Qlucore Omics Explorer allows for comprehensive analysis of peak data such as ChIP-seq and ATAC-seq. The main components of the peak analysis processing are peak detection, consensus peaks and count matrix generation. The genome browser in the NGS module includes powerful filtering options for the peak analysis, making it easy to find interesting features. The browser allows the user to annotate peaks and export the results as a bed file. If the experiment also includes RNA-seq data, it is possible to generate a count matrix for RNA-seq and swap between the RNA-seq and ChIP/ATAC-seq count matrices during the analysis.

Key functionalities 

  • Full work-flow support. From normalization to data export.  

  • Peak detection and visualization of detected peaks in dedicated tracks. 

  • Peak variable list with peak information automatically created. 

  • Visualizations (Genome browser, line plots, heatmaps, pie chart).  

  • Use the List tool to select Peaks and add them to a variable list that can be saved. 

  • Option to save data as a BED file. 

The peak analysis support requires Next Generation Sequencing module for Qlucore Omics Explorer. 

To learn more about NGS module

Watch video and learn more

"Easy and fast ChIP seq analysis tailored for research biologists"

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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At Yale School of Medicine researchers use Qlucore

Yale School of Public Health, Yale School of Medicine

Supporting researchers working with large complex datasets of proteins, genes and metabolites

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Short introduction video

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