Biomedical Genomics Workbench

Are you ready to uncover the signals that lead to breakthrough discoveries in your human disease research? Biomedical Genomics Workbench is a comprehensive and accurate data analysis platform that enables you to find the signal in the noise in your cancer and hereditary disease NGS data. With its broad selection of end-to-end analysis workflows, tools, and visualization modules, it enables easy and accurate discovery, verification, and validation of novel disease biomarkers.

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Find the signals in the noise with ease!

Discover novel insights with greater than 95% sensitivity and unsurpassed accuracy. Biomedical Genomics Workbench guides you through a complete analysis of your genome, exome, targeted amplicon, transcriptome, and epigenetic NGS sequencing data for results you can trust.

  • Complete end-to-end and customizable analysis workflows for the comprehensive discovery, verification, and validation of novel biomarkers
  • Specialized functionalities such as primer and primer-dimer removal for highly accurate targeted amplicon sequencing results
  • High sensitivity detection of germline and low frequency variants from DNA-Seq and RNA-Seq data
  • Unsurpassed accuracy for copy number detection in exome and targeted amplicon sequencing data
  • Easy viewing of findings such as dynamic protein structures in 3D, and sequencing reads afford faster discovery





Lead your own discovery

Modify workflows and discovery parameters for hypothesis-led  analysis that guides you to the most promising breakthroughs.


Discover more faster

Simplicity, flexibility, and accuracy combine to provide you with a solution that is more than 50% faster than open-source alternatives.


With the help of Biomedical Genomics Workbench and it’s clearly laid out and intuitive user interface, I was able to prototype, develop, and validate a custom targeted amplicon NGS data analysis pipeline in a matter of a few weeks. Any roadblock I would come across in this process was resolved either with the detailed documentation available or with a quick email to their responsive technical support team.

Steven Lockton, Biological Dynamics

Try Biomedical Genomics Workbench today!

QIAGEN Bioinformatics provides a portfolio of NGS solutions that integrate seamlessly to aid researchers in the fight against hereditary disease and cancer.
Learn from a case story how Biomedical Genomics Workbench is used for the identification of low frequency variants from a tumor/normal pair.

Start your 30-day no obligation trial of Biomedical Genomics Workbench today and take a look at our tutorials to see how you can use the software for your research.

Download tutorials
1. Variant identification in a tumor sample
2. Identification of somatic variants in a tumor sample using the matched normal sample for removal of germline variants
3. ChIP Sequencing using the Biomedical Genomics Workbench
4. Modification of an existing workflow
5. Visualize variants on protein structure
6. Copy Number Variant Detection
7. RNA-Seq Analysis of Human Breast Cancer Data
8. Small RNA Analysis using Illumina Data


System requirements

  • Windows Vista, Windows 7, Windows 8 or Windows Server 2008
  • Mac OS X 10.7 or later.
  • Linux: Red Hat 5.0 or later. SUSE 10.2 or later. Fedora 6 or later.
  • 8 GB RAM required
  • 16 GB RAM recommended
  • 1024 x 768 display required
  • 1600 x 1200 display recommended
  • Intel or AMD CPU required
  • Minimum 80 GB free disc space required in the CLC_References directory (if you are not connected to a server). If you have less free disc space available it is possible to change the reference data location. How to do this is described in Download and configure reference data

We frequently release updates and improvements such as new functionalities, bug fixes or plugins. To get a complete overview, please read the latest improvements.

  • Special requirements for read mapping. The numbers below give minimum and recommended memory for systems running mapping and analysis tasks. The requirements suggested are based on the genome size. Systems with less memory than specified below will benefit from installing the legacy read mapper plugin (see ). This is slower than the standard mapper but adjusts to the amount of memory available.
    • Human ( 3.2 gigabases) and Mouse ( 2.7 gigabases)
      • Minimum: 6 GB RAM
      • Recommended: 8 GB RAM

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