System requirements
- Windows 8, Windows 10, Windows 11, Windows Server 2012, Windows Server 2016, Windows Server 2019 and Windows Server 2022
- Mac: macOS 10.15, macOS 11 through macOS 12.01. Macs with the Apple M1 chip are supported. The software is expected to run without problems on more recent macOS releases than those listed, but we do not guarantee this.
- Linux: RHEL 7 and later, SUSE Linux Enterprise Server 12 and later. The software is expected to run without problem on other recent Linux systems, but we do not guarantee this. To use BLAST related functionality, libnsl.so.1 is required.
- 64 bit operating system
- 16 GB RAM recommended (8 GB RAM required)
- 1024 x 768 display required
- 1600 x 1200 display recommended
- Intel or AMD CPU required
See Limitations on maximum number of cores for information pertaining to working on systems with >64 cores.
System requirements for read mapping
For mapping to the human genome ( 3.2 gigabases), or genomes of a similar size, 16 GB RAM is required. Smaller systems can be used when mapping to small genomes.
Larger amounts of memory can help the overall speed of the analysis when working with large datasets, but little gain is expected above about 32 GB of RAM.
Increasing the number of cpus can decrease the time a read mapping takes, however performance gain is expected to be limited above approximately 40 threads.
Special system requirements for the 3D Molecule Viewer
- Requirements
- A graphics card capable of supporting OpenGL 2.0.
- Updated graphics drivers. Please make sure the latest driver for the graphics card is installed.
- Recommendations
- A discrete graphics card from either Nvidia or AMD/ATI. Modern integrated graphics cards (such as the Intel HD Graphics series) may also be used, but these are usually slower than the discrete cards.
Indirect rendering (such as x11 forwarding through ssh), remote desktop connection/VNC, and running in virtual machines is not supported.
Special requirements for de novo assembly
. De novo assembly may need more memory than stated above - this depends both on the number of reads, error profile and the complexity and size of the genome. See http://resources.qiagenbioinformatics.com/white-papers/White_paper_on_de_novo_assembly_4.pdf for examples of the memory usage of various data sets.
Subsections