Our latest release introduces a new expectation-maximization algorithm to enhance your RNA-seq results,
and new tools for variant analysis and genotyping.
CLC Genomics Server is a software solution for deployment on your central compute cluster or compute server for centralized bioinformatics analysis and sharing of data generated from all High-Throughput Sequencing platforms. It comes with the same bioinformatics data analysis tools that are known from our successful CLC Genomics Workbench, such as mapping of reads to a known reference, de novo assembly, and variant calling. With a single click within CLC Genomics Workbench it is possible to utilize your central compute clusters or servers to schedule analysis of amounts of data that are not possible to analyse in a desktop computer environment.
CLC Genomics Server enables sharing of data towards all end-users of CLC Genomics Workbench through mounting of the data folders from the central data storage system attached to the compute cluster or server. With a group based access privilege system on folder level, it is possible to define which user groups should have access to reading and writing of specific data folders. Users and groups can be managed either with a built-in directory system or through a connection to well known LDAP based directory systems.
It is also possible to share data through a central relational database management system like Oracle, MySQL or PostgreSQL with CLC Bioinformatics Database.
External grid integration is available via a DRMAA interface. We currently support the following systems:
CLC Genomics Server supports all major file formats such as fastq, fasta, BAM, VCF, and BED on both input and output side.
The Command-Line Tools for CLC Genomics Server allows you to build your own pipeline by integrating and deploying functionalities provided by either CLC Genomics Server, an external application, or in-house developed pipeline. A flexible plug-in system utilized by the API and our Software Development Kit furthermore makes it possible to tightly integrate your own algorithms directly into the software and make them conveniently available in CLC Genomics Workbench.
Using the built-in Job Node Support or the most popular external grid scheduling systems, it is possible to enable an array of server compute nodes to do your data analysis. This ensures a fit into the organization with both an existing shared compute cluster or a setup of dedicated compute nodes for CLC Genomics Server.
Our tools support bioinformaticians and developers in customizing CLC Genomics Server to meet your specific requirement. In addition, the QIAGEN Informatics Custom Solutions team is able to provide a comprehensive suite of consulting, development, training, and professional services that match bioinformatics and integration requirements regardless of complexity.
The CLC Server and its backends, including the CLC Server job nodes and the grid nodes, must run on the same type of operating system.
Analysis on the CLC Genomics Server can only be initiated from either CLC Genomics Workbench or the Command Line Tools, thus not from other Workbenches from CLC bio.
Scope of requirements
All the system requirements on this page are requirements for the most recent versions of the products. For requirements for older versions, please refer to the user manual for that specific version.
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 memory requirements for working with genomes
A 64-bit computer and operating system is necessary if you wish to utilize more than 2 GB RAM. The numbers below give minimum and recommended amounts for systems running mapping and analysis tasks. The requirements suggested are based on the genome size.
Special requirements for de novo assembly
De novo assembly may need more memory than stated above – this depends both on the number of reads and the complexity and size of the genome. See http://www.clcbio.com/white-paper for examples of the memory usage of various data sets.
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