AARHUS, Denmark–Last week CLC bio released the beta version of their long awaited assembly algorithm for de novo assembly to selected customers and now it’s publically available. The new assembler comes in two different editions, as a standalone command-line interface and as a graphical user interface through a plug-in for CLC Genomics Workbench.

User Experience Manager at CLC bio, Søren Mønsted, states, “There are two major features that sets our de novo assembler apart from other de novo assembly tools in the market. The first is the memory requirements which are far less than required by other solutions in the market, making assembly of the human genome possible on a single computer with 32 GB of RAM. The other major feature is the speed where the aforementioned 32 GB RAM computer can de novo assemble a 37-fold coverage human genome in around 7 hours – that’s super fast!”

Post Doc. at Bioinformatics Research Center (BiRC) at the Aarhus University, Eva Stukenbrock, is comparing genomes of disease causing fungi in order to reveal patterns of evolution and host specialization. She has intensively used CLC Genomics Workbench for both reference assemblies of Illumina reads and genome analyses and is now also applying the de novo assembler to her large genome dataset. Stukenbrock says, “We have been part of the development process and are quite enthusiastic about this new de novo algorithm and the potential it has for our research projects. As a biologist, I’m personally very happy about the possibility to use this powerful algorithm through a smooth graphical user interface, so I can run de novo assemblies without having to involve programmers or the IT department!”

The de novo assembler beta version supports data from multiple high-throughput sequencing instruments, including hybrid de novo assemblies with Illumina, Roche 454, and Sanger data. The aim is to release the final version in the first quarter of 2010. For more information or to download the beta version, go to: http://www.clcbio.com/denovo

CLC bio has also released an updated white-paper benchmarking the new algorithm against both Velvet and ABySS, which is available here: http://www.clcbio.com/white-paper

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