CLC Assembly Cell is a high-performance computing solution for mapping reads to a reference and de novo assembly of next generation sequencing data.
With the command-line interface of CLC Assembly Cell, you can easily include its functionalities in scripts and other next generation sequencing workflows, and it's easy to install on your desktop computer or larger compute cluster. CLC Assembly Cell is accelerated through advanced algorithm implementations using the SIMD instruction set to parallelize and accelerate the most compute intensive parts of the algorithms. This makes the software one of the fastest and most accurate packages for NGS data analysis on the market.
Our new assembler is now part of CLC Assembly Cell - allowing rapid error correction and assembly of PacBio data. The read mapper has also been improved to better handle mapping of long reads such as PacBio sequencing reads.
Read more about CLC Assembly Cell
Assembling gold standard reference genomes for microbial pathogens has become fast and easy. We're proud to bring you a new feature allowing rapid error correction and assembly of PacBio data.
The latest version of CLC Genome Finishing Module introduces tools for error-correction and de novo assembly of raw PacBio reads. High quality assemblies can be generated in a fraction of the time that is needed by leading alternatives.
CLC Genome Finishing Module consumes less than 10 percent of the memory used by alternative solutions, while completing the assembly faster. The Module runs on all major operating systems, and fully integrates into the widely used CLC Genomics Workbench and CLC Genomics Server solutions. The included preconfigured workflow makes it extremely user-friendly.
Benchmarking
We compared the performance of the industry standard HGAP1 when run on a high performance computer to the performance of the workflow "PacBio De Novo Assembly Pipeline" included in CLC Genome Finishing Module. Please note that our PacBio De Novo Assembly Pipeline was run on a standard laptop for this comparison.
1Chin et al. (2013), Nature Methods, 10(6), 563–569
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