Advancing de novo assembly-based pipelines
A novel platform could significantly increase throughput and decrease costs in de novo assembly-based variant calling in whole genome sequencing applications.
A novel platform for de novo assembly-based variant calling has been announced that has been shown to significantly increase throughput by 10-times and decrease assembly costs per genome by over 70%. The CompStor Novos platform could be utilized in precision medicine, improving patient well-being.
“Variant calling is an important problem in personalized medicine that is usually addressed by aligning reads to the reference genome, an approach that often misses large structural variations,” commented Pavel Pevzner, director of the NIH Center for Computational Mass Spectrometry at the University of California, San Diego (CA, USA).
“Although an alternative assembly-based approach is better suited for finding such variations, it is still rarely used due its high computational cost. OmniTier’s CompStor Novos represents an important advancement in enabling de novo assembly-based pipelines.”
Following on from its development, the CompStor Novos has been utilized in a joint study by its developers, OmniTier (MN, USA) and the Mayo Clinic’s Center for Individualized Medicine (MN, USA). The results showed that not only does it achieve short-variant statistics that are comparable to the most reliable alignment-based pipeline, but it also reveals longer variants that are not found with the alignment method.
“De novo assemblers are generally available for use on a single compute platform – either a single server, which is slow but low-cost, or a supercomputer, which is fast but expensive. By introducing parallelized de novo assembly and efficiently aggregating the compute and memory resources on commodity servers, the CompStor Novos tiered memory solution offers the opportunity to speed-up assembly-based workflows and extend such solutions to other time-extensive applications in genomics, transcriptomics, and proteomics,” explained Pevzner.
The platform should enable researchers to begin utilizing de novo assembly techniques for improved diagnostics, as well as speeding up the pace of precision medicine.
