CompStor Novos: a low cost yet fast assembly-based variant calling for personal genomes

Abstract

Application of assembly methods for personal genome analysis from next generation sequencing data has been limited by the requirement for an expensive supercomputer hardware or long computation times when using ordinary resources. We describe CompStor™ Novos, achieving supercomputer-class performance in de novo assembly computation time on standard server hardware, based on a tieredmemory implementation. Run on commercial off-the-shelf servers, Novos assembly is more precise and 10-20 times faster than that of existing assembly algorithms. Furthermore, we integrated Novos into a variant calling pipeline and demonstrate that both compute times and precision of calling point variants and indels compare well with standard alignment-based pipelines. Additionally, assembly eliminates bias in the estimation of allele frequency for indels and naturally enables discovery of breakpoints for structural variants with base pair resolution. Thus, Novos bridges the gap between alignment-based and assemblybased genome analyses. Extension and adaption of its underlying algorithm will help quickly and fully harvest information in sequencing reads for personal genome reconstruction.

An ensemble deep learning framework to refine large deletions in linked-reads

“…Assembly-based methods assemble the full length of sequences (contigs) first, and then seek to detect variations by aligning to the reference. Alignment-based approaches generally enable the detection of SV at better resolution and higher sensitivity, by avoiding reference bias from alignment [8]. However, the computation cost is generally higher than alignment-based approach.…”