![]() ![]() Despite this, studies have shown a strong correlation between SVs and genetic disorders, including Crohn's disease and Down's syndrome. While SNVs have been widely studied in recent years, larger-scale structural variations have been more dificult to characterize. Many studies in genomics are focused on characterizing the content of these variations and identifying associations with diseases or other phenotypes. These variations exist at different scales, ranging from single nucleotide variants (SNVs), to small-scale insertions and deletions (indels), up to large structural variations (SVs) of kilo- to mega-base scale. Within a species, individual genomes differ from one another by a certain amount of genetic variation. ![]() The results of applying our method to this simulation data are presented along with a discussion of the benefits and drawbacks of this technique. We validate our method by applying it to a series of 5 Mb simulation genomes derived from both mammalian and bacterial references. We propose a novel graph construction that builds upon the well-known de Bruijn graph to incorporate the reference, and describe a simple algorithm, based on iterative message passing, which uses this information to significantly improve assembly results. ![]() In this paper, we present a computational method for incorporating a reference sequence into an assembly algorithm. While significant progress has been made in recent years on both de novo assembly and resequencing (read mapping) methods, few attempts have been made to bridge the gap between them. Current methods for identifying structural variation, however, are predominantly focused on either assembling whole genomes from scratch, or identifying the relatively small changes between a genome and a reference sequence. Recent studies in genomics have highlighted the significance of structural variation in determining individual variation. ![]()
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