|Home | About | Journals | Submit | Contact Us | Français|
Structural variations (SV) found in eukaryotic genomes include insertions, deletions, inversions, translocations, and copy number variations (CNV). The emerging body of literature clearly illustrates the role of SVs, such as CNVs, in the susceptibility or resistance to certain diseases. While microarrays have traditionally been an effective tool for identifying CNVs, recent advances in paired-end, next-generation sequencing technology now provide an alternative approach.To detect copy number variants using paired-end sequencing it is essential to leverage a bioinformatics pipeline that can distinguish the mapped ends, and identify statistically significant differences as compared to a reference sequence. Several commercial and open source software tools for automatic detection of SVs and CNVs are available and this list is rapidly growing. Though the number of tools continues to increase, they are neither as robust nor mature as those currently available for analyzing microarray experiments.As such, packages are being constantly evaluated with the intent of determining which performs best in this capacity.For its annual research project, the GVRG has hypothesized that an optimal combination of the statistical models and paired-end reads will have the most traction in next-generation sequencing for CNV detection. Using C. elegans as a model organism, we have performed and directed experiments to study the capability of next-generation sequencing for such a purpose. It has been reported that there is nearly 2% natural gene content variation between the Bristol and Hawaii C. elegans strains as determined by aCGH.These published differences, which include a number of CNVs, have provided a valuable framework for conducting the experiments and analyzing results.