With the increasing popularity of whole-genome shotgun sequencing (WGSS) via high-throughput sequencing technologies, it is becoming highly desirable to perform comparative studies involving multiple individuals (from a specific population, race, or a group sharing a particular phenotype). The conventional approach for a comparative genome variation study involves two key steps: (1) each paired-end high-throughput sequenced genome is compared with a reference genome and its (structural) differences are identified; (2) the lists of structural variants in each genome are compared against each other. In this study we propose to move away from this two-step approach to a novel one in which all genomes are compared with the reference genome simultaneously for obtaining much higher accuracy in structural variation detection. For this purpose, we introduce the maximum parsimony-based simultaneous structural variation discovery problem for a set of high-throughput sequenced genomes and provide efficient algorithms to solve it. We compare the proposed framework with the conventional framework, on the genomes of the Yoruban mother-father-child trio, as well as the CEU trio of European ancestry (both sequenced by Illumina platforms). We observed that the conventional framework predicts an unexpectedly high number of de novo variations in the child in comparison to the parents and misses some of the known variations. Our proposed framework, on the other hand, not only significantly reduces the number of incorrectly predicted de novo variations but also predicts more of the known (true) variations.
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