Massively parallel (MP) sequencing technologies are on their way to reduce the cost of whole shotgun sequencing of an individual donor genome to USD 1000. Coupled with algorithms to accurately detect structural (in particular expressed) differences among many individual genomes, MP sequencing technologies are soon to change the way cancer diagnosed and treated. In this talk we will briefly go through some of the algorithm development efforts at the Lab for Computational Biology in SFU for simultaneously analyzing large collections of MP sequenced genomes and transcriptomes, and in particular for identifying and differentiating common and rare, expressed and unexpressed large scale variants with high accuracy. Our algorithms, which we collectively call CommonLAW (Common Loci structural Alteration detection Widgets) move away from the current model of detecting genomic variants in single MP sequenced donors independently, and checking whether two or more donor genomes indeed agree or disagree on the variations. Instead, we propose a new model in which structural variants are detected among multiple genomes and transcriptomes simultaneously. One of our methods, Comrad, for example, enables integrated analysis of transcriptome (i.e. RNA) and genome (i.e. DNA) sequence data for discovering expressed rearrangements in multiple, possibly related, individuals.
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