About this Class

Over the past decade the importance of Structural Variation (SV) is becoming more obvious not just for population diversity but also with clear impacts in multiple diseases (e.g. Neurological) as well as cancer.  SV are often loosely defined as 50bp or larger being characterized in five different SV types that impact more base pairs than single nucleotide variations all together. These types of genomic alterations (SV) are often located in tandem repeats and are thus hard to identify and thus study. My group as well as others have demonstrated that long read platforms are superior to identify SV and resolve their alleles together with giving deeper insights in the complexity and variability of repeats. This is also true to resolve the often-complex genomes of cancer patients, where often complex alleles are forming new haplotypes or impacting complex regions such as HLA. In my talk I will summarize the efforts of my group over the past year to improve the detection of Structural Variation and rapid turnaround of diagnosis using long read platforms such as Oxford Nanopore. As such I will demonstrate how we enabled a rapid sequencing effort for full genome sequencing from blood take from patients to report within 8 hours. I will continue to discuss how this can be further optimized in terms of comprehensiveness and scaling. My talk will conclude in highlighting novel developments in my group around single cell genomics sequencing and how we can improve the characterization and study of somatic and mosaic alleles thought a standard sequencing approach. Dr. Fritz Sedlazeck is an Associate Professor at the Human Genome Sequencing Center at Baylor College of Medicine and an Adjunct Associate Professor at Rice University. His research focuses on algorithmic developments and high-performance computing for genomic and genetic applications. Specifically, he studies ways to improve the characterization of complex genomic alterations between individuals’ genomes based on large genomic sequencing data and as such improve our understanding of complex phenotypes such as human diseases.