MAY 09, 2018 9:00 AM PDT

Size Matters: Accurate Detection and Phasing of Structural Variations

C.E. Credits: CEU
Speaker

Abstract

In this presentation I will describe our latest work to obtain comprehensive genomes leveraging long and linked reads. The vast majority of NGS whole-genome data covers hundreds of thousands of samples with short illumina reads, which are unable to capture the full spectrum of genetic variation and genomic complexity. Such comprehensive variation is critical to understanding the full heritability and genetic foundations of human disease. In this seminar I will present our novel alignment strategy (NGMLR) for long read data (Oxford Nanopore and PacBio) and our novel Structural Variations (SVs) caller Sniffles. These two methods improved the accuracy for both technologies enabling the accurate and easy detection of SVs. This includes also nested events that we have previously been blind to or linked events connecting genes over multiple regions.  We will discuss problems, characteristics and limitations of short reads. In addition, I will discuss the impact of these novel found SVs in cancer and other genomes with respect to RNA seq. In the end, I will highlight our current findings where we combine these long read technologies with linked reads to be able to phase SNV and SVs together to obtain a diploid genome per sample. These phased genomes are the most comprehensive representation of genomes up to date and we can now finally generate them within days. 


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MAY 09, 2018 9:00 AM PDT

Size Matters: Accurate Detection and Phasing of Structural Variations

C.E. Credits: CEU

Specialty

Biotechnology

Cancer Research

Molecular Diagnostics

Bioinformatics

Gene Expression

Cancer Diagnostics

Molecular Biology

Lab Safety

Immunotherapy

Pcr/rt-Pcr/real-Time Pcr

Flow Cytometry

Genetics

Immuno-Oncology

Dna Sequencing

Clinical Diagnostics

Geography

Europe67%

North America33%

Registration Source

Website Visitors100%

Job Title

Student50%

Educator/Faculty50%

Organization

Academic Institution67%

Other33%


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