Broadcast Date: September 22nd, 2015
Time: 11:00 am ET, 8:00 am PT
Targeted resequencing of DNA allows researchers to focus on genes of interest for cost-effective analysis of genetic variations. Typically, to analyze single nucleotide mutations and copy number changes in a single sample, researchers have had to employ completely different analysis platforms and sample-preparation methods.
During this webinar we will describe and present data for an NGS target enrichment workflow, based on the novel Single Primer Enrichment Technology (SPET), for the simultaneous targeted analysis of multiple types of variations. This method can be employed for the analysis of SNPs, indels, and CNVs in a single assay, making conservation of precious patient samples and more efficient use of sequencing resources possible. We will feature data from target-enrichment studies with a 509 cancer gene panel using a simple protocol that generates sequence-ready libraries from good quality DNA as well as DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues.
Who Should Attend
- R&D scientists using NGS as a tool for sequence analysis
- Researchers selectively targeting the genome to discover biomarkers for mutations, variant detection, and copy number variations
- Clinical research and development scientists developing NGS-based diagnostic and prognostic tests based on genomic biomarkers
- Clinicians using NGS-diagnostics to guide potential treatment
- Gene sequencing center scientists performing patient tumor genomic characterization
You Will Learn
- How the novel Single Primer Enrichment Technology (SPET) is superior to existing methods for analysis of copy number variation and loss of heterozygocity.
- How Single Primer Enrichment Technology (SPET) is employed with a panel of 509 cancer genes to enable a comprehensive view of the biology of a patient sample using a single assay.
- How the simple analysis method delivers CNV measurements with statistical significance and a broad dynamic range making it ideal for the analysis of low and high copy number changes which are common in cancer samples.
Produced with support from:
Luke Sherlin, Ph.D.
Director, Technical Support,
Stephanie Huelga, Ph.D.
Lead Bioinformatics Scientist,