Catch up on popular industry topics with short videos that you can’t find anywhere else!
Choose by Resource, then filter by Product OR Topic
Learn about the integrated analysis and clinical interpretation of CNV, LOH, and Sequence Variants of FFPE cancer samples profiled on a solid tumor NGS panel.
Learn about the Implementation of copy number variant detection from existing exome and genome samples.
Learn about exome reanalysis and integrated SNV and AOH detection in a patient with a bone marrow failure syndrome.
Learn what the ASCAT algorithm does and how it accounts for complications of aneuploidy, tumor heterogeneity, and contamination from normal cells in tumor sample analysis.
GISTIC helps to identify regions that are significantly gained or lost across a set of samples, giving a greater weight to high-amplitude events which are less likely to represent random aberrations.
Enrichment analysis identifies GO terms and the genes associated with these that are significantly over-represented in the aberrant regions. Learn more.
The method applies the frequency of aberration at a location across the entire sample set and a footprint as the interval lengths of overlapping aberrations.
Learn what aberrations are common, and two popular approaches (GISTIC and STAC) used to determine which are statistically significant.
Gain a better understanding of what mosaicism is, how to identify it, and how it affects the calling algorithms.
Gain a better understanding of how to identify systematic biases in your data. Then, learn some different correction approaches, and when and how to apply them.
Learn some basic terms used when analyzing chromosomal copy number aberrations and allelic imbalances.