Tumor heterogeneity has been known for a while but quantifying heterogeneity is still a challenge.  NGS is the method of choice in the analysis of tumor heterogeneity, however, there are some inherent challenges associated with it. These include false positives, gaps in the gene due to overrepresentation and incomplete representation of low-frequency transcripts – all contributing to an inaccurate picture.  Conventional library prep strategies for NGS are based on PCR, which introduces sequence-based bias and amplification noise, leading to these inaccuracies. 

In this webinar, we will cover

1. Principles of UMI and the new QIAseq product porfolio
2. How UMI along with SPE (single primer extension) allows for increased uniformity across difficult-to-sequence regions, removal of library construction bias, improved data analysis and sequencing optimization
3. How data generated from using UMI and SPE is directly comparable to analysis derived from whole transcriptome and exome sequencing
4. Application of UMI and SPE in the discovery of novel gene fusions and in the analysis of gene expression and genetic variation