RNA sequencing is advancing our ability to profile cell types and detect disease. However, challenges in data normalization, cell type deconvolution, and RNA modification analysis have previously limited its potential.
This webinar brings together leading researchers in transcriptomics to discuss novel approaches to RNA sequencing and data analysis. Attendees will learn about an innovative method for normalizing scRNA-seq data and deconvoluting cell types in bulk RNA-seq data, along with insights from direct RNA sequencing that explore the rRNA epitranscriptome in tumor samples.
What You Will Learn:
- An improved approach to cell type deconvolution in bulk RNA-seq that addresses limits in existing models such as BayesPrism and CIBERSORTx
- Epitranscriptomic fingerprinting, a method that uses rRNA modifications to identify tissues, predict tumorigenicity, and classify disease with minimal sequencing data
- Long-read RNA sequencing for cancer driver variant detection, including a splicing-aware approach that integrates transcript and variant analysis for improved functional predictions
- How scRNA-seq normalization biases impact cell type deconvolution and how to correct them
- How direct RNA sequencing provides a more complete view of the transcriptome
- Future directions in RNA sequencing and their impact on research and diagnostics
- Researchers working with bulk or single-cell RNA sequencing
- Bioinformaticians developing or applying RNA analysis tools
- Scientists exploring RNA-based biomarkers for disease classification
- Anyone interested in the latest advances in RNA sequencing