Keynote: Next-Generation RNA-Seq Workflows and Analysis
Distinguished Scientist, Illumina, Inc.
Statistical methods for bulk and single-cell RNA-seq experiments
Professor, Biostatistics & Medical Informatics, University of Wisconsin – Madison
What can we learn from large gene expression data sets?
Scientist, Allen Institute for Brain Science
1. Provide examples for how the Allen Brain Atlases can be applied to study brain development and disease using whole-transcriptome data.
2. Describe how complex data sets can provide both unique analytical challenges as well as novel biological insights.
Journeys through Space and Time: Ultra High-Resolution Expression Profiling of Long Noncoding RNAs
Head of Clinical Genomics, Garvan Institute of Medical Research, Conjoint Associate Professor in the St Vincent’s Hospital Clinical School at the University of New South Wales
1. Provide an example of how a technology advancement has led to a key change in our understanding of the functions of the genome.
2. Explain the effect of increased cellular diversity on transcriptome coverage attained by RNA sequencing.
3. Describe the different ways that regulatory information can be stored in noncoding regions of the genome.