Gene Expression Profiling Track at Genetics & Genomics

rna-seq
May 13, 2015

Keynote: Next-Generation RNA-Seq Workflows and Analysis

Gary Schroth,PhD

Distinguished Scientist, Illumina, Inc.

See Abstract
A well done RNA-Seq experiment can provide the most comprehensive, accurate and unbiased way to study gene expression, alternative splicing, RNA variation and RNA structure. The past few years have seen amazing technological advances that have led to a wide range of improvements in the RNA-Seq experimental process. Next-Generation RNA-Seq studies are unbiased and accurate, yet sensitive enough to work well with low amounts of total RNA (even if the RNA is highly degraded or comes from FFPE samples). These new methods are also less expensive, require less hands-on-time, and are easier to perform than ever before. Finally the data analysis bottleneck associated with RNA-Seq studies has been completely removed with the advent of pipelines that take full advantage of massively parallel cloud computing resources. In this talk I will review the state-of-the-art in RNA-Seq experimental design, library prep, sequencing and data analysis.
May 13, 10:30 AM – 11:30 AM PT

Statistical methods for bulk and single-cell RNA-seq experiments

Christina Kendziorski, PhD

Professor, Biostatistics & Medical Informatics, University of Wisconsin – Madison

See Abstract
I will discuss recent statistical methods for identifying differentially expressed genes in static and time course bulk RNA-seq experiments. I will also provide an overview of the opportunities and challenges provided by single cell RNA-seq and will discuss a statistical method we have developed for characterizing gene expression dynamics and sample heterogeneity in single cell RNA-seq experiments.Learning objectives:1. Participants will learn the basics of RNA-seq analysis with a focus on normalization and identification of differentially expressed genes.
May 13, 12:00 PM – 1:00 PM PT

What can we learn from large gene expression data sets?

Jeremy A Miller, PhD

Scientist, Allen Institute for Brain Science

See Abstract
The Allen Institute for Brain Science provides several brain atlases that are freely available to the public at www.brain-map.org. A common use for these atlases is to study expression patterns for specific genes of interest in the developing or adult mouse, rhesus monkey, or human brain. In addition, we and others have performed transcriptome-wide analyses on these data to address particular questions of brain development and anatomy. Here, I will describe a few of the Allen Brain Atlases and then present three short vignettes. First, we find that most genes with consistent expression patterns between adult human brains are involved in brain function and dysfunction. Second, we find that, while most genes show consistent expression patterns between species, many differences exist, which could potentially provide insight into the efficacy of some mouse models of disease. Finally, we show how gene expression patterns in different layers of cortex can change dramatically with age while retaining discrete laminar identities, suggesting that it is important both ‘where’ in the brain you look as well as ‘when’.Learning objectives:
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.
May 13, 3:00 PM – 4:00 PM PT

Journeys through Space and Time: Ultra High-Resolution Expression Profiling of Long Noncoding RNAs

Marcel Dinger, MSc (hons), PhD

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

Long noncoding RNAs (lncRNAs) are increasingly recognized as having key regulatory roles in development and disease. However, these regulatory molecules often have short half lives and are expressed only in specific tissues or cell types, resulting in the poor representation of lncRNAs in transcriptomic datasets. Using novel detection and sampling approaches, we reveal a high-resolution spatiotemportal view of the long noncoding transcriptome that provides fresh insights into their roles in development and disease.Learning Objectives:
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.

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