Synopsis

• Generating a novel and comprehensive catalog of high-confidence 5’complete human long non-coding RNA (lncRNA) genes by integratingFANTOM5 CAGE data with RNA-Seq
• Clarifying the heterogeneity of lncRNAs to determine the derivation of intergenic lncRNAs as enhancer-like regions rather than from promoters
• Incorporating genetic and expression data to identify potential roles in diseases and gene regulation for thousands of lncRNAs

Synopsis

• Evaluating these two sequencing protocols
• Exploring the real advantages and disadvantages of total RNA-Seq

Synopsis

• Gene expression analysis of target model systems is required to understand the genetic changes associated with biological responses
• We show that the combination of RT-PCR amplification with next generation deep sequencing provides a more sensitive and robust approach for transcriptome profiling than standard RNA-Seq
• The RT-PCR step with targeted primers enables the profiling assay to generate highly specific data, even with mouse xenograft models, directly from total RNA

Synopsis

• Discussing a role for nascent RNA-Seq as a Diagnostic and Prognostic Tool
• Analyzing 3’vs. 5’ sequencing, applications utility and robustness
• Understanding how to map polymerase on the template
• Realizing the benefits from a low posttranscriptional noise

Synopsis

• Identifying the minimum sample size for low-input RNA-sequencing
• Evaluation of SMARTer/Nextera vs. TruSeq on sorted human immune cells from blood and tumor
• Profiling small numbers of sorted immune populations from clinical tumor tissues

Synopsis

• The Command Line is Compromise: A New Paradigm for Single-Cell RNA-Seq Analysis
• We have developed a new analysis paradigm for gene expression analysis using bidirectional cell population-gene set visualization, which enables bench scientists to derive insight from RNA-Seq data, particularly single cell RNA-Seq; share their gene sets and discovery process reproducibly; pre-process and normalize how they see fit; and requires no command line or bioinformatics expertise.
• Using a publicly available data set from a study of metastatic melanoma and intuitive supervised and unsupervised analyses, we show that layers of depth beyond traditional immunological phenotyping are possible and show reduction in analysis time from >4
  hours to less than 30 seconds.
• We present an innovative, rapid, intuitive analysis paradigm to harness the power of single cell RNA-Seq and gene expression studies.

Synopsis

• Understanding that cancer immunotherapies are highly variable from patient to patient and that RNA-Seq can provide novel insights into cancer immune subtypes
• Realize the emerging role of RNA-Seq in developing predictive and early pharmacodynamics biomarkers for cancer immunotherapy

Synopsis

• Omicsoft RNA-Seq pipelines (RNA-Seq transcriptome profiling, miRNA-Seq, Single-Cell RNA-Seq, etc) used at Merck
• Integrating PD1 genomics biomarker data (RNA-Seq, Whole Exome Sequencing, NanoString and MSI) using the Omicsoft Land technology

Synopsis

• Exploring the mechanism utility and consequence of dysregulation of 3’ termination in disease pathogenesis and spread
• Insights into how 3’ regulation is related to health and disease
• Recent data into cancer, metastasis and neurological diseases

Synopsis

High-resolution RNA analysis using next-generation sequencing (RNA-seq) is a rapidly growing application in disease research. The quality of RNA extracted from biological
specimens is highly variable and yields are often low, thus impacting the ability to generate high-quality sequencing libraries.

In this presentation, we will:

• Discuss improvements in the library preparation workflow
• Provide guidance on working with low input & degraded samples
• Demonstrate improved detection of differential expression in tumor profiling

Synopsis

• Review state of the art in RNA splicing detection, quantification, and prediction algorithms
• Showcase examples of how machine learning algorithms are used to derive new findings in RNA processing and disease associated transcript variations that are then verified experimentally
• Address challenges and directions in Big Data analysis in the field

Synopsis

• Demonstrating RNA-Seq as a powerful technology for transcriptome-wide profiling of mRNA isoform variation resulting from alternative RNA processing and modifications
• Identifying novel biomarkers and therapeutic targets of cancer through global analysis of mRNA isoform variations in clinical cancer RNA-Seq data sets
• Exploring robust and efficient statistical and computational methods required for isoform analysis using massive RNA-Seq datasets

Synopsis

1. Data Processing & Informatics Management, Rob Currie
2. The Utility of Non-Coding RNA in Drug Development, Paul Kayne
3. What’s Necessary for Clinical Validation: Controlling the Statistical Variables, Garry Nolan
4. RNA Based Modifications: Exploring a Potential Role in Drug Development, Yi Xing