Setting up for a successful RNA-experiment

Short course describing the considerations for a successful RNA-seq experiment. This workshop is adapted from the "Setting up for Success when planning an RNA-seq experiment" session at GCC-BOSC 2018

Audience	Computational Skills	Prerequisites	Duration
Novice to Experienced Researchers	None	None

This workshop is geared towards researchers who are thinking about conducting an RNA-seq experiment and are interested in knowing more about what is involved. The planning process requires taking a step back to evaluate various factors and ultimately assess the feasibility of the experiment, including thinking about potential pitfalls and how to avoid them. The workshop will go into detail about the different strategies for working with RNA-seq data depending on the biological question being addressed. Specific topics include:

• Best practice guidelines for experimental design (Biological replicates, Paired-end vs Single-end, Sequencing depth).
• Data storage and computational requirements.
• Overview of commonly used workflows for differential gene expression, de-novo assembly, isoform quantification and other uses of RNA sequencing.

The focus of this workshop is to outline current standards and required resources for the analysis of RNA sequencing data. This workshop will not provide an exhaustive list of software tools or pipelines available; rather it aims to provide a fruitful discussion on how best to prepare for performing RNA-seq data analysis from the lab to manuscript preparation.

Learning Objectives

• Describe the resources needed to perform an experiment to identify differentially expressed genes using RNA sequencing, including in the laboratory and computationally.
• Describe key experimental design considerations.
• Explain the analysis workflow (including QC) starting with raw data and finishing with a list of differentially expressed genes.
• List tools and computational skills necessary to implement the various steps in the above-mentioned workflow.

Contents/Schedule

Lessons	Duration	Presenter
Introduction	15 mins	Radhika Khetani
Library Prep	30 mins	Radhika Khetani
Sequencing steps & sequencers	25 mins	Meeta Mistry
Experimental planning considerations	35 mins	Mary Piper
Strategies for bulk RNA-seq analysis	30 mins	Meeta Mistry
Data management	15 mins	Radhika Khetani
Break	30 mins
Raw data QC	30 mins	Mary Piper
Mapping/quantification	25 mins	Meeta Mistry
Sample-level assessment	30 mins	Mary Piper
Count modeling and hypothesis testing	30 mins	Meeta Mistry
Visualization of results	15 mins	Radhika Khetani
Functional analysis	20 mins	Mary Piper

Resources

• Information about RIN number from Agilent