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Short course describing the considerations for a successful RNA-seq experiment

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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

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