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RNA-Seq with Kallisto and Sleuth

Goal

Analyze RNA-Seq data for differential expression. Kallisto manual is a quick, highly-efficient software for quantifying transcript abundances in an RNA-Seq experiment. Even on a typical laptop, Kallisto can quantify 30 million reads in less than 3 minutes. Integrated into CyVerse, you can take advantage of CyVerse data management tools to process your reads, do the Kallisto quantification, and analyze your reads with the Kallisto companion software Sleuth manual in an R-Studio environment.


Prerequisites

Downloads, access, and services

In order to complete this tutorial you will need access to the following services/software

Prerequisite Preparation/Notes Link/Download
CyVerse account You will need a CyVerse account to complete this exercise CyVerse User Portal
Atmosphere access (optional) This tutorial will use R studio in Atmosphere; if desired you can complete these sections by installing the Sleuth tools on your own R instance CyVerse User Portal

Platform(s)

We will use the following CyVerse platform(s):

Platform Interface Link Platform Documentation Quick Start
Data Store GUI/Command line Data Store Data Store Manual Data Store Guide
Discovery Environment Web/Point-and-click Discovery Environment DE Manual Discovery Environment Guide
Atmosphere Command line (ssh) and/or Desktop (VNC) Atmosphere Atmosphere Manual Atmosphere Guide

Application(s) used

Discovery Environment App(s):

App name Version Description App link Notes/other links
Kallisto-0.42.3-index 0.42.3 Kallisto-0.42.3-index Kallisto-0.42.3-index Kallisto manual
Kallisto-0.42.3-quant-PE 0.42.3 Kallisto Quantification Kallisto-0.42.3-quant-PEp Kallisto manual

Atmosphere Image(s):

Image name Version Description Link Notes/other links
CyVerse Training Workshop 1.1.2 Image for use at CyVerse Training Workshops CyVerse Training Workshop Image This image has Kallisto and Sleuth installed. Once started, an R Studio Server will be available at your image ip address, port 8787 (e.g. image.ip.address:8787)

Input and example data

In order to complete this tutorial you will need to have the following inputs prepared

Input File(s) Format Preparation/Notes Example Data
RNA-Seq reads Fastq (may also be compressed, e.g. fastq.gz) These reads should have been cleaned by upstream tools such as Trimmomatic Example fastQ files
Reference transcriptome fasta Transcriptome for your organism of interest Example transcriptome

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