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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.


Manual Maintainer(s)

Who to contact if this manual needs fixing. You can also email Tutorials@CyVerse.org

Maintainer Institution Contact
Jason Williams CyVerse / Cold Spring Harbor Laboratory Williams@cshl.edu

.. toctree::
        :maxdepth: 2

        Tutorial home <self>
        Organize Kallisto Input Data <step1.rst>
        Build  Transcriptome Index and Quantify Reads with Kallisto <step2.rst>
        Analyze Kallisto Results with Sleuth <step3.rst>


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|

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|

Application(s) used

Discovery Environment App(s):

App name Version Description App link Notes/other links
Kallisto-v.0.43.1 0.43.1 Kallisto v.0.43.1 |Kallisto app| |Kallisto manual|
RStudio Sleuth 0.30.0 RStudio with Sleuth (v.0.30.0) and dependencies |Sleuth app| |Sleuth manual|

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|

Sample Data and Working with Your Own Data

Sample data

About the Sample Dataset In this tutorial, we are using publicly available data from the SRA. This tutorial will start with cleaned and processed reads. The SRA experiment used data from bioproject |PRJNA272719|. The abstract from that project is reprinted here:

'To survey transcriptome changes by the mutations of a DNA demethylase ROS1 responding to a phytohormone abscisic acid, we performed the Next-gen sequencing (NGS) associated RNA-seq analysis. Two ROS1 knockout lines (ros1-3, ros1-4; Penterman et al. 2007 [PMID: 17409185]) with the wild-type Col line (wt) were subjected. Overall design: Three samples (ros1-3, ros1-4 and wt), biological triplicates, ABA or mock treatment, using Illumina HiSeq 2500 system' |citation|.

Tip

Working with your own data

If you have your own FASTQ files upload them to CyVerse using instructions in the CyVerse |Data Store Guide| (e.g. iCommands/Cyberduck).


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