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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.
Who to contact if this manual needs fixing. You can also email [email protected]
| Maintainer | Institution | Contact |
|---|---|---|
| Jason Williams | CyVerse / Cold Spring Harbor Laboratory | [email protected] |
.. 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>
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| |
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| |
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| |
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
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).
Fix or improve this documentation
- Search for an answer: |CyVerse Learning Center|
- Ask us for help: click |Intercom| on the lower right-hand side of the page
- Report an issue or submit a change: |Github Repo Link|
- Send feedback: [email protected]