This space is home to learning materials and tutorials created for CyVerse products and services. To search the entire CyVerse wiki, use the box at the upper right.


LEARNING MATERIALS
 

 

 

 

Skip to end of metadata
Go to start of metadata

Rationale and background:

Plant microRNA prediction tools that utilize small RNA sequencing data are emerging quickly. These existing tools have at least one of the following problems:

1. High false positive rate;

2. Long running time;

3. Work only for genomes in their databases;

4. Hard to install or use.

miR-PREFeR workflow uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species. miR-PREFeR was tested on several plant species and the results show that miR-PREFeR is sensitive, accurate, fast, and has low memory footprint. The miR-PREFeR paper is published on Bioinformatics. http://bioinformatics.oxfordjournals.org/content/30/19/2837.abstract. Please cite the paper if you use the tool in your work.

In this example we will identify microRNA from a Genome-wide profiling of small RNAs in Arabidopsis seedlings under salt and cold stresses experiment. For AtCold library, plants were treated under 5 ?C for 24 hours; For AtPdep and AtPind libraries, plants were treated with 300mM NaCl for 5 hours. Total RNA was isolated from shoots and fractionated on 15% denaturing polyacrylamide gel. RNA molecules ranging from 18 to 30 nt were excised. For AtCold and AtPdep libraries, excised RNAs were ligated to a preadenylated 3' adaptor and a 5’- adaptor using T4 RNA ligase. For AtPind library, excised RNAs were first de-phosphorylated at the 5’ end. After 3’ end adaptor was ligated, the 5’ end was phosphorylated before 5’ adaptor was ligated. Ligation products were purified in polyacrylamide gels, followed by RT-PCR. The small RNA libraries were sequenced using Illumina Genome Analyzer

In this tutorial, we will use data stored at the NCBI Sequence Read Archive.

  1. Align the data to the Sorghum v1 reference genome using HISAT2
  2. Transcript assembly using StringTie
  3. Identify differential-expressed genes using Ballgown
  4. Use Atmosphere to visually explore the differential gene expression results.

If you do not have an account, please see one of the on-site CyVerse staff for a temporary account.

Specific Objectives

By the end of this module, you should

  1. Be more familiar with the DE user interface
  2. Understand the starting data for microRNA analysis
  3. Be able to align short sRNA reads with a reference genome in the DE
  4. Be able to identify and quantify microRNA in DE



  • No labels