The applications listed here are available for use in the Discovery Environment and are documented in: Discovery Environment Manual.

Discovery Environment Applications List

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The DE Quick Start tutorial provides an introduction to basic DE functionality and navigation.

Please work through the documentation and add your comments on the bottom of this page, or email comments to support@cyverse.org.

Rationale and background:


In recent years, high-throughput experimental techniques such as microarray, RNA-Seq and mass spectrometry can detect cellular molecules at systems-level. These kinds of analyses generate huge quantities of data, which need to be given a biological interpretation. A commonly used approach is via clustering in the gene dimension for grouping different genes based on their similarities(Yu et al. 2010).

To search for shared functions among genes, a common way is to incorporate the biological knowledge, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), for identifying predominant biological themes of a collection of genes.

After clustering analysis, researchers not only want to determine whether there is a common theme of a particular gene cluster, but also to compare the biological themes among gene clusters. The manual step to choose interesting clusters followed by enrichment analysis on each selected cluster is slow and tedious. To bridge this gap, we designed clusterProfiler(Yu et al. 2012), for comparing and visualizing functional profiles among gene clusters.

 

Pre-Requisites:

A CyVerse account (Register for a CyVerse account at https://user.cyverse.org).

An up-to-date Java-enabled web browser. (Firefox recommended. If you wish to work with your own large datasets and upload them using iCommands, Chrome is not suitable due to its issues in utilizing 64-bit Java.)

Mandatory arguments

  1. Inputs
    1. Input File: Path to the input file that contains the list of id's
    2. Number of lines per file: How many lines that you want to split the input file into. The number of output files corresponds tonumberof lines that you want to split into
    3. File_prefix: Prefix of the file that gets created (Default is "x")
  2. Outputs
    1. Output folder: Name of the output folder (Default is "Output")

Test run

The test data for this app is located at /iplant/home/shared/iplantcollaborative/example_data/OSG-RMTA/sra_id_list.txt
The 'sra_id_list.txt' file contains a list of 15 SRA id's, one SRA ID per line

 

  1. Inputs
    1. Input File: /iplant/home/shared/iplantcollaborative/example_data/OSG-RMTA/sra_id_list.txt
    2. Number of lines per file: 5
    3. File_prefix: osg_rmta_
  2. Outputs
    1. Output folder: OSG-RMTA-File-Split

After a successful run, you'll get 3 files each containing 5 SRA Id's with the prefix osg_rmta_ in OSG-RMTA-File-Split folder

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