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pypm install grape.recipe.pipeline

How to install grape.recipe.pipeline

  1. Download and install ActivePython
  2. Open Command Prompt
  3. Type pypm install grape.recipe.pipeline
 Python 2.7Python 3.2Python 3.3
Windows (32-bit)
1.1.9
1.1.15Never BuiltWhy not?
1.1.9 Available View build log
1.1.5 Available View build log
Windows (64-bit)
1.1.9
1.1.15Never BuiltWhy not?
1.1.9 Available View build log
1.1.5 Available View build log
Mac OS X (10.5+)
1.1.9
1.1.15Never BuiltWhy not?
1.1.9 Available View build log
1.1.5 Available View build log
Linux (32-bit)
1.1.11
1.1.15Never BuiltWhy not?
1.1.11 Available View build log
1.1.9 Available View build log
1.1.8 Available View build log
1.1.6 Available View build log
1.1.5 Available View build log
Linux (64-bit)
1.1.15 Available View build log
1.1.11 Available View build log
1.1.9 Available View build log
1.1.8 Available View build log
1.1.6 Available View build log
1.1.5 Available View build log
 
Author
License
GPL
Dependencies
Depended by
Lastest release
version 1.1.15 on Jan 9th, 2014

The grape.recipe.pipeline package is a Buildout recipe used for configuring Grape, a pipeline used for processing and analyzing RNA-Seq data. RNA-Seq is a next generation sequencing technology used to sequence cellular RNA. To run Grape, you need the read files in Fasta, FastQ or prealigned SAM/BAM formats, as well as a genome and a gene transcript annotation. First, Grape does the quality control and then aligns the reads to the genome. The core of Grape is the analysis of the transcriptome. Grape quantifies Gene and transcript expression levels, estimates exon inclusion levels, and discovers novel splice forms for you, and includes a powerful web application that allows you to seamlessly publish your summary statistics locally or on the Internet. While Grape can run on a standalone machine with modest hardware requirements, it is designed to run in parallel on a computer cluster. Grape comes with its own default mapping and quantification tools, and makes it easy to replace any of these tools with your own, given that they support popular data interchange formats. Grape is being actively developed at the CRG, and is used in dozens of projects with massive datasets, like the Illumina Body Map Project (HBM) and the ENCODE project.

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Last updated Jan 9th, 2014

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