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## Installing Virtual Environment and Eggifying your source code for OERPub

Go to your Repo : cd <repo_name>
and then :
virtualenv --no-site-packages .
./bin/easy_install -U ZopeSkel
./bin/paster create --list-templates
./bin/paster create -t nested_namespace <repo_name>

This will create and empty egg for your repo !

saketkc

import gdata.docs.data
import gdata.docs.client
import gdata.docs.service
import gdata.client
import gdata.docs.client
import os
import stat
import  mimetypes
from google_api_config import CONSUMER_KEY
from google_api_config import CONSUMER_SECRET

saketkc

#!/usr/bin/env python
'''
Automatically estimate insert size of the paired-end reads for a given SAM/BAM file.
Usage: getinsertsize.py <SAM file> or samtools view <BAM file> | getinsertsize.py -
Author: Wei Li
'''


from __future__ import print_function
import sys;

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<tool id="panama1" name="panama"  version="1.0.0">
    <requirements>
        <requirement type="package">panama</requirement>
    </requirements>
    <description>eQTL Analysis using PANAMA(http://ml.sheffield.ac.uk/qtl/panama/)</description>
    <command>
        panama $expression_data $snp_data; mv PANAMA_results.csv output1 
    </command>
    <inputs>
        <param name="expression_data" type="data" format="text" label="Select Expression Data" />

saketkc

#!/usr/bin/env python
"""
Run panama on expression and snp data
usage: panama_run.py [options]
   --exp_data: Expression data CSV file
   --snp_data: SNP data file
   --output1: Output CSV dataset
"""

import optparse, os, sys, subprocess, tempfile, shutil

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<tool id="panama1" name="panama"  version="1.0.0">
    <requirements>
        <requirement type="package">panama</requirement>
    </requirements>
    <description>eQTL Analysis using PANAMA(http://ml.sheffield.ac.uk/qtl/panama/)</description>
    <command interpreter="python">
        panama_run.py --exp_data=$expression_data --snp_data=$snp_data --output1=$output1
    </command>
    <inputs>
        <param name="expression_data" type="data" format="text" label="Select Expression Data" />

saketkc

library(limma)
Cy5 <- "F635 Median"
Cy5b <- "B635 Mean"
targets <- readTargets("Grade2_targets.csv")
f <- factor(targets$Condition, levels = unique(targets$Condition))
design <- model.matrix(~0 + f)
cont.matrix <- makeContrasts(Grade2vsControl=Grade2-Control, levels=design)
colnames(design) <- levels(f)

RG <- read.maimages( targets$FileName,

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FileName	Condition
Control_H-02_2000153953.gpr	Control
Control_H-25_2000153952.gpr	Control
Control_H-35_2000153943.gpr	Control
Control_H-58_2000153935.gpr	Control
Control_HC-25_2000153932.gpr	Control
Grade II_CF 10729_2000154012.gpr	Grade2
Grade II_CF 20468_2000153947.gpr	Grade2
Grade II_CF 29538_2000154020.gpr	Grade2
Grade II_CF 8551_2000153940.gpr	Grade2

saketkc

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

saketkc

name: multiz
channels: !!python/object/new:ruamel_yaml.comments.CommentedSeq
  listitems:
  - bioconda
  - r
  - defaults
  state: !!python/tuple
  - _yaml_format: !!python/object:ruamel_yaml.comments.Format
      _flow_style: false
    _yaml_line_col: !!python/object:ruamel_yaml.comments.LineCol