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## ensembl_vep_86_with_offline_cache.md

      
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                / ensembl_vep_86_with_offline_cache.md
            
            
              Created
              June 22, 2018 10:28
                — forked from ckandoth/ensembl_vep_86_with_offline_cache.md
            
              
                Install Ensembl's VEP v86 with various caches for running offline
              
          
    Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.
To follow these instructions, we'll assume you have these packaged essentials installed:
## For Debian/Ubuntu system admins ##
sudo apt-get install -y build-essential git libncurses-dev

## For RHEL/CentOS system admins ##
sudo yum groupinstall -y 'Development Tools'
sudo yum install -y git ncurses-devel

  
## bash.sh
wget --no-cookies --no-check-certificate --header "Cookie: oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/8u121-b13/e9e7ea248e2c4826b92b3f075a80e441/server-jre-8u121-windows-x64.tar.gz" -O ./server-jre-8u121-windows-x64.tar.gz

## VarScan2_format_converter.py
__author__ = "Anand M"

'''
Takes output file generated by VarScan2 somatic programme and converts the formats.
'''

import argparse, math, re

parser = argparse.ArgumentParser(
    description="Converts VarScan2 somatic vcf to native format and vice-versa.\nInput is automatically detected")

## cancer.R
# FILE: Classifying Breast Cancer as Benign or Malignant
# AUTHOR: Timothy P. Jurka

library(RTextTools);

# GET THE BREAST CANCER DATA FROM http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names
data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",header=FALSE)
data <- data[-1]

# ADD TEXTUAL DESCRIPTORS FOR EACH MASS CHARACTERISTIC FOR THE DOCUMENT-TERM MATRIX

## example-r-markdown.rmd
This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown)
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and
(5) discusses the implications of R Markdown.

## gist:1f96183ec2b8bfb4daac
dasdasdasdas

## gist:2e7fcdf35f2bd2321bf5
asddsaadsdas

## gist:2730500b07422cced38d
dsdasdsads

## gist:3cf0bd7bcfb0045aeca3
sdsasdasd

## gist:4ed5e57b41b50954471f
sdsasdds
	__author__ = "Anand M"

	'''
	Takes output file generated by VarScan2 somatic programme and converts the formats.
	'''

	import argparse, math, re

	parser = argparse.ArgumentParser(
	description="Converts VarScan2 somatic vcf to native format and vice-versa.\nInput is automatically detected")
	# FILE: Classifying Breast Cancer as Benign or Malignant
	# AUTHOR: Timothy P. Jurka

	library(RTextTools);

	# GET THE BREAST CANCER DATA FROM http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names
	data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",header=FALSE)
	data <- data[-1]

	# ADD TEXTUAL DESCRIPTORS FOR EACH MASS CHARACTERISTIC FOR THE DOCUMENT-TERM MATRIX
	This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown)
	using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
	This combination of tools provides an exciting improvement in usability for
	[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
	Specifically, this post
	(1) discusses getting started with R Markdown and `knitr` in Rstudio 0.96;
	(2) provides a basic example of producing console output and plots using R Markdown;
	(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
	(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and
	(5) discusses the implications of R Markdown.