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scchess / ensembl_vep_86_with_offline_cache.md
Created Jun 22, 2018 — forked from ckandoth/ensembl_vep_86_with_offline_cache.md
Install Ensembl's VEP v86 with various caches for running offline
View ensembl_vep_86_with_offline_cache.md

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
@scchess
scchess / bash.sh
Created Mar 22, 2018 — forked from jonashackt/bash.sh
Download JRE automatically with wget
View 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
@scchess
scchess / VarScan2_format_converter.py
Created Apr 4, 2017 — forked from PoisonAlien/VarScan2_format_converter.py
Takes output file generated by VarScan2 somatic programme and converts the formats.
View 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")
@scchess
scchess / cancer.R
Created Aug 19, 2016 — forked from timjurka/cancer.R
How to classify breast cancer as benign or malignant using RTextTools.
View 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
View 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.