jimbojsb

Step 0:

Get Homebrew installed on your mac if you don't already have it

Step 1:

Install highlight. "brew install highlight". (This brings down Lua and Boost as well)

Step 2:

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

nickloman

rsync -av rsync://ftp.ncbi.nlm.nih.gov/genomes/Bacteria --include "*/" --include "Bacteria/Escherichia*/*.fna" --exclude=* .

# bonus script - concatenate chromosomes and plasmids into single fasta file, make sure the files don't already exist

find . -mindepth 1 -type d | xargs -L 1 -I '{}' find {} -name "*.fna" | while read i ; do cat "$i" >> `dirname "$i"`.fasta ; done

cobyism

Deploying a subfolder to GitHub Pages

Sometimes you want to have a subdirectory on the master branch be the root directory of a repository’s gh-pages branch. This is useful for things like sites developed with Yeoman, or if you have a Jekyll site contained in the master branch alongside the rest of your code.

For the sake of this example, let’s pretend the subfolder containing your site is named dist.

Step 1

Remove the dist directory from the project’s .gitignore file (it’s ignored by default by Yeoman).

stephenturner

## See http://gettinggeneticsdone.blogspot.com/2013/06/customize-rprofile.html

## Load packages
library(BiocInstaller)

## Don't show those silly significanct stars
options(show.signif.stars=FALSE)

## Do you want to automatically convert strings to factor variables in a data.frame?
## WARNING!!! This makes your code less portable/reproducible.

seandavi

idat2lumibatch <- function(filenames) {
  # filenames is a character vector of iDAT filenames
  require(illuminaio)
  require(lumi)
  idatlist = lapply(filenames,readIDAT)
  exprs = sapply(idatlist,function(x) {
    return(x$Quants$MeanBinData)})
  se.exprs = sapply(idatlist,function(x) {
    return(x$Quants$DevBinData/sqrt(x$Quants$NumGoodBeadsBinData))})
  beadNum = sapply(idatlist,function(x) {

arunsrinivasan

require(reshape2)

# data.table commit (1048)
require(data.table)
# Loading required package: data.table
# data.table 1.8.11  For help type: help("data.table")

set.seed(1)
N <- 2e7 # size of DT

arq5x

export SAMPLES="2484-AJ-0001 2484-AJ-0002 2484-AJ-0003"

######################################
# Make FASTQ
######################################
export OVHOME=/home/arq5x/cphg-home/cphg-quinlan/projects/ov-cell-lines
export STEPNAME=ovc-fastq
for sample in `echo $SAMPLES`
do
export QSUB="qsub -W group_list=cphg_arq5x -q arq5xlab -V -l select=1:mem=8000m:ncpus=1 -N $STEPNAME -m bea -M arq5x@virginia.edu";

seandavi

Usage

To use these scripts:

• Clone this repository: git clone https://gist.github.com/95a4b2ab3b90f6f0bfd9.git snpEffScript
• cd snpEffScript
• make appropriate changes to setup.sh
• call snpEff.sh like so:

vsbuffalo

# A quick function to save a PBM (http://en.wikipedia.org/wiki/Netpbm_format)
# visualize *a lot* of missing data pretty quickly (outside of R).

writeMissingPBM <- function(x, file) {
  dims <- dim(x)
  x[] <- as.integer(is.na(x))
  con <- file(file, open="wt")
  writeLines(sprintf("P1\n%d %d", ncol(x), nrow(x)), con)
  write.table(x, file=con, sep=" ", col.names=FALSE, row.names=FALSE, quote=FALSE)
  close(con)