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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module Imports"
   ]
  },
  {

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Guidelines for conducting and participating in meetings

Meeting etiquette

• Presenters should prepare prior to the meeting. Preparations might include:
  • Carefully reviewing materials to be presented for completeness, accuracy, and adherence to guidelines (discussed in sections below).
  • Independently or with others, going through the presentation as if you were presenting it.
  • Saving materials on OneDrive and sharing them prior to the meeting (discussed in sections below).
• Meeting organizers should set an agenda and email the agenda prior to the start of the meeting by replying to the meeting invite.

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# data
/data

# History files
.Rhistory
.Rapp.history

# Session Data files
.RData
.RDataTmp

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docker run -u $UID:$UID  -v $PWD:$PWD -w $PWD ensemblorg/ensembl-vep perl /opt/vep/src/ensembl-vep/INSTALL.pl -a cf -c $PWD/vep_data -s homo_sapiens -y GRCh38

git clone https://github.com/Ensembl/VEP_plugins $PWD/vep_data/Plugins

docker run -i -t -u $UID:$UID -v $PWD:$PWD:rw -v $PWD/vep_data:/opt/vep/.vep  -w $PWD ensemblorg/ensembl-vep vep --verbose  --everything --humdiv --check_existing --species homo_sapiens --assembly GRCh38 --cache --dir $PWD/vep_data -i $PWD/fermikit.flt.vcf -o $PWD/test

# regulatory build gff
ftp://ftp.ensembl.org/pub/release-94/regulation/homo_sapiens/homo_sapiens.GRCh38.Regulatory_Build.regulatory_features.20161111.gff.gz

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#!/usr/bin/env bash

apt-get update
add-apt-repository -y -u ppa:certbot/certbot

packages="
csh
gdebi-core
openjdk-8-jdk
python-certbot-nginx

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user www-data;
worker_processes auto;
pid /run/nginx.pid;

events {
	worker_connections 768;
}

http {

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user www-data;
worker_processes auto;
pid /run/nginx.pid;


events {
	worker_connections 768;
	# multi_accept on;
}

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source("https://raw.githubusercontent.com/dvera/mingle/master/R/bamToImat.R")

binsize=100000

x=bamToImat(bams,binsize,minInteractions=1,threads=12)

testsa=lapply(1:length(x),function(i){
  ss=summary(x[[i]][[1]])
  ss=data.frame(dist=binsize*(ss[,2]-ss[,1]),count=ss[,3])
  return(ss)

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matPlot <- function(
	scoreMats ,
	outdir ,
	matnames=NULL ,
# per-clustering settings
	sortOn=1 ,
	sortMethods="kmeans" ,
	sortRanges = NULL ,
	sortBreaks = NULL ,
	sortGroups=3 ,