Bruno Grande BrunoGrandePhD

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.

Instead of the official instructions, we will use conda to install VEP and its dependencies. If you don't already have conda, install it into $HOME/miniconda3 as follows:

curl -sL https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh -o /tmp/miniconda.sh
sh /tmp/miniconda.sh -bfp $HOME/miniconda3

Add the conda bin folder into your $PATH so that all installed tools are accessible via command-line. You can also add this to your ~/.bashrc

	#! /bin/bash
	# Script to execute a SQL query through awscli

	set -ex

	CLUSTER_ARN='arn:aws:rds:us-east-1:035458030717:cluster:tower'
	SECRET_ARN=$(aws secretsmanager describe-secret \
	--secret-id nextflow-aurora-mysql-MasterSecret \| jq -r .ARN)

	QUERY="${1}"

	require(caret)

	#load some data
	data(USArrests)

	### Prepare Data (postive observations)
	# add a column to be the strata. In this case it is states, it can be sites, or other locations
	# the original data has 50 rows, so this adds a state label to 10 consecutive observations
	USArrests$state <- c(rep(c("PA","MD","DE","NY","NJ"), each = 5))
	# this replaces the existing rownames (states) with a simple numerical index