felipealbrecht

library(DeepBlueR)
library(gplots)
library(RColorBrewer)
library(matrixStats)
library(stringr)

#crest_data <- deepblue_list_experiments(project = "CREST", genome="grch38", epigenetic_mark = "DNA Methylation")

#crest_data <- crest_data[grep("_call", deepblue_extract_names(crest_data)),]

felipealbrecht

import os
import xmlrpclib
import time

import StringIO
import gzip

url = "http://deepblue.mpi-inf.mpg.de/xmlrpc"
genome = "grch38"
user_key = "anonymous_key"

felipealbrecht

#Our objective is to extract the average DNA methylation (beta value) in all regulatory elements defined in the BLUEPRINT regulatory build (modified from~\cite{Zerbino2015}) across all BLUEPRINT samples. After downloading these data from DeepBlue we use the package gplots to create a heatmap showing the most variable regions (rows) across samples (columns). Moreover, we cluster samples by their pairwise Spearman correlation coefficients (Figure~\ref{Fig:1}). 
#In Listing 1 we demonstrate how DeepBlueR can be used to quickly and efficiently aggregate a large data set on the DeepBlue server in preparation for downstream analysis in R. We omit the R code for generating the heatmap here. It can be found in the supplemental material. To retrieve the required data, we first select all bisulfite sequencing experiments from the BLUEPRINT project (lines $5$-$9$). Our selection comprises $206$ data files and $47$ distinct cell types after filtering for the correct file type (lines $9$-$11$). For each file, we select th

felipealbrecht

# Load dependencies
# install DeepBlueR from bioconductor 
# http://bioconductor.org/packages/release/bioc/html/DeepBlueR.html
library(DeepBlueR)
library(dplyr)
library(tidyr)

# List all BLUEPRINT samples
blueprint_samples <- deepblue_list_samples(
    extra_metadata = list("source" = "BLUEPRINT Epigenome"))

felipealbrecht

import xmlrpclib
import time

url = "http://deepblue.mpi-inf.mpg.de/xmlrpc"
user_key = "anonymous_key"

server = xmlrpclib.Server(url, allow_none=True)

GENOME = "hg19"

felipealbrecht

DeepBlue Epigenomic Data Server change log

Version 1.7.0 - (04.04.2016)

• Stable and published versions.

Version 1.7.3 - (19.04.2016)

• Command select_genes supports filtering genes by chromosome location. This version breake old code where the select_genes were used. For fixinging it, just include Null,Null, and Null, after the gene_set parameter. Examples and code were updated.

Version 1.7.5 - (27.04.2016)

felipealbrecht

import random

def mp(Matrix):
	for m in Matrix:
		print m

def fill(Matrix, pos, start_x, end_x, start_y, end_y):

	if (start_x == end_x) or (start_y == end_y):
		return

felipealbrecht

import xmlrpclib

url = "http://deepblue.mpi-inf.mpg.de/xmlrpc"
user_key = "anonymous_key"

server = xmlrpclib.Server(url, allow_none=True)

# List all peak experiments from the biosources "inflammatory macrophage" and "macrophage" from the Blueprint project
(status, experiments) = server.list_experiments(None, "peaks", "H3K4me3", ["inflammatory macrophage", "macrophage"], None, None, "BLUEPRINT Epigenome", user_key)

felipealbrecht

#include <iostream>
#include <vector>

typedef std::vector<int> Array;

int find_first_ocorrece_where_value_is_higher(Array& a, int value);

int main() {
	Array v = {0, 10, 20, 30, 40, 50};

felipealbrecht

// The frog start at position -1, the river starts at position 0, the frog must go to position river size (path) or bigger
// Compile with c++11, e.g. in clang++: clang++ frog.cpp -std=c++11

#include <vector>
#include <iostream>
#include <tuple>

typedef std::vector<int> Path;
typedef std::vector<std::tuple<int, int> > Visited;