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setwd("/home/bk/Desktop/hg38")

dta <- read.csv("L1.6kb.CpG.rel.pos.csv",header = F, sep = ",")
dim(dta)
dta.t <- t(dta[,-1])
dta.t <- dta.t[,colSums(is.na(dta.t))<nrow(dta.t)]
nc <- ncol(dta.t)

res <- lapply(1:nc,function(i) {
  h<-hist(dta.t[,i], plot=F, breaks = 10)

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# For RNA-seq reads, use "--dta/--downstream-transcriptome-assembly" 
# the command is similar to 'bowtie2'

$ hisat2 -x genome -1 read1.fq.gz -2 read2.fq.gz -S Sample.sam -p 8 --dta  2>&1 | tee $l.stat 

$ samtools view -bS Sample.sam | samtools sort -@ 8 - Sample.sorted


# The bam files can be fed into other pipelines e.g DESeq, edgeR, and etc

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#!/usr/bin/env python2
import re, csv, sys, os, glob, warnings, itertools
from math import ceil
from optparse import OptionParser
from operator import itemgetter

MIN_PYTHON = (2, 7)
if sys.version_info < MIN_PYTHON:
    sys.exit("Python %s.%s or later is required.\n" % MIN_PYTHON)

mbk0asis

library(DESeq2)
library(pcaExplorer)
library(ggplot2)
library(gplots)
library(ggrepel)
library(reshape2)

# set working directory
setwd('~/00-NGS/SETDB1_TCGA/GSE40419/')

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1. Google 'GSE40419' (GEO accession number)

2. Look for 'BioProject' or 'SRA' ID (RJNA173917 or ERP001058)

3. Go to 'SRA Run Selector' 

4. Enter "RJNA173917" or "ERP001058" in the search.

5. Choose samples to download.

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computeMatrix scale-regions -S 2m*.bw 20m*.bw 28m*.bw -R repeatMasker_mm10/ERV1.bed -o Muscle.ERV1.matrix.gz --skipZeros -bs 10 -a 1000 -b 1000 --regionBodyLength 2000 -bl blackList_LINE.LTR

plotHeatmap --whatToShow 'heatmap and colorbar' -m Muscle.ERV1.matrix.gz -out test.Muscle.ERV1.Heatmap.png --samplesLabel 2m-1 2m-2 20m-1 20m-3 28m-1 28m-2 --heatmapHeight 10 --heatmapWidth 1.5 --xAxisLabel "" --regionsLabel "" --startLabel "s" --endLabel "e" --colorList 'white,black'

plotProfile -m  Muscle.LINE.bin500.matrix.gz -out Muscle.LINE.bin500.Heatmap.pdf --plotFileFormat pdf --plotHeight 8 --perGroup --averageType mean --colors black red blue --plotType se --startLabel "start" --endLabel "end" --regionsLabel ""

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####################################################################
# Load a count data

countsTable <- read.csv("counts.csv",row.names=1, header=T)
head(countsTable)
dim(countsTable)

####################################################################
# run DESeq2

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# deepTools - command line
# Use "--outFileNameMatrix" option to export data set (data are already oriented by strand)
    
$ computeMatrix scale-regions -p 40 -S low/*.bw -R ~/00-NGS/SETDB1_TCGA/GSE40419/promoter.10kb.bed 
-o SETDB1.low.Matrix.gz --skipZeros -bs 50 
-a 1000 -b 1000 --regionBodyLength 5000 
-bl ~/00-NGS/SETDB1_TCGA/GSE40419/blackList_LINE.LTR 
--outFileNameMatrix SETDB1.low.Matrix.tab

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#source("https://bioconductor.org/biocLite.R")
#biocLite("TCGAbiolinks")

library(TCGAbiolinks)
library(data.table)
library(dplyr)
library(DT)

############################################################
# Clinical data 

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#source("https://bioconductor.org/biocLite.R")
#biocLite("org.Mm.eg.db")

library("DESeq2")
library("calibrate")
library("pcaExplorer")
library("ggplot2")
library("gplots")
library("ggrepel")
library("gage")