Skip to main content

ngsplot ngs.plot tutorial



#!/bin/sh
#
#SBATCH --job-name=testJob
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --partition=dragon-default
#SBATCH --ntasks-per-node=1
#SBATCH --mem=8192
#
# Display all variables set by slurm
env | grep "^SLURM" | sort

## run with command: sbatch NGSPLOT_test.sh

#module load gcc/4.7.4
#module load  compilers-extra
#module load samtools/1.1
#module load bwtool/1.0

module purge
module load slurm
module load gcc/4.7.4
module load R/3.1.1
module load ngsplot

dataDir="/home/alamt/HMCan_test/"
workDir=/home/alamt/NGSPLOT/

cd $workDir


hg19size=hg19.chrom.sizes

tfName="MEF_2A"

bedFile="F5_robust_DPI_TSS_500_upstream_500_downstream_nonCoding_only.bed"


###############################
##### 1. Run ngs.plot.r
##### Mandatory parameters:
#  -G   Genome name. Use ngsplotdb.py list to show available genomes.
#  -R   Genomic regions to plot: tss, tes, genebody, exon, cgi, enhancer, dhs or bed
#  -C   Indexed bam file or a configuration file for multiplot
#  -O   Name for output: multiple files will be generated
##### Optional parameters related to configuration file:
#  -E   Gene list to subset regions OR bed file for custom region
#  -T   Image title
###############################

ngs.plot.r  -G hg19 -R bed  -C $dataDir/wgEncodeHaibTfbs/wgEncodeHaibTfbsGm12878Mef2aPcr1xAlnRep1.bam:$dataDir/wgEncodeSydhTfbs/wgEncodeSydhTfbsGm12878InputStdAlnRep1.bam  -O  MEF_2A_NGSPLOT.txt -T $tfName -E $bedFile

Comments

Post a Comment

Popular posts from this blog

MATLAB cross validation

// use built-in function samplesize = size( matrix , 1); c = cvpartition(samplesize,  'kfold' , k); % return the indexes on each fold ///// output in matlab console K-fold cross validation partition              N: 10    NumTestSets: 4      TrainSize: 8  7  7  8       TestSize: 2  3  3  2 ////////////////////// for i=1 : k    trainIdxs = find(training(c,i) ); %training(c,i);  // 1 means in train , 0 means in test    testInxs  = find(test(c,i)       ); % test(c,i);       // 1 means in test , 0 means in train    trainMatrix = matrix (  matrix(trainIdxs ), : );    testMatrix  = matrix (  matrix(testIdxs  ), : ); end //// now calculate performance %%  calculate performance of a partiti...
5 comments
Read more

MATLAB optimization toolbox usage with genetic algorithm

Useful tutorial http://www.mathworks.com/products/global-optimization/description3.html Best example of implementatoin with Constraint, objective function http://www.mathworks.com/help/gads/examples/constrained-minimization-using-the-genetic-algorithm.html More about how to use multi-objective http://www.mathworks.com/discovery/multiobjective-optimization.html http://www.mathworks.com/help/gads/examples/performing-a-multiobjective-optimization-using-the-genetic-algorithm.html http://www.mathworks.com/help/gads/examples/multiobjective-genetic-algorithm-options.html Example GAMULTOBJ (can handle Multiple Objective)  GA(can handle 1 objective) Constrained Minimization Problem We want to minimize a simple fitness function of two variables x1 and x2 min f(x) = 100 * (x1^2 - x2) ^2 + (1 - x1)^2; x min f(x) = 100 * (x1^2 + x2) ^2 + (1 + x1)^2; x such that the following two nonlinear constraints and bounds are satisfied x1*x2 + x1 - x2 + 1.5 <...
1 comment
Read more

Feature subset selection Using Genetic Algorithm in MATLAB

function callGeneticAlgo global mat global trainInd global testInd [trainInd,~,testInd] = dividerand(1420,0.7,0,0.3); global counter global errList counter = 1; errList = []; fileName=  '../features/alltopPNPDMF.feature' ; mat = load(fileName); [x,fval,exitflag,output,population,score] = gaFeaSelection(1588,100,10800); % param1 = #feature excludig label % param2 =  population size % param3 = sec to test (3 hour = 10800 sec) dlmwrite('selected.GA',x,'delimiter','\n'); display('Done'); end function [x,fval,exitflag,output,population,score] = gaFeaSelection (nvars,PopulationSize_Data,TimeLimit_Data) % This is an auto generated MATLAB file from Optimization Tool. % Start with the default options options = gaoptimset; % Modify options setting options = gaoptimset(options,'PopulationType', 'bitString'); options = gaoptimset(options,'PopulationSize', PopulationSize_Data); options = gaoptimset(options,'TimeLimit', T...
2 comments
Read more