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package biotools.ro1;

/**
 * Created by hd on 9/9/14.
 */
public class Gene {

    private String geneA;
    private String geneB;

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>HSBGPG Human gene for bone gla protein (BGP)
GGCAGATTCCCCCTAGACCCGCCCGCACCATGGTCAGGCATGCCCCTCCTCATCGCTGGGCACAGCCCAGAGGGT
ATAAACAGTGCTGGAGGCTGGCGGGGCAGGCCAGCTGAGTCCTGAGCAGCAGCCCAGCGCAGCCACCGAGACACC
ATGAGAGCCCTCACACTCCTCGCCCTATTGGCCCTGGCCGCACTTTGCATCGCTGGCCAGGCAGGTGAGTGCCCC
CACCTCCCCTCAGGCCGCATTGCAGTGGGGGCTGAGAGGAGGAAGCACCATGGCCCACCTCTTCTCACCCCTTTG
GCTGGCAGTCCCTTTGCAGTCTAACCACCTTGTTGCAGGCTCAATCCATTTGCCCCAGCTCTGCCCTTGCAGAGG
GAGAGGAGGGAAGAGCAAGCTGCCCGAGACGCAGGGGAAGGAGGATGAGGGCCCTGGGGATGAGCTGGGGTGAAC
CAGGCTCCCTTTCCTTTGCAGGTGCGAAGCCCAGCGGTGCAGAGTCCAGCAAAGGTGCAGGTATGAGGATGGACC
TGATGGGTTCCTGGACCCTCCCCTCTCACCCTGGTCCCTCAGTCTCATTCCCCCACTCCTGCCACCTCCTGTCTG
GCCATCAGGAAGGCCAGCCTGCTCCCCACCTGATCCTCCCAAACCCAGAGCCACCTGATGCCTGCCCCTCTGCTC

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// We include what we need for the test
#include <gatb/gatb_core.hpp>
/********************************************************************************/
/*                      Graph nodes iteration                                   */
/********************************************************************************/
int main (int argc, char* argv[])
{
    // We check that the user provides at least one option (supposed to be in HDF5 format).
    if (argc < 2)
    {

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int main(int argc, char* argv[]) {
    
    BNLearner learner(argv[1]); 
    learner.useGreedyHillClimbing();

    gum::BayesNet<float> bn = learner.learnBN();

    cout << "Structure learning" << endl;
    cout << "------------------" << endl;
    const gum::Potential<float>& p1 = bn.cpt(bn.idFromName("lung_cancer?"));

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int main(int argc, char* argv[]) {
    
    namespace spd = spdlog;

    try {
        //Create console, multithreaded logger
        auto console = spd::stdout_logger_mt("console");
        console->info("Bayesian Network Build");

        int k = atoi(argv[2]);

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#!/bin/sh
#SBATCH --time=24:00:00          # Run time in hh:mm:ss
#SBATCH --mem-per-cpu=16384       # Minimum memory required per CPU (in megabytes)
#SBATCH --job-name=bnp-bootstrap
#SBATCH --error=/work/otu/hdogan/job.%J.err
#SBATCH --output=/work/otu/hdogan/job.%J.out

Rscript bootstrap.R data.csv

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library(bnlearn)

options(echo=TRUE)
args <- commandArgs(trailingOnly = TRUE)
print(args)

data <- read.table(args[1], sep = ",", header = TRUE)
col_names <- names(data)
data[, col_names] <- lapply(data[,col_names] , factor)

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###### S1400707GEM_1
.
├── $RECYCLE.BIN
│   └── S-1-5-21-4038060156-87135356-2143169266-1001
│       └── desktop.ini
├── Configuration.xml
├── Eclipse
│   └── workspace
│       └── snpAnnotationForBacteria
│           ├── SAM18146101_s_1.out-Variants-snpEff-test.txt

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[0.000000] Reading FastQ file s_197_1_extracted_reads.fastq;
[392.937697] 24921054 sequences found
[392.937699] Done
[393.003596] Reading FastQ file s_197_2_extracted_reads.fastq;
[717.206876] 24921054 sequences found
[717.206878] Done
[717.206927] Reading read set file output_assemble//Sequences;
[726.800873] 49842108 sequences found
[784.617406] Done
[784.617411] 49842108 sequences in total.

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# views.py
# ========
def add_file(request, project_id):
    if request.method == 'POST':
        form = VennCsvForm(request.POST, request.FILES)
        if form.is_valid():
            new_file = VennCsv(file=request.FILES['file'])
            new_file.user = request.user
            new_file.project = get_object_or_404(VennProject, pk=project_id)
            new_file.save()