Last updated: 2018-07-25

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    File Version Author Date Message
    Rmd d8394a3 Briana Mittleman 2018-07-25 start clean up code analysis

Install new packages:

source("https://bioconductor.org/biocLite.R")
biocLite("BSgenome.Hsapiens.UCSC.hg19")

Load Packages:

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(cleanUpdTSeq)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min
Loading required package: BSgenome
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'
The following object is masked from 'package:base':

    expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: Biostrings
Loading required package: XVector

Attaching package: 'Biostrings'
The following object is masked from 'package:base':

    strsplit
Loading required package: rtracklayer
Loading required package: BSgenome.Drerio.UCSC.danRer7
Loading required package: seqinr

Attaching package: 'seqinr'
The following object is masked from 'package:Biostrings':

    translate
Loading required package: e1071
library(GenomicRanges)
library(BSgenome.Hsapiens.UCSC.hg19)

I am also going to install cleanUpdTSeq on my midway account because I will want to write scripts using this package that can take in any bedfile and will write out the file with the classification results. I can also have the cutoff option be a parameter that will change.

The test set should have chr, start, end, name, score, strand.

#!/bin/rscripts

# usage: ./cleanupdtseq.R in_bedfile, outfile, cuttoff

#this script takes a putative peak file, and output file name and a cuttoff for classification and outputs the file with all of the seqs classified. 

#use optparse for management of input arguments I want to be able to imput the 6up nuc file and write out a filter file  

#script needs to run outside of conda env. should module load R in bash script when I submit it 
library(optparse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(cleanUpdTSeq)
library(GenomicRanges)
library(BSgenome.Hsapiens.UCSC.hg19)


option_list = list(
  make_option(c("-f", "--file"), action="store", default=NA, type='character',
              help="input file"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file"),
  make_option(c("-c", "--cutoff"), action="store", default=NA, type='double',
              help="assignment cuttoff")
)
  

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


#interrupt execution if no file is  supplied
if (is.null(opt$file)){
  print_help(opt_parser)
  stop("Need input file", call.=FALSE)
}

#imput file for test data 
testSet <- read.table(file = opt$file, sep="\t", header=TRUE)
peaks <- BED2GRangesSeq(testSet, withSeq=FALSE)

#build vector with human genome  

testSet.NaiveBayes <- buildFeatureVector(peaks, BSgenomeName=Hsapiens,
                                         upstream=40, downstream=30, 
                                         wordSize=6, alphabet=c("ACGT"),
                                         sampleType="unknown", 
                                         replaceNAdistance=30, 
                                         method="NaiveBayes",
                                         ZeroBasedIndex=1, fetchSeq=TRUE)

#classfy sites with built in classsifer

data(classifier)
testResults <- predictTestSet(testSet.NaiveBayes=testSet.NaiveBayes,
                              classifier=classifier,
                              outputFile=NULL, 
                              assignmentCutoff=opt$cutoff)


#write results  

write.table(testResults, file=opt$output, quote = F, row.names = F, col.names = T)

I will need to module load R in the bash script that writes this.

#!/bin/bash

#SBATCH --job-name=clean_filteredpeakstotal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=clean_filteredpeakstotal.out
#SBATCH --error=clean_filteredpeakstotal.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END


module load R

Rscript cleanupdtseq.R  -f /project2/gilad/briana/threeprimeseq/data/clean.peaks/APAfiltered_named.bed -o /project2/gilad/briana/threeprimeseq/data/clean.peaks/clean_APAfilteredTotal.txt -c .5

#add names to bed file with peaks 
#awk '{print $1 "\t" $2 "\t" $3 "\t" $1 ":" $2 ":" $3 "\t"  $4 "\t"  $5 "\t" $6}' APAfiltered.bed > APAfiltered_named.bed


seq 1 199880 > peak.num.txt
paste APAfiltered.bed peak.num.txt | column -s $'\t' -t > temp
awk '{print $1 "\t" $2 "\t" $3 "\t" $7  "\t"  $4 "\t"  $5 "\t" $6}' temp >  APAfiltered_named.bed
!sq

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] BSgenome.Hsapiens.UCSC.hg19_1.4.0  cleanUpdTSeq_1.18.0               
 [3] e1071_1.6-8                        seqinr_3.4-5                      
 [5] BSgenome.Drerio.UCSC.danRer7_1.4.0 BSgenome_1.48.0                   
 [7] rtracklayer_1.40.3                 Biostrings_2.48.0                 
 [9] XVector_0.20.0                     GenomicRanges_1.32.6              
[11] GenomeInfoDb_1.16.0                IRanges_2.14.10                   
[13] S4Vectors_0.18.3                   BiocGenerics_0.26.0               
[15] workflowr_1.1.1                   

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18                compiler_3.5.1             
 [3] git2r_0.23.0                class_7.3-14               
 [5] R.methodsS3_1.7.1           bitops_1.0-6               
 [7] R.utils_2.6.0               tools_3.5.1                
 [9] zlibbioc_1.26.0             digest_0.6.15              
[11] lattice_0.20-35             evaluate_0.11              
[13] Matrix_1.2-14               DelayedArray_0.6.2         
[15] yaml_2.1.19                 GenomeInfoDbData_1.1.0     
[17] stringr_1.3.1               knitr_1.20                 
[19] ade4_1.7-11                 rprojroot_1.3-2            
[21] grid_3.5.1                  Biobase_2.40.0             
[23] XML_3.98-1.12               BiocParallel_1.14.2        
[25] rmarkdown_1.10              magrittr_1.5               
[27] whisker_0.3-2               MASS_7.3-50                
[29] backports_1.1.2             Rsamtools_1.32.2           
[31] htmltools_0.3.6             matrixStats_0.54.0         
[33] GenomicAlignments_1.16.0    SummarizedExperiment_1.10.1
[35] stringi_1.2.4               RCurl_1.95-4.11            
[37] R.oo_1.22.0

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