Last updated: 2019-03-07

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    Modified:   code/Snakefile

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File Version Author Date Message
Rmd 72c34ce Briana Mittleman 2019-03-07 signal site loc based on front side
html 4bf5d09 Briana Mittleman 2019-03-06 Build site.
Rmd 8717550 Briana Mittleman 2019-03-06 res for AATAAA
html 4023fe0 Briana Mittleman 2019-03-06 Build site.
Rmd d561190 Briana Mittleman 2019-03-06 analysis up to getting seqs
html ba63ea2 Briana Mittleman 2019-03-06 Build site.
Rmd c200503 Briana Mittleman 2019-03-06 add signal site loc analysis

In the Signal Site enrichment analysis I looked at the peaks to see if signal sites are enriched upstream of my peaks. I found this is true but now I want to see where the signal sites are in comparison to my peaks. I am going to use the biostrings package tool matchPWM for this analysis.

library(workflowr)
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library(Biostrings)
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Make location file

I need to get the coordinates for the regions I care about. I want to look at the peak and 150bp upstream. This is probably larger than I will need to look at but it will be good to have an inclusive look first.

I want to use the peak file and make a file that is the peak and upstream 150:

Upstream150Bases.py

#python  
def main(Fin, Fout):
  outBed=open(Fout, "w")
  chrom_lengths=open("/project2/gilad/briana/genome_anotation_data/chrom_lengths2.sort.bed","r")
  #make a dictionary with chrom lengths
  length_dic={}
  for i in chrom_lengths:
    chrom, start, end = i.split()
    length_dic[str(chrom)]=int(end)  

#write file 
  for ln in open(Fin):
    chrom, start, end, name, score, strand = ln.split()
    chrom=str(chrom)
    if strand=="+":
      start_new=int(start)-150
      if start_new <= 1:
        start_new = 0 
      end_new= int(end)
      if end_new == 0:
        end_new=1
      outBed.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_new, end_new, name, score, strand))
    if strand == "-":
      start_new=int(start)
      end_new=int(end) + 150
      outBed.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_new, end_new, name, score, strand))
  outBed.close()  

if __name__ == "__main__":
    import sys
    inFile = sys.argv[1]
    outFile=sys.argv[2] 
    main(inFile, outFile)

run_get150up.sh

#!/bin/bash

#SBATCH --job-name=run_get150up
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=run_get150upt.out
#SBATCH --error=run_get150up.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

python Upstream150Bases.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed  /project2/gilad/briana/threeprimeseq/data/Signal_Loc/APAPeaks_5percCov_fixedStrand_peakand150up.bed

Get subject (reads)

Input the regions:

Fix chromosomes:

PeakRegions=read.table("../data/Signal_Loc/APAPeaks_5percCov_fixedStrand_peakand150up.bed", header=F,col.names = c("chr","start", "end", "peak", "score", "strand")) %>% mutate(Chrom=paste("chr", chr, sep="")) %>% select(Chrom, start,end,peak,score,strand)
write.table(PeakRegions, file="../data/Signal_Loc/APAPeaks_5percCov_fixedStrand_peakand150up_fixedChr.bed", quote=F, col.names = F, row.names = F, sep="\t")
#convert to reads 

reads.GR= readGeneric(file="../data/Signal_Loc/APAPeaks_5percCov_fixedStrand_peakand150up_fixedChr.bed",chr =1, start = 2, end =3,  meta.cols =4, header=F, zero.based=TRUE,strand=6)

I need to overlap these positions with the genome

Make motifs

AATAAA= PWM("AATAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
ATTAAA= PWM("ATTAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
AGTAAA= PWM("AGTAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
TATAAA= PWM("TATAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
CATAAA= PWM("CATAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
GATAAA= PWM("GATAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
AATATA= PWM("AATATA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
AATACA= PWM("AATACA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
AATAGA= PWM("AATAGA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
AAAAAG= PWM("AAAAAG", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))
ACTAAA= PWM("ACTAAA", type = c("log2probratio", "prob"), prior.params = c(A=0.25, C=0.25, G=0.25, T=0.25))

find the mathes

genome.hg19 <- getBSgenome("BSgenome.Hsapiens.UCSC.hg19")

#matches <- matchPWM(pwm=AATAAA, subject = genome.hg19) %>% keepStandardChromosomes(., species= "Homo sapiens")


DNAstringSetPeaks=data.frame(seq=getSeq(genome.hg19, reads.GR))

x=DNAString(DNAstringSetPeaks[1,1])

hits <- matchPWM(AATAAA,x,with.score=T) 


start(hits)
[1] 215 219 255

Look over and make hits file for all

list_AATAAA_res=c()
for (i in 1:nrow(DNAstringSetPeaks)){
  x=DNAString(DNAstringSetPeaks[i,1])
  list_AATAAA_res=c(list_AATAAA_res,matchPWM(AATAAA,x,with.score=T))
}

Get out the start positions:

starts_AATAAA=c()
nsig=c()
last_oc_AATAAA=c()
for (i in list_AATAAA_res){
  nsig=c(nsig, length(start(i)))
  starts_AATAAA=c(starts_AATAAA, start(i))
  #print(length(start(i)))
  if (length(start(i)) != 0 ){
    last_oc_AATAAA=c(last_oc_AATAAA, max(start(i),na.rm =T))
  }
}

Histogram of results:

summary(starts_AATAAA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    1.0    79.0   175.0   156.7   216.0  1612.0 
hist(starts_AATAAA,breaks=10000)

Version Author Date
4bf5d09 Briana Mittleman 2019-03-06 
summary(nsig)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   1.000   3.000   3.857   5.000  74.000 
sum(nsig==0)
[1] 3584
sum(nsig==1)
[1] 8358
hist(nsig,breaks=100)

Version Author Date
4bf5d09 Briana Mittleman 2019-03-06

Look at the first occurence:

summary(last_oc_AATAAA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    1.0   209.0   215.0   213.1   231.0  1612.0 
hist(last_oc_AATAAA,breaks=1000)

Version Author Date
4bf5d09 Briana Mittleman 2019-03-06
I want to get the closest occurance of the 

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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] grid      stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] BSgenome.Hsapiens.UCSC.hg19_1.4.0 genomation_1.14.0                
 [3] BSgenome_1.50.0                   rtracklayer_1.42.2               
 [5] GenomicRanges_1.34.0              GenomeInfoDb_1.18.2              
 [7] Biostrings_2.50.2                 XVector_0.22.0                   
 [9] IRanges_2.16.0                    S4Vectors_0.20.1                 
[11] BiocGenerics_0.28.0               forcats_0.4.0                    
[13] stringr_1.4.0                     dplyr_0.8.0.1                    
[15] purrr_0.3.1                       readr_1.3.1                      
[17] tidyr_0.8.3                       tibble_2.0.1                     
[19] ggplot2_3.1.0                     tidyverse_1.2.1                  
[21] workflowr_1.2.0                  

loaded via a namespace (and not attached):
 [1] nlme_3.1-137                bitops_1.0-6               
 [3] matrixStats_0.54.0          fs_1.2.6                   
 [5] lubridate_1.7.4             httr_1.4.0                 
 [7] rprojroot_1.3-2             tools_3.5.1                
 [9] backports_1.1.3             R6_2.4.0                   
[11] KernSmooth_2.23-15          lazyeval_0.2.1             
[13] colorspace_1.4-0            seqPattern_1.14.0          
[15] withr_2.1.2                 tidyselect_0.2.5           
[17] compiler_3.5.1              git2r_0.24.0               
[19] cli_1.0.1                   rvest_0.3.2                
[21] Biobase_2.42.0              xml2_1.2.0                 
[23] DelayedArray_0.8.0          scales_1.0.0               
[25] digest_0.6.18               Rsamtools_1.34.1           
[27] rmarkdown_1.11              pkgconfig_2.0.2            
[29] htmltools_0.3.6             plotrix_3.7-4              
[31] rlang_0.3.1                 readxl_1.3.0               
[33] rstudioapi_0.9.0            impute_1.56.0              
[35] generics_0.0.2              jsonlite_1.6               
[37] BiocParallel_1.16.6         RCurl_1.95-4.12            
[39] magrittr_1.5                GenomeInfoDbData_1.2.0     
[41] Matrix_1.2-15               Rcpp_1.0.0                 
[43] munsell_0.5.0               reticulate_1.11.1          
[45] stringi_1.3.1               whisker_0.3-2              
[47] yaml_2.2.0                  SummarizedExperiment_1.12.0
[49] zlibbioc_1.28.0             plyr_1.8.4                 
[51] crayon_1.3.4                lattice_0.20-38            
[53] haven_2.1.0                 hms_0.4.2                  
[55] knitr_1.21                  pillar_1.3.1               
[57] reshape2_1.4.3              XML_3.98-1.19              
[59] glue_1.3.0                  evaluate_0.13              
[61] data.table_1.12.0           modelr_0.1.4               
[63] cellranger_1.1.0            gtable_0.2.0               
[65] assertthat_0.2.0            xfun_0.5                   
[67] gridBase_0.4-7              broom_0.5.1                
[69] GenomicAlignments_1.18.1