Compare to data from Lianogluo et al LCLse

Last updated: 2018-12-20

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File	Version	Author	Date	Message
Rmd	d99a6f3	Briana Mittleman	2018-12-20	initializa comparison analysis

The Lianoglou et al paper has data from LCLs as well. I am going to download their high confidence peaks from http://www.polyasite.unibas.ch

“In total, we collected 351,840 Poly(A) sites comprising a total of 4,394,848 reads. We calculated 35.20% of the poly(A) sites, which are 2.68% of all reads, to originate from internal priming.”

library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

LianoglouLCL=read.table("../data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.bed", stringsAsFactors = F, col.names =c("chr", "start", "end", "Status", "Score", "Strand"))

LianoglouLCL %>% group_by(Status) %>% tally()

# A tibble: 3 x 2
  Status      n
  <chr>   <int>
1 IP     123864
2 OK     227975
3 <NA>        1

Filter on the OK peaks.

LianoglouLCL_ok=LianoglouLCL %>% filter(Status=="OK")

My reads in thier Peaks

I can map our reads to these peaks to see what percent of our reads map to these with feature counts. I will need to make this an SAF file.

LianoglouLCLBed2SAF.py

from misc_helper import *

fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK.bed"):
    chrom, start, end, name, score, strand = ln.split()
    chrom_F=chrom[3:]
    start_i=int(start)
    end_i=int(end)
    fout.write("%s\t%s\t%d\t%d\t%s\n"%(name, chrom_F, start_i, end_i, strand))
fout.close()

Feature Counts
LianoglouLCL_FC.sh

#!/bin/bash

#SBATCH --job-name=LianoglouLCL_FC
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=LianoglouLCL_FC.out
#SBATCH --error=LianoglouLCL_FC.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/LianoglouLCL_CleanPeaks.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/LianoglouLCL_CleanPeaks.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2

fix_LianoglouLCL_FC.py

infile= open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/LianoglouLCL_CleanPeaks.Total.fc.summary", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/LianoglouLCL_CleanPeaks.Total.fc_fixed.summary",'w')
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        libraries=[i_list[0]]
        for sample in i_list[1:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        print(libraries)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n' )
    else:
        fout.write(i)
fout.close()

Pull summary onto computer and explore percent of reads mapping to peaks.

Peak Overlap

I can also ask how many of our peaks overlap with theirs.

sed 's/^chr//' /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK.bed > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR.bed

#sort

sort -k 1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR.bed > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR_sort.bed

Remake file in python:

inFile=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR_sort.bed", "r")
outFile=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR_sort_fixed.bed", "w")
for ln in inFile:
  chrom, start, end, stat, score, strand = ln.split()
  start_i=int(start)
  end_i=int(end)
  outFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_i, end_i, stat, score, strand))
outFile.close()

import pybedtools
lian=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ip.OK_noCHR_sort_fixed.bed")
Peak=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed") 

lianOverPeak=Peak.intersect(lian, u=True)

#this only results in one overlap:  
lianOverPeak.saveas("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/myPeaksInLiangoluLCL.bed")

This results in 39213 peaks.

I will look at our peaks, thier peaks and our tracks in IGV.

Next I can look at the peaks that are called at IP in the Lianglou data.


sed 's/^chr//' /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly.bed > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR.bed

#sort

sort -k 1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR.bed > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR_sort.bed

Remake file in python:

inFile=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR_sort.bed", "r")
outFile=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR_sort_fixed.bed", "w")
for ln in inFile:
  chrom, start, end, stat, score, strand = ln.split()
  start_i=int(start)
  end_i=int(end)
  outFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_i, end_i, stat, score, strand))
outFile.close()

import pybedtools
lian=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/SRR1005684.3pSites.highconfidence.ipOnly_noCHR_sort_fixed.bed")
Peak=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed") 

lianOverPeak=Peak.intersect(lian, u=True)

#this only results in one overlap:  
lianOverPeak.saveas("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/myPeaksInLiangoluLCL_IPOnly.bed")

This results in 35700 peaks.

Our peaks are wider and may incompase the ok and IP peaks. Some of these overlap. I will look at how many.

I can ask how many of the OK peaks in our data are also in the IP list of our peaks

import pybedtools
ip=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/myPeaksInLiangoluLCL_IPOnly.bed")
ok=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/myPeaksInLiangoluLCL.bed") 

okoverip=ok.intersect(ip, u=True)

#this only results in one overlap:  
okoverip.saveas("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/myPeaksInLiangoluLCL_OkandIP.bed")

This results in 16459 peaks.

One problem is thier peaks are only one base pair and we have peaks tat are 1 bp away, ex chr7:5,528,801-5,528,844.

Session information

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6    
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.1.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] utf8_1.1.4        rlang_0.2.2       R.oo_1.22.0      
[10] pillar_1.3.0      glue_1.3.0        withr_2.1.2      
[13] R.utils_2.7.0     modelr_0.1.2      readxl_1.1.0     
[16] bindr_0.1.1       plyr_1.8.4        munsell_0.5.0    
[19] gtable_0.2.0      cellranger_1.1.0  rvest_0.3.2      
[22] R.methodsS3_1.7.1 evaluate_0.11     knitr_1.20       
[25] fansi_0.4.0       broom_0.5.0       Rcpp_0.12.19     
[28] scales_1.0.0      backports_1.1.2   jsonlite_1.5     
[31] hms_0.4.2         digest_0.6.17     stringi_1.2.4    
[34] grid_3.5.1        rprojroot_1.3-2   cli_1.0.1        
[37] tools_3.5.1       magrittr_1.5      lazyeval_0.2.1   
[40] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[46] rmarkdown_1.10    httr_1.3.1        rstudioapi_0.8   
[49] R6_2.3.0          nlme_3.1-137      git2r_0.23.0     
[52] compiler_3.5.1

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Compare to data from Lianogluo et al LCLse

Briana Mittleman

12/19/2018

My reads in thier Peaks

Peak Overlap

Session information