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<h1 class="title toc-ignore">“A” content in each mapped read</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>6/11/2018</em></h4>
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<p><strong>Last updated:</strong> 2018-06-15</p>
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signature of mult nucleotide analysus
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start A analysis
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<p></details></p>
<hr />
<p>The goal of this analysis is to start to understand the sequence composition of the three prime seq reads. This may help me detect misspriming at AAAAA rich regions rather than true site usage. The genomic sequence does not carry the polyadenylation signal, this means reads mapping to a genomic AAAAAA region may be false positives. Gruber et al. removed reads that consisted of more than 80% AAAA.</p>
<div id="percent-nucleotide-in-bins" class="section level2">
<h2>Percent nucleotide in bins</h2>
<p>One method is to measure the number of AAAAAs in my bins with bedtools nuc. I willl need a fasta file and a bed file with the bin.</p>
<pre class="r"><code>library(workflowr)</code></pre>
<pre><code>Loading required package: rmarkdown</code></pre>
<pre><code>This is workflowr version 1.0.1
Run ?workflowr for help getting started</code></pre>
<pre class="r"><code>library(ggplot2)
library(dplyr)</code></pre>
<pre><code>Warning: package 'dplyr' was built under R version 3.4.4</code></pre>
<pre><code>
Attaching package: 'dplyr'</code></pre>
<pre><code>The following objects are masked from 'package:stats':
filter, lag</code></pre>
<pre><code>The following objects are masked from 'package:base':
intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(cowplot)</code></pre>
<pre><code>Warning: package 'cowplot' was built under R version 3.4.3</code></pre>
<pre><code>
Attaching package: 'cowplot'</code></pre>
<pre><code>The following object is masked from 'package:ggplot2':
ggsave</code></pre>
<pre class="r"><code>library(tidyr)
library(reshape2)</code></pre>
<pre><code>Warning: package 'reshape2' was built under R version 3.4.3</code></pre>
<pre><code>
Attaching package: 'reshape2'</code></pre>
<pre><code>The following object is masked from 'package:tidyr':
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<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=nuc.bin
#SBATCH --time=8:00:00
#SBATCH --output=nuc.bin.out
#SBATCH --error=nuc.bin.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed > /project2/gilad/briana/threeprimeseq/data/bin200.nuccov.bed </code></pre>
<p>I can now pull this file into R.</p>
<pre class="r"><code>bin_nuccov=read.table("../data/bin200.nuccov.bed")
names(bin_nuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen")
perc_A_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numA") %>% mutate(percA=numA/200)</code></pre>
<pre><code>Warning: package 'bindrcpp' was built under R version 3.4.4</code></pre>
<pre class="r"><code>ggplot(perc_A_bin, aes(percA)) + geom_histogram(bins=30) + labs(title="Percent of As in the bins")</code></pre>
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<p></details></p>
<p>I will apply the same filter as I did in the cov.200bp.wind file. I will keep bins with greater than 0 reads in half of the libraries.</p>
<pre class="r"><code>cov_all=read.table("../data/ssFC200.cov.bed", header = T, stringsAsFactors = FALSE)
#remember name switch!
names=c("Geneid","Chr", "Start", "End", "Strand", "Length", "N_18486","T_18486","N_18497","T_18497","N_18500","T_18500","N_18505",'T_18505',"N_18508","T_18508","N_18853","T_18853","N_18870","T_18870","N_19128","T_19128","N_19141","T_19141","N_19193","T_19193","N_19209","T_19209","N_19223","N_19225","T_19225","T_19223","N_19238","T_19238","N_19239","T_19239","N_19257","T_19257")
colnames(cov_all)= names
cov_nums_only=cov_all[,7:38]
keep.exprs=rowSums(cov_nums_only>0) >= 16
cov_all_filt=cov_all[keep.exprs,]
cov_all_filt_bins= cov_all_filt %>% separate(col=Geneid, into=c("bin","gene"), sep=".E") %>% select(bin)
cov_all_filt_bins$bin=as.integer(cov_all_filt_bins$bin)</code></pre>
<p>I will intersect the percA file with the bins in the filtered file.</p>
<pre class="r"><code>perc_A_bin_filt= perc_A_bin %>% semi_join(cov_all_filt_bins, by="bin")</code></pre>
<p>I can no plot the distribution of percA in this.</p>
<pre class="r"><code>ggplot(perc_A_bin_filt, aes(percA)) + geom_histogram(bins = 30) + labs(title="Percent of As in the bins after filtering")</code></pre>
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<p></details></p>
<p>I will compare this distribution to those for other nucleotides. (C)</p>
<pre class="r"><code>perc_C_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numC") %>% mutate(percC=numC/200)
ggplot(perc_C_bin, aes(percC)) + geom_histogram(bins=30) + labs(title="Percent of Cs in the bins")</code></pre>
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<p></details></p>
<pre class="r"><code>perc_C_bin_filt= perc_C_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_C_bin_filt, aes(percC)) + geom_histogram(bins = 30) + labs(title="Percent of Cs in the bins after filtering")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-8-2.png" width="672" style="display: block; margin: auto;" /></p>
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</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
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<p></details></p>
<p>’ For G</p>
<pre class="r"><code>perc_G_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numG") %>% mutate(percG=numG/200)
ggplot(perc_G_bin, aes(percG)) + geom_histogram(bins=30) + labs(title="Percent of Gs in the bins")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /></p>
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</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>perc_G_bin_filt= perc_G_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_G_bin_filt, aes(percG)) + geom_histogram(bins = 30) + labs(title="Percent of Gs in the bins after filtering")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-9-2.png" width="672" style="display: block; margin: auto;" /></p>
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</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
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</tbody>
</table>
<p></details></p>
<p>for T</p>
<pre class="r"><code>perc_T_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numT") %>% mutate(percT=numT/200)
ggplot(perc_T_bin, aes(percT)) + geom_histogram(bins=30) + labs(title="Percent of Ts in the bins")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
</td>
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</table>
<p></details></p>
<pre class="r"><code>perc_T_bin_filt= perc_T_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_T_bin_filt, aes(percT)) + geom_histogram(bins = 30) + labs(title="Percent of Ts in the bins after filtering")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-10-2.png" width="672" style="display: block; margin: auto;" /></p>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
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</table>
<p></details></p>
<p>Now I will join all of the percent usage of each nucleotide in the filtered bins so I can plot them on one plot.</p>
<pre class="r"><code>percNuc= perc_A_bin_filt %>% left_join(perc_T_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% left_join(perc_G_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% left_join(perc_C_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% select("bin", "percA", "percT", "percG", "percC")
percNuc_melt=melt(percNuc, id.vars = "bin")
ggplot(percNuc_melt, aes(value)) + geom_histogram(bins = 30) + facet_wrap(~variable) + labs(title="Percent each nucleotide in filtered bins")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/ee80ba6da8265e20b2e27505f86c954b5e2c9bac/docs/figure/a.content.Rmd/unnamed-chunk-11-1.png" target="_blank">ee80ba6</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
</td>
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</tbody>
</table>
<p></details></p>
</div>
<div id="strech-of-nucleotides" class="section level2">
<h2>Strech of Nucleotides</h2>
<p>Next check is if the bins have 5 As in a row. I can do this using bedtools nuc as well.<br />
###Five A’s</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=nucA
#SBATCH --time=8:00:00
#SBATCH --output=nucA.out
#SBATCH --error=nucA.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed -pattern "AAAAA" > /project2/gilad/briana/threeprimeseq/data/bin200.5.A.nuccov.bed </code></pre>
<pre class="r"><code>bin_Anuccov=read.table("../data/bin200.Anuccov.bed")
names(bin_Anuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen", "fiveA")
hist(bin_Anuccov$fiveA)</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-13-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/ee80ba6da8265e20b2e27505f86c954b5e2c9bac/docs/figure/a.content.Rmd/unnamed-chunk-13-1.png" target="_blank">ee80ba6</a>
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<td style="text-align:left;">
Briana Mittleman
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2018-06-11
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<p></details></p>
<p>I will filter this the same way I filtered the other file.</p>
<pre class="r"><code> bin_Anuccov_filt = bin_Anuccov %>% semi_join(cov_all_filt_bins, by="bin") %>% select( bin, gene, fiveA)
hist(bin_Anuccov_filt$fiveA)</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-14-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/ee80ba6da8265e20b2e27505f86c954b5e2c9bac/docs/figure/a.content.Rmd/unnamed-chunk-14-1.png" target="_blank">ee80ba6</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
</td>
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</table>
<p></details></p>
<pre class="r"><code>summary(bin_Anuccov_filt$fiveA)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.4404 0.0000 13.0000 </code></pre>
<p>Count the number of bins with each value for number of 5 AAAAA</p>
<pre class="r"><code>countA_regions= bin_Anuccov_filt %>% group_by(fiveA) %>% count(fiveA)</code></pre>
<div id="five-ts" class="section level3">
<h3>Five T’s</h3>
<p>I will compare this to regions with 5 T’s<br />
(This isnt the best comparison because the probability of each of these stretches genome wide may be different)</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=nucT
#SBATCH --time=8:00:00
#SBATCH --output=nucT.out
#SBATCH --error=nucT.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed -pattern "TTTTT" > /project2/gilad/briana/threeprimeseq/data/bin200.5.T.nuccov.bed </code></pre>
<pre class="r"><code>bin_Tnuccov=read.table("../data/bin200.5.T.nuccov.bed")
names(bin_Tnuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen", "fiveT")
summary(bin_Tnuccov$fiveT)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.4191 0.0000 14.0000 </code></pre>
<pre class="r"><code>bin_Tnuccov_filt = bin_Tnuccov %>% semi_join(cov_all_filt_bins, by="bin") %>% select( bin, gene, fiveT)
hist(bin_Tnuccov_filt$fiveT)</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-17-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/ee80ba6da8265e20b2e27505f86c954b5e2c9bac/docs/figure/a.content.Rmd/unnamed-chunk-17-1.png" target="_blank">ee80ba6</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-11
</td>
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</table>
<p></details></p>
<pre class="r"><code>summary(bin_Tnuccov_filt$fiveT)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.5163 1.0000 9.0000 </code></pre>
<pre class="r"><code>countT_regions= bin_Tnuccov_filt %>% group_by(fiveT) %>% count(fiveT)</code></pre>
<p>The numbers are not super different.</p>
</div>
</div>
<div id="correlation-between-count-and-a-statistics" class="section level2">
<h2>Correlation between count and A statistics</h2>
<div id="percent-nucleotide" class="section level4">
<h4>Percent nucleotide</h4>
<p>cov_all_filt</p>
<pre class="r"><code>#select bin coverage fore 18486
cov_all_filt_18486=cov_all_filt %>%separate(col=Geneid, into=c("bin","gene"), sep=".E") %>% select(bin, T_18486)
cov_all_filt_18486$bin=as.integer(cov_all_filt_18486$bin)
#join with percA
perc_A_bin_filt_cov= perc_A_bin_filt %>% select(bin, percA) %>% right_join(cov_all_filt_18486,by="bin")
#melt it
perc_A_bin_filt_cov_melt=melt(perc_A_bin_filt_cov, id.vars="bin")
#plot it
perA_18486total=ggplot(perc_A_bin_filt_cov, aes(y=T_18486, x=percA)) + geom_point()+ labs(y="Total Bin count",x="Percent A in bin") + geom_smooth(method="lm", col="red")
#select bin coverage fore 18486
cov_all_filt_18486N=cov_all_filt %>%separate(col=Geneid, into=c("bin","gene"), sep=".E") %>% select(bin, N_18486)
cov_all_filt_18486N$bin=as.integer(cov_all_filt_18486N$bin)
#join with percA
perc_A_bin_filt_covN= perc_A_bin_filt %>% select(bin, percA) %>% right_join(cov_all_filt_18486N,by="bin")
#melt it
perc_A_bin_filt_cov_melNt=melt(perc_A_bin_filt_covN, id.vars="bin")
perA_18486nuc=ggplot(perc_A_bin_filt_covN, aes(y=N_18486, x=percA)) + geom_point() + labs(y="Nuclear Bin count",x="Percent A in bin") + geom_smooth(method="lm", col="red")
title <- ggdraw() + draw_label("No relationship between bin read count and percentage \n of A nucleotides in a bin ", fontface = 'bold')
x=plot_grid(perA_18486total,perA_18486nuc)
grid.plot=plot_grid(title, x,ncol=1, rel_heights=c(0.3, 1))
ggsave( "../output/plots/perc.A.bincount.png", grid.plot, width = 10, height = 7)</code></pre>
<pre class="r"><code>lm(perc_A_bin_filt_covN$percA~perc_A_bin_filt_covN$N_18486 )</code></pre>
<pre><code>
Call:
lm(formula = perc_A_bin_filt_covN$percA ~ perc_A_bin_filt_covN$N_18486)
Coefficients:
(Intercept) perc_A_bin_filt_covN$N_18486
2.655e-01 2.643e-05 </code></pre>
<pre class="r"><code>lm( perc_A_bin_filt_cov$percA ~ perc_A_bin_filt_cov$T_18486)</code></pre>
<pre><code>
Call:
lm(formula = perc_A_bin_filt_cov$percA ~ perc_A_bin_filt_cov$T_18486)
Coefficients:
(Intercept) perc_A_bin_filt_cov$T_18486
0.2658285 0.0000085 </code></pre>
<p>Both have about a 0 correlation.</p>
<p>I will remove bins we did not have coverage in.</p>
<pre class="r"><code>perc_A_bin_filt_cov_no0= perc_A_bin_filt %>% select(bin, percA) %>% right_join(cov_all_filt_18486,by="bin") %>% filter(T_18486 >0 )
ggplot(perc_A_bin_filt_cov_no0, aes(y=T_18486, x=percA)) + geom_point() + geom_smooth(method="lm", col="red")</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-20-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/256ba93fa029b734d871e5d78c0b6ab9bc351abc/docs/figure/a.content.Rmd/unnamed-chunk-20-1.png" target="_blank">256ba93</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-12
</td>
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</table>
<p></details></p>
<p>Check for T</p>
<pre class="r"><code>#join with percA
perc_T_bin_filt_cov= perc_T_bin_filt %>% select(bin, percT) %>% right_join(cov_all_filt_18486,by="bin")
#melt it
perc_T_bin_filt_cov_melt=melt(perc_T_bin_filt_cov, id.vars="bin")
#plot it
ggplot(perc_T_bin_filt_cov, aes(y=T_18486, x=percT)) + geom_point()</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-21-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/256ba93fa029b734d871e5d78c0b6ab9bc351abc/docs/figure/a.content.Rmd/unnamed-chunk-21-1.png" target="_blank">256ba93</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-12
</td>
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</tbody>
</table>
<p></details></p>
</div>
<div id="number-of-times-we-see-5-of-a-nucleotide" class="section level4">
<h4>Number of times we see 5 of a nucleotide</h4>
<pre class="r"><code>#join with fiveA
bin_Anuccov_filt_cov= bin_Anuccov_filt %>% select(bin, fiveA) %>% right_join(cov_all_filt_18486,by="bin")
#plot it
ggplot(bin_Anuccov_filt_cov, aes(y=T_18486, x=fiveA)) + geom_point()</code></pre>
<p><img src="figure/a.content.Rmd/unnamed-chunk-22-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>It does not look like these are drivers of the variation we see in counts.</p>
<p>This analysis has shown me that mispriming is not a global problem in the samples. We do not see a correlaation bertween percent As in a bin and the read count for either the total or the nuclear. I also do not see any outliers bins with high coverage and high A percentage. If this is a problem it is at a specfici locus level. We can assess this in regions we see differences between total and nuclear fractions.</p>
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
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/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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 reshape2_1.4.3 tidyr_0.7.2 cowplot_0.9.2
[5] dplyr_0.7.5 ggplot2_2.2.1 workflowr_1.0.1 rmarkdown_1.8.5
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 compiler_3.4.2 pillar_1.1.0
[4] git2r_0.21.0 plyr_1.8.4 bindr_0.1.1
[7] R.methodsS3_1.7.1 R.utils_2.6.0 tools_3.4.2
[10] digest_0.6.15 evaluate_0.10.1 tibble_1.4.2
[13] gtable_0.2.0 pkgconfig_2.0.1 rlang_0.2.1
[16] yaml_2.1.19 stringr_1.3.1 knitr_1.18
[19] rprojroot_1.3-2 grid_3.4.2 tidyselect_0.2.4
[22] glue_1.2.0 R6_2.2.2 purrr_0.2.5
[25] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[28] scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0
[31] colorspace_1.3-2 labeling_0.3 stringi_1.2.2
[34] lazyeval_0.2.1 munsell_0.4.3 R.oo_1.22.0 </code></pre>
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